From 4ae9ab8768ab35b3b0a6cfb806972d14ca48da98 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Tue, 30 Jun 2026 06:06:58 +0000 Subject: [PATCH 01/54] skills: add webwright.skills memory/skill-library module + skill_use tool MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit A built-in submodule turning solved tasks into reusable, executable code skills: - skills/{library,retrieve,decide,gate,update,llm}: store / retrieve (relevance) / decide (use·adapt·skip utility) / admission gate (gold|self_verify|none) / evolve (incremental growth on existing library) — backend-agnostic via configure_llm over webwright's own Model abstraction (no hardcoded gateway/key/path) - tools/skill_use.py: solve-time tool (agent invokes like self_reflection/image_qa) -> retrieve+decide -> JSON recommendation (use/adapt/skip + source path) - python -m webwright.skills.update --manifest batch.json --library ./lib : batch growth - tests/skills: 5 unit tests pass against the migrated module (logic == original) Co-Authored-By: Claude Opus 4.8 (1M context) --- src/webwright/skills/__init__.py | 21 +++ src/webwright/skills/decide.py | 50 +++++++ src/webwright/skills/gate.py | 62 ++++++++ src/webwright/skills/library.py | 59 ++++++++ src/webwright/skills/llm.py | 57 ++++++++ src/webwright/skills/retrieve.py | 75 ++++++++++ src/webwright/skills/update.py | 208 +++++++++++++++++++++++++++ src/webwright/tools/skill_use.py | 72 ++++++++++ tests/skills/test_evolve.py | 49 +++++++ tests/skills/test_gate.py | 35 +++++ tests/skills/test_library.py | 33 +++++ tests/skills/test_retrieve_decide.py | 40 ++++++ tests/skills/test_update.py | 36 +++++ 13 files changed, 797 insertions(+) create mode 100644 src/webwright/skills/__init__.py create mode 100644 src/webwright/skills/decide.py create mode 100644 src/webwright/skills/gate.py create mode 100644 src/webwright/skills/library.py create mode 100644 src/webwright/skills/llm.py create mode 100644 src/webwright/skills/retrieve.py create mode 100644 src/webwright/skills/update.py create mode 100644 src/webwright/tools/skill_use.py create mode 100644 tests/skills/test_evolve.py create mode 100644 tests/skills/test_gate.py create mode 100644 tests/skills/test_library.py create mode 100644 tests/skills/test_retrieve_decide.py create mode 100644 tests/skills/test_update.py diff --git a/src/webwright/skills/__init__.py b/src/webwright/skills/__init__.py new file mode 100644 index 0000000..6f3383f --- /dev/null +++ b/src/webwright/skills/__init__.py @@ -0,0 +1,21 @@ +"""webwright.skills — a memory/skill library module for webwright. + +Store solved tasks as reusable, executable code skills; retrieve + judge (use/adapt/skip) at +solve time; admit via a gate; and grow the library incrementally (evolve). Plugs into webwright +as a built-in submodule: + - solve-time reuse : the `skill_use` tool (agent invokes it like self_reflection / image_qa) + - offline growth : `update.evolve` (run after solves to distill gate-passed solves into skills) + +Backend-agnostic: configure_llm(model) wires it to any webwright Model. +""" +from .library import Library, Skill +from .retrieve import retrieve, Candidate +from .decide import decide, Decision +from .gate import gate, GateResult +from .update import evolve, Trace # NOTE: don't import the `update` function here — it would +from .llm import configure_llm # shadow the `update` submodule. Use update.evolve / update.update. + +__all__ = [ + "Library", "Skill", "retrieve", "Candidate", "decide", "Decision", + "gate", "GateResult", "evolve", "Trace", "configure_llm", +] diff --git a/src/webwright/skills/decide.py b/src/webwright/skills/decide.py new file mode 100644 index 0000000..eaddc8d --- /dev/null +++ b/src/webwright/skills/decide.py @@ -0,0 +1,50 @@ +"""判断用不用：候选 + 任务 → use / adapt / skip（utility）。 + +接口稳定（实现可换）： + decide(task, candidates, *, method="llm") -> Decision +相关 ≠ 有用：retrieve 给"像不像"，decide 给"该不该用、怎么用"。 +""" +from __future__ import annotations +from dataclasses import dataclass + +from .llm import llm_json + + +@dataclass +class Decision: + verdict: str # "use" | "adapt" | "skip" + skill_id: str | None + reason: str + + +def _decide_llm(task: str, candidates) -> Decision: + if not candidates: + return Decision("skip", None, "no candidate skills") + cat = "\n".join( + f"- skill_id: {c.skill.skill_id} | template: {c.skill.meta.get('template','')} | " + f"summary: {c.skill.summary} | params: {c.skill.signature.get('params', [])}" + for c in candidates + ) + sys = ( + "Decide whether a library skill is worth using for THIS task. Output STRICT JSON: " + '{"verdict":"use|adapt|skip","skill_id":"...","reason":"..."}.\n' + "- use = the skill fits the task as-is (just different parameter values).\n" + "- adapt = the skill's expensive core (login / navigation / extraction) is reusable, but the " + "FINAL step differs; the agent should reuse the front and add/adapt only the last step.\n" + "- skip = no candidate is worth it; solve from scratch (skill_id = null).\n" + "Relevance is not enough — only 'use'/'adapt' if it genuinely saves work." + ) + user = f"## Task\n{task}\n\n## Candidate skills (most relevant first)\n{cat}" + out = llm_json(sys, user) + verdict = out.get("verdict", "skip") + if verdict not in ("use", "adapt", "skip"): + verdict = "skip" + skill_id = out.get("skill_id") if verdict != "skip" else None + return Decision(verdict=verdict, skill_id=skill_id, reason=out.get("reason", "")) + + +_DECIDERS = {"llm": _decide_llm} + + +def decide(task: str, candidates, *, method: str = "llm") -> Decision: + return _DECIDERS[method](task, candidates) diff --git a/src/webwright/skills/gate.py b/src/webwright/skills/gate.py new file mode 100644 index 0000000..4ebc43e --- /dev/null +++ b/src/webwright/skills/gate.py @@ -0,0 +1,62 @@ +"""准入闸：只有"对的"解/技能才准进库，防 correct-but-narrow / regression 污染。 +gate 是【独立第二只眼】，与解题 agent 自己的 self_reflection 不同（后者是解题完成条件）。 + +接口稳定（实现可换），method 可配置： + gate(result, *, gold=None, output_schema=None, method="auto") -> GateResult + +- method="gold" : 与 gold 比对（WebArena 等有标准答案；真独立、能挡住抽错的解）。★推荐 +- method="self_verify" : 不变量（result 非空 + shape 合 output_schema）。无 gold 时的弱占位。 + ⚠️ 局限：只查"有没有/形状对不对"，不查"对不对"——抽错但非空的答案照样放行。 + （注：webwright 的 self_reflection 因 require_self_reflection_success 而恒为 + predicted_label==1，故不能用它当 gate；那是解题完成条件，非独立准入。） +- method="none" : 不把关（纯演示复用，不防污染）。 +- method="auto" : 有 gold 用 gold，否则 self_verify。 +升级路径（next step）：真实站用 WebJudge（OM2W 官方 judge）或跨源一致核验，做真独立把关。 +""" +from __future__ import annotations +from dataclasses import dataclass + + +@dataclass +class GateResult: + admit: bool + reason: str + + +def _shape_ok(result, output_schema) -> bool: + if not output_schema: + return True + t = output_schema.get("type") + if t == "array": + return isinstance(result, list) + if t == "object": + return isinstance(result, dict) + if t in ("string",): + return isinstance(result, str) + if t in ("number", "integer"): + return isinstance(result, (int, float)) and not isinstance(result, bool) + return True + + +def _self_verify(result, output_schema) -> GateResult: + if result is None: + return GateResult(False, "result is null") + if isinstance(result, (list, dict, str)) and len(result) == 0: + return GateResult(False, "result is empty") + if not _shape_ok(result, output_schema): + return GateResult(False, f"shape != output_schema ({output_schema.get('type')})") + return GateResult(True, "self-verify passed (non-empty, shape ok)") + + +def _gold(result, gold) -> GateResult: + if result == gold: + return GateResult(True, "matches gold") + return GateResult(False, "differs from gold") + + +def gate(result, *, gold=None, output_schema=None, method: str = "auto") -> GateResult: + if method == "none": + return GateResult(True, "no gate (admit all)") + if method == "gold" or (method == "auto" and gold is not None): + return _gold(result, gold) + return _self_verify(result, output_schema) diff --git a/src/webwright/skills/library.py b/src/webwright/skills/library.py new file mode 100644 index 0000000..9bb6345 --- /dev/null +++ b/src/webwright/skills/library.py @@ -0,0 +1,59 @@ +"""Skill store. A skill = a directory under the library root holding skill.py + meta.json. + +Interface (stable — implementations behind it may change): + Library(root).list() -> [Skill] + Library(root).get(skill_id) -> Skill | None + Library(root).add(skill) # write skill.py + meta.json +""" +from __future__ import annotations +import json +from dataclasses import dataclass, field +from pathlib import Path + + +@dataclass +class Skill: + skill_id: str + code: str # source of skill.py + meta: dict = field(default_factory=dict) # {template, site, signature, summary, ...} + + @property + def summary(self) -> str: + return self.meta.get("summary", "") + + @property + def signature(self) -> dict: + return self.meta.get("signature", {}) + + +class Library: + def __init__(self, root: str | Path): + self.root = Path(root) + self.root.mkdir(parents=True, exist_ok=True) + + def _dir(self, skill_id: str) -> Path: + return self.root / skill_id + + def list(self) -> list[Skill]: + out = [] + for d in sorted(self.root.iterdir()): + if (d / "meta.json").exists(): + out.append(self.get(d.name)) + return [s for s in out if s] + + def get(self, skill_id: str) -> Skill | None: + d = self._dir(skill_id) + if not (d / "meta.json").exists(): + return None + meta = json.loads((d / "meta.json").read_text()) + code = (d / "skill.py").read_text() if (d / "skill.py").exists() else "" + return Skill(skill_id=skill_id, code=code, meta=meta) + + def add(self, skill: Skill) -> None: + d = self._dir(skill.skill_id) + d.mkdir(parents=True, exist_ok=True) + (d / "skill.py").write_text(skill.code) + (d / "meta.json").write_text(json.dumps(skill.meta, ensure_ascii=False, indent=2)) + + def path(self, skill_id: str) -> Path: + return self._dir(skill_id) / "skill.py" diff --git a/src/webwright/skills/llm.py b/src/webwright/skills/llm.py new file mode 100644 index 0000000..2e0703e --- /dev/null +++ b/src/webwright/skills/llm.py @@ -0,0 +1,57 @@ +"""LLM helper for the skills module — backend-agnostic, via webwright's own model abstraction. + +No hardcoded gateway/endpoint/key: the caller passes a webwright Model (or a model config dict), +so this works with any backend webwright supports (openai / anthropic / openrouter / custom). +""" +from __future__ import annotations + +import json +import re +from typing import Any, Optional + +from webwright.models import get_model + +# Process-wide default model, set once via configure_llm() so retrieve/decide/update can call +# llm() without each caller threading a Model through. Falls back to env-configured openai. +_DEFAULT_MODEL: Optional[Any] = None + + +def configure_llm(model: Any) -> None: + """Register the Model (or model-config dict) the skills module should use.""" + global _DEFAULT_MODEL + _DEFAULT_MODEL = get_model(model) if isinstance(model, dict) else model + + +def _model() -> Any: + if _DEFAULT_MODEL is not None: + return _DEFAULT_MODEL + # default: openai model from env (OPENAI_API_KEY / OPENAI_BASE_URL respected by the model class) + return get_model({"model_class": "openai"}) + + +def llm(system: str, user: str, *, model: Any = None, **_: Any) -> str: + """Single-turn call. Returns raw text. `model` overrides the configured default.""" + m = model if model is not None else _model() + messages = [ + m.format_message(role="system", content=system), + m.format_message(role="user", content=user), + ] + return m(messages) + + +def llm_json(system: str, user: str, **kw: Any) -> dict: + """Call + parse the first {...} JSON object out of the reply.""" + txt = llm(system, user, **kw) + match = re.search(r"\{.*\}", txt, re.S) + if not match: + return {} + try: + return json.loads(match.group(0)) + except Exception: + s = match.group(0) + for end in range(len(s), 0, -1): + try: + return json.loads(s[:end]) + except Exception: + continue + return {} diff --git a/src/webwright/skills/retrieve.py b/src/webwright/skills/retrieve.py new file mode 100644 index 0000000..fae5d1e --- /dev/null +++ b/src/webwright/skills/retrieve.py @@ -0,0 +1,75 @@ +"""取：任务 → 最相关的候选技能（relevance）。 + +接口稳定（实现可换）： + retrieve(task, library, *, k=3, method="llm") -> [Candidate] +MVP: 单次 LLM 调用，把整库当 flat catalog 列进 prompt 让它选。库大了换 embedding，接口不变。 +""" +from __future__ import annotations +from dataclasses import dataclass + +from .library import Library, Skill +from .llm import llm_json + + +@dataclass +class Candidate: + skill: Skill + score: float # relevance 0..1 + reason: str + + +def _catalog(library: Library) -> str: + lines = [] + for s in library.list(): + lines.append( + f"- skill_id: {s.skill_id}\n" + f" template: {s.meta.get('template','')}\n" + f" site: {s.meta.get('site','')}\n" + f" summary: {s.summary}\n" + f" params: {s.signature.get('params', [])}" + ) + return "\n".join(lines) + + +def _retrieve_llm(task: str, library: Library, k: int) -> list[Candidate]: + cat = _catalog(library) + if not cat: + return [] + sys = ( + "You match a web task to the most RELEVANT skills in a catalog (relevance only — not yet " + "whether to use them). Return STRICT JSON: " + '{"candidates":[{"skill_id":"...","score":<0..1>,"reason":"..."}]}, most relevant first, ' + f"at most {k}. score = how relevant. If nothing is relevant, return an empty list." + ) + user = f"## Task\n{task}\n\n## Skill catalog\n{cat}\n\nReturn at most {k} candidates." + out = llm_json(sys, user) + cands = [] + for c in (out.get("candidates") or [])[:k]: + sk = library.get(c.get("skill_id", "")) + if sk: + try: + score = float(c.get("score", 0)) + except Exception: + score = 0.0 + cands.append(Candidate(skill=sk, score=score, reason=c.get("reason", ""))) + return cands + + +def _retrieve_simple(task: str, library: Library, k: int) -> list[Candidate]: + """No-LLM fallback: rank by keyword overlap between task and template/summary.""" + toks = set(task.lower().split()) + scored = [] + for s in library.list(): + bag = (s.meta.get("template", "") + " " + s.summary).lower().split() + overlap = len(toks & set(bag)) + if overlap: + scored.append(Candidate(skill=s, score=overlap / (len(toks) or 1), reason="keyword overlap")) + scored.sort(key=lambda c: c.score, reverse=True) + return scored[:k] + + +_RETRIEVERS = {"llm": _retrieve_llm, "simple": _retrieve_simple} + + +def retrieve(task: str, library: Library, *, k: int = 3, method: str = "llm") -> list[Candidate]: + return _RETRIEVERS[method](task, library, k) diff --git a/src/webwright/skills/update.py b/src/webwright/skills/update.py new file mode 100644 index 0000000..bcb4a80 --- /dev/null +++ b/src/webwright/skills/update.py @@ -0,0 +1,208 @@ +"""沉淀（间歇）：把通过 gate 的解蒸馏、写回 library，让库从使用中长大。 + +接口稳定（实现可换）： + update(traces, library, *, method="grow") -> [被加/更新的 skill_id] + +- method="grow" : 库里还没覆盖这个 template 的，就把这条成功解原样提升为技能（最小形态）。 +- method="refine" : 批量提炼——对齐 N 个 gate 过的解 → 参数化(泛化) + 拆出可复用 primitive + 薄任务层 + → 一个更好的库技能。这是"update 加泛化性 + primitive 复用性"的实现（单次 LLM 批量调用）。 +""" +from __future__ import annotations +import json +import re +from dataclasses import dataclass, field + +from .library import Library, Skill +from .llm import llm + + +@dataclass +class Trace: + template: str + code: str # 这条任务的 final_script（已过 gate = 正确） + answer: object = None + meta: dict = field(default_factory=dict) # params / site / start_url / output_schema ... + # usage：这条任务是怎么用库的（驱动 update 的信号） + used_skill_id: str | None = None + verdict: str | None = None # use | adapt | skip + correct: bool = True + + +def _slug(template: str) -> str: + s = re.sub(r"[^a-z0-9]+", "_", template.lower()).strip("_") + return s[:48] or "skill" + + +def _extract_code(txt: str) -> str: + m = re.search(r"```(?:python)?[ \t]*\n", txt) + if m: + end = txt.rfind("```") + if end > m.end(): + return txt[m.end():end] + return txt + + +_REFINE_SYS = ( + "You are given N working Python solutions that EACH solve one concrete instance of the SAME web-task " + "template (they already passed a correctness gate). Distill them into ONE better library skill.\n" + "Do TWO things:\n" + "1) GENERALIZE: align the N solutions; the parts that are IDENTICAL across them are the reusable " + "skeleton; the parts that DIFFER are parameters. Expose the differing values as function " + "arguments / taskspec params — do NOT hardcode any instance's specific values. Make extraction " + "robust (paginate/until-done, self-verify against any declared total).\n" + "2) DECOMPOSE INTO REUSABLE PRIMITIVES: factor the expensive, reusable core into clearly-named " + "primitive functions (e.g. login(), open_report(period), extract_rows()), and keep a THIN task " + "layer on top that calls them. This lets future tasks reuse the primitives even if the final step " + "differs.\n" + "Interface (fixed): the skill reads taskspec.json from sys.argv[1] " + "(taskspec = {params, start_url, credentials, output_schema}) and writes agent_response.json with " + "retrieved_data MATCHING output_schema exactly. Output ONLY the python code in one ```python block." +) + +_REFINE_INCREMENTAL = ( + "\n\nINCREMENTAL MODE: a CURRENT library skill for this template already exists (shown below). " + "Do NOT rewrite it from scratch. START from the current skill and IMPROVE it using the NEW solutions: " + "keep its working primitives and structure, only widen/fix what the new solutions reveal (handle a " + "param value it missed, make an extraction more robust, fix a bug). Preserve everything that already " + "works. Output the full improved skill in one ```python block." +) + + +def _refine(traces: list[Trace], library: Library) -> list[str]: + """批量提炼：对齐 N 个 gate 过的解 → 参数化 + primitive。 + 增量：若库里已有同 template 技能，则在【现有技能基础上】改进/加宽（而非从原始解重写）。""" + if not traces: + return [] + template = traces[0].template + schema = traces[0].meta.get("output_schema") + sid = _slug(template) + existing = library.get(sid) # 已有技能？→ 增量演化 + + blocks = [f"## Template\n{template}\n\n## Required output_schema for retrieved_data\n{json.dumps(schema)}\n"] + if existing and existing.code: + blocks.append(f"## CURRENT library skill (improve THIS, do not rewrite)\n```python\n{existing.code}\n```") + label = "NEW solutions" if existing else "Solutions" + for i, tr in enumerate(traces): + blocks.append( + f"## {label} {i} (params={json.dumps(tr.meta.get('params'), ensure_ascii=False)}, " + f"answer={json.dumps(tr.answer, ensure_ascii=False)[:120]})\n```python\n{tr.code}\n```" + ) + sys_prompt = _REFINE_SYS + (_REFINE_INCREMENTAL if existing else "") + code = _extract_code(llm(sys_prompt, "\n\n".join(blocks), max_tokens=16000, timeout=400)) + n_prev = (existing.meta.get("n_solves", 0) if existing else 0) + meta = { + "template": template, + "provenance": "update-refined-incremental" if existing else "update-refined", + "site": traces[0].meta.get("site", ""), + "summary": f"Refined from {n_prev + len(traces)} gate-passed solves; parameterized + primitives.", + "signature": {"params": list((traces[0].meta.get("params") or {}).keys()), + "call": "python skill.py taskspec.json"}, + "output_schema": schema, + "n_solves": n_prev + len(traces), + "revisions": (existing.meta.get("revisions", 1) + 1) if existing else 1, + } + library.add(Skill(skill_id=sid, code=code, meta=meta)) + return [sid] + + +def _grow(traces: list[Trace], library: Library) -> list[str]: + existing = {s.meta.get("template") for s in library.list()} + added = [] + for tr in traces: + if not tr.template or tr.template in existing: + continue + sid = _slug(tr.template) + meta = {"template": tr.template, "provenance": "distilled", **tr.meta} + library.add(Skill(skill_id=sid, code=tr.code, meta=meta)) + existing.add(tr.template) + added.append(sid) + return added + + +def evolve(traces: list[Trace], library: Library) -> dict: + """统一 update：在【已有库】上，按每条轨迹的 usage(use/adapt/skip)决定怎么改库。 + 这是"可持续增长的库"的核心——不是每次从零建，而是在 v_{n-1} 上长出 v_n。 + + - USE 成功的轨迹：技能够好，不动库（只是复用证据）。 + - ADAPT 成功的轨迹：复用了核心、fix 了末端 → 把这批 fix 后的解【提炼回该 template 的技能】 + （加宽/更稳）。这就是"fix 沉淀进库"。 + - SKIP / 库没覆盖：该 template 还没有技能 → 用这批解【新增】一个技能。 + + 只吃 gate 过(correct=True)的轨迹（防污染）。返回一份 changelog。 + """ + good = [t for t in traces if t.correct] + changelog = {"use": [], "adapt_refined": [], "added": [], "dropped_wrong": len(traces) - len(good)} + existing_templates = {s.meta.get("template"): s.skill_id for s in library.list()} + + # 按 template 分组（同族解一起提炼/沉淀） + by_tmpl: dict[str, list[Trace]] = {} + for t in good: + by_tmpl.setdefault(t.template, []).append(t) + + for tmpl, group in by_tmpl.items(): + verdicts = {t.verdict for t in group} + if tmpl not in existing_templates: + # 库没覆盖 → 新增（用这批解提炼出一个技能） + sid = _refine(group, library)[0] + changelog["added"].append(sid) + elif "adapt" in verdicts: + # 有 fix 发生 → 把 fix 后的解重新提炼回该技能（加宽/更稳） + sid = _refine(group, library)[0] # _refine 用同 slug，覆盖加宽 + changelog["adapt_refined"].append(sid) + else: + # 全 use 成功 → 技能够好，不动 + changelog["use"].append(existing_templates[tmpl]) + return changelog + + +_UPDATERS = {"grow": _grow, "refine": _refine} + + +def update(traces, library: Library, *, method: str = "grow") -> list[str]: + return _UPDATERS[method](traces, library) + + +# ---------- CLI: batch update via a manifest ---------- +def traces_from_manifest(manifest: dict) -> list["Trace"]: + """manifest = {"template": str, "runs": [{"dir","admit","params","answer"?,"verdict"?}, ...]}. + Reads each run's final_script.py; builds a Trace. correct = the run's gate verdict (admit).""" + from pathlib import Path + template = manifest.get("template", "") + out = [] + for r in manifest.get("runs", []): + d = Path(r["dir"]) + fs = d / "final_script.py" + code = fs.read_text(encoding="utf-8") if fs.exists() else "" + answer = r.get("answer") + if answer is None and (d / "agent_response.json").exists(): + try: + answer = json.load(open(d / "agent_response.json")).get("retrieved_data") + except Exception: + pass + out.append(Trace(template=template, code=code, answer=answer, + correct=bool(r.get("admit", True)), + verdict=r.get("verdict", "skip"), + used_skill_id=r.get("used_skill_id"), + meta={"params": r.get("params", {}), "site": r.get("site", ""), + "start_url": r.get("start_url", ""), + "output_schema": r.get("output_schema")})) + return out + + +def main(argv=None) -> int: + import argparse + p = argparse.ArgumentParser( + prog="python -m webwright.skills.update", + description="Batch-update the skill library from a manifest of gate-judged solves.") + p.add_argument("--manifest", required=True, help="JSON: {template, runs:[{dir,admit,params,...}]}") + p.add_argument("--library", required=True, help="Path to the skill library directory.") + a = p.parse_args(argv) + manifest = json.load(open(a.manifest, encoding="utf-8")) + traces = traces_from_manifest(manifest) + changelog = evolve(traces, Library(a.library)) + print(json.dumps(changelog, ensure_ascii=False, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/webwright/tools/skill_use.py b/src/webwright/tools/skill_use.py new file mode 100644 index 0000000..9acc210 --- /dev/null +++ b/src/webwright/tools/skill_use.py @@ -0,0 +1,72 @@ +"""skill_use — solve-time tool: query the skill library for a reusable skill for THIS task. + +Like self_reflection / image_qa, the agent invokes this from bash during solving: + + python -m webwright.tools.skill_use --task "Get the latest release version of facebook/react" \ + --library "$WORKSPACE_DIR/../library" + +It retrieves the most relevant skill (relevance) and judges utility (use / adapt / skip), then +prints a JSON recommendation telling the agent how to reuse it (and the path to read its source). +The agent decides: reuse as-is (use), reuse the core and change only the last step (adapt), or +solve from scratch (skip). Retrieval/judgement never block solving — on any error it prints skip. +""" +from __future__ import annotations + +import argparse +import json +import os +import sys + +from webwright.skills.library import Library +from webwright.skills.retrieve import retrieve +from webwright.skills.decide import decide + + +def recommend(task: str, library_root: str) -> dict: + lib = Library(library_root) + cands = retrieve(task, lib) + if not cands: + return {"verdict": "skip", "skill_id": None, "reason": "library has no relevant skill"} + d = decide(task, cands) + out = {"verdict": d.verdict, "skill_id": d.skill_id, "reason": d.reason} + if d.verdict != "skip" and d.skill_id: + sk = lib.get(d.skill_id) + if sk: + out["summary"] = sk.summary + out["call"] = sk.signature.get("call", "") + out["source_path"] = str(lib.path(sk.skill_id)) + out["how_to_reuse"] = ( + "USE: copy the source into your final_script and fill THIS task's params; " + "ADAPT: reuse its login/navigation/extraction core, change ONLY the final step." + ) + return out + + +def build_parser() -> argparse.ArgumentParser: + p = argparse.ArgumentParser( + prog="python -m webwright.tools.skill_use", + description="Query the skill library for a reusable skill for the current task.", + ) + p.add_argument("--task", required=True, help="The current task description / intent.") + p.add_argument("--library", default=os.environ.get("SKILL_LIBRARY_ROOT", "library"), + help="Path to the skill library dir (default: $SKILL_LIBRARY_ROOT or ./library).") + p.add_argument("--output", default="", help="Write JSON to this path instead of stdout.") + return p + + +def main(argv: list[str] | None = None) -> int: + args = build_parser().parse_args(argv) + try: + result = recommend(args.task, args.library) + except Exception as exc: # never block solving + result = {"verdict": "skip", "skill_id": None, "reason": f"skill_use error: {exc}"} + payload = json.dumps(result, ensure_ascii=False, indent=2) + if args.output: + with open(args.output, "w", encoding="utf-8") as f: + f.write(payload) + print(payload) + return 0 + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/tests/skills/test_evolve.py b/tests/skills/test_evolve.py new file mode 100644 index 0000000..ad4189c --- /dev/null +++ b/tests/skills/test_evolve.py @@ -0,0 +1,49 @@ +"""Unit test: evolve (growing library, usage-driven). Stubs _refine to stay LLM-free.""" +import sys, tempfile +from pathlib import Path +pass +import webwright.skills.update as U +from webwright.skills.library import Library, Skill + + +def run(): + # stub _refine: deterministically "build/widen" a skill for the group's template + def fake_refine(group, library): + from webwright.skills.update import _slug + sid = _slug(group[0].template) + library.add(Skill(sid, f"# refined from {len(group)} solves\n", + {"template": group[0].template, "provenance": "test-refine"})) + return [sid] + U._refine = fake_refine + + with tempfile.TemporaryDirectory() as d: + lib = Library(d) + + # round 1: template T1 not in lib, skip verdict -> ADD + t1 = [U.Trace("T1", "code", verdict="skip", correct=True), + U.Trace("T1", "code", verdict="skip", correct=True)] + log1 = U.evolve(t1, lib) + assert log1["added"], f"new template should be added: {log1}" + assert len(lib.list()) == 1 + + # round 2: T1 now exists, all USE success -> library unchanged + t2 = [U.Trace("T1", "code", used_skill_id="t1", verdict="use", correct=True)] + log2 = U.evolve(t2, lib) + assert log2["use"] and not log2["added"] and not log2["adapt_refined"], log2 + assert len(lib.list()) == 1, "pure use must not change library" + + # round 3: T1 exists, an ADAPT happened -> refine back (widen) + t3 = [U.Trace("T1", "code2", used_skill_id="t1", verdict="adapt", correct=True)] + log3 = U.evolve(t3, lib) + assert log3["adapt_refined"], f"adapt should refine back: {log3}" + + # wrong solves are dropped (not fed to refine) + t4 = [U.Trace("T2", "bad", verdict="skip", correct=False)] + log4 = U.evolve(t4, lib) + assert log4["dropped_wrong"] == 1 and not log4["added"], log4 + + print("test_evolve OK") + + +if __name__ == "__main__": + run() diff --git a/tests/skills/test_gate.py b/tests/skills/test_gate.py new file mode 100644 index 0000000..6240b3b --- /dev/null +++ b/tests/skills/test_gate.py @@ -0,0 +1,35 @@ +"""Unit test: admission gate (deterministic, no external).""" +import sys +from pathlib import Path +pass +from webwright.skills.gate import gate + +ARR = {"type": "array", "items": {"type": "string"}} + + +def run(): + # self_verify: reject null / empty, admit non-empty + assert gate(None, method="self_verify").admit is False + assert gate([], method="self_verify").admit is False + assert gate("", method="self_verify").admit is False + assert gate(["Sprite"], method="self_verify").admit is True + + # self_verify: shape must match output_schema + assert gate(["a", "b"], output_schema=ARR, method="self_verify").admit is True + assert gate({"x": 1}, output_schema=ARR, method="self_verify").admit is False, "dict != array schema" + + # gold: admit iff equal + assert gate(["Sprite"], gold=["Sprite"], method="gold").admit is True + assert gate(["Pepsi"], gold=["Sprite"], method="gold").admit is False + + # auto: gold present -> use gold; absent -> self_verify + assert gate(["Sprite"], gold=["Sprite"]).admit is True # auto+gold -> match + assert gate(["Pepsi"], gold=["Sprite"]).admit is False # auto+gold -> mismatch -> reject + assert gate(["anything"]).admit is True # auto, no gold -> self_verify pass + assert gate(None).admit is False # auto, no gold -> self_verify fail + + print("test_gate OK") + + +if __name__ == "__main__": + run() diff --git a/tests/skills/test_library.py b/tests/skills/test_library.py new file mode 100644 index 0000000..174c6c3 --- /dev/null +++ b/tests/skills/test_library.py @@ -0,0 +1,33 @@ +"""Unit test: library store (deterministic, no LLM).""" +import sys, tempfile +from pathlib import Path +pass +from webwright.skills.library import Library, Skill + + +def run(): + with tempfile.TemporaryDirectory() as d: + lib = Library(d) + assert lib.list() == [], "empty library should list nothing" + assert lib.get("nope") is None, "missing skill -> None" + + sk = Skill(skill_id="s1", code="print('hi')\n", + meta={"template": "do {x}", "summary": "does x", "signature": {"params": ["x"]}}) + lib.add(sk) + + got = lib.get("s1") + assert got is not None and got.code == "print('hi')\n", "get returns added code" + assert got.meta["template"] == "do {x}" + assert got.summary == "does x" + assert got.signature["params"] == ["x"] + assert [s.skill_id for s in lib.list()] == ["s1"], "list shows added skill" + assert lib.path("s1").name == "skill.py" and lib.path("s1").exists() + + # re-open from disk -> persisted + lib2 = Library(d) + assert [s.skill_id for s in lib2.list()] == ["s1"], "persisted across re-open" + print("test_library OK") + + +if __name__ == "__main__": + run() diff --git a/tests/skills/test_retrieve_decide.py b/tests/skills/test_retrieve_decide.py new file mode 100644 index 0000000..4bf032a --- /dev/null +++ b/tests/skills/test_retrieve_decide.py @@ -0,0 +1,40 @@ +"""Unit test: deterministic parts of retrieve/decide (no LLM). +The LLM paths are smoke-tested in test_front.py.""" +import sys, tempfile +from pathlib import Path +pass +from webwright.skills.library import Library, Skill +from webwright.skills.retrieve import retrieve, Candidate +from webwright.skills.decide import decide, Decision + + +def _lib(d): + lib = Library(d) + lib.add(Skill("bestsellers", "x", {"template": "Get the top best-selling product in period", + "summary": "magento bestsellers report"})) + lib.add(Skill("reviews", "x", {"template": "Get reviewers who mention something", + "summary": "product page reviews"})) + return lib + + +def run(): + with tempfile.TemporaryDirectory() as d: + lib = _lib(d) + + # retrieve(method="simple"): keyword overlap, deterministic + cands = retrieve("top best-selling product", lib, method="simple") + assert cands, "simple retrieve should find the bestsellers skill" + assert cands[0].skill.skill_id == "bestsellers", "most-overlapping skill ranked first" + + cands2 = retrieve("zzz nonsense quux", lib, method="simple") + assert cands2 == [], "no overlap -> no candidates" + + # decide with no candidates -> skip (deterministic, no LLM) + d0 = decide("anything", []) + assert isinstance(d0, Decision) and d0.verdict == "skip" and d0.skill_id is None + + print("test_retrieve_decide OK") + + +if __name__ == "__main__": + run() diff --git a/tests/skills/test_update.py b/tests/skills/test_update.py new file mode 100644 index 0000000..2abfffe --- /dev/null +++ b/tests/skills/test_update.py @@ -0,0 +1,36 @@ +"""Unit test: update 'grow' (deterministic, no LLM).""" +import sys, tempfile +from pathlib import Path +pass +from webwright.skills.library import Library +from webwright.skills.update import update, Trace + + +def run(): + with tempfile.TemporaryDirectory() as d: + lib = Library(d) + assert lib.list() == [] + + traces = [ + Trace(template="Get the top-{n} best-selling {entity} in {period}", code="print(1)", + answer=["Sprite"], meta={"site": "shopping_admin"}), + Trace(template="Get the reviewers who mention {x}", code="print(2)", answer=["Bob"]), + ] + added = update(traces, lib, method="grow") + assert len(added) == 2, "two new templates -> two skills added" + assert len(lib.list()) == 2, "library grew to 2" + + # idempotent: same templates again -> nothing new + added2 = update(traces, lib, method="grow") + assert added2 == [], "already-covered templates not re-added" + assert len(lib.list()) == 2, "library unchanged" + + # the distilled skill carries its code + provenance + s = lib.get(added[0]) + assert s.code == "print(1)" and s.meta["provenance"] == "distilled" + + print("test_update OK") + + +if __name__ == "__main__": + run() From 91a2090a115a0ffb62f72ba63a94ff27852d4fe5 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Tue, 30 Jun 2026 06:12:30 +0000 Subject: [PATCH 02/54] skills: prompt hint helper + skill_mode overlay; env-based model for skill_use CLI - skills/prompt.with_skill_hint: prepend skill-library usage hint to task prompt (non-invasive; webwright merges system_template by replacement, so prompt-level is the clean way) - config/skill_mode.yaml: optional overlay doc + step budget for skill-reuse runs - llm._model(): bare CLI (python -m webwright.tools.skill_use) builds model from SKILL_MODEL_NAME/ENDPOINT (or OPENAI_*) env -> same backend as agent, no hardcoded gateway Co-Authored-By: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> --- src/webwright/config/skill_mode.yaml | 13 +++++++++++++ src/webwright/skills/__init__.py | 3 ++- src/webwright/skills/prompt.py | 26 ++++++++++++++++++++++++++ 3 files changed, 41 insertions(+), 1 deletion(-) create mode 100644 src/webwright/config/skill_mode.yaml create mode 100644 src/webwright/skills/prompt.py diff --git a/src/webwright/config/skill_mode.yaml b/src/webwright/config/skill_mode.yaml new file mode 100644 index 0000000..b8d6158 --- /dev/null +++ b/src/webwright/config/skill_mode.yaml @@ -0,0 +1,13 @@ +# Skill-library mode (optional overlay): raise the step budget headroom for skill reuse runs. +# Enable by stacking: webwright run ... -c base.yaml -c model_openai.yaml -c skill_mode.yaml +# +# How the agent is told to reuse skills: the SKILL-LIBRARY block is prepended to the TASK prompt +# by the caller (see webwright.skills.prompt.with_skill_hint), NOT injected via system_template — +# webwright merges system_template by replacement, so a prompt-level hint is the clean, non-invasive +# way and keeps webwright's default behavior unchanged when this mode is off. +# +# Requires env: +# SKILL_LIBRARY_ROOT path to the skill library (read by the skill_use tool) +# SKILL_MODEL_NAME / SKILL_MODEL_ENDPOINT (optional) backend for skill_use; defaults to OPENAI_* +agent: + step_limit: 100 diff --git a/src/webwright/skills/__init__.py b/src/webwright/skills/__init__.py index 6f3383f..a4d119d 100644 --- a/src/webwright/skills/__init__.py +++ b/src/webwright/skills/__init__.py @@ -14,8 +14,9 @@ from .gate import gate, GateResult from .update import evolve, Trace # NOTE: don't import the `update` function here — it would from .llm import configure_llm # shadow the `update` submodule. Use update.evolve / update.update. +from .prompt import with_skill_hint __all__ = [ "Library", "Skill", "retrieve", "Candidate", "decide", "Decision", - "gate", "GateResult", "evolve", "Trace", "configure_llm", + "gate", "GateResult", "evolve", "Trace", "configure_llm", "with_skill_hint", ] diff --git a/src/webwright/skills/prompt.py b/src/webwright/skills/prompt.py new file mode 100644 index 0000000..5497b1c --- /dev/null +++ b/src/webwright/skills/prompt.py @@ -0,0 +1,26 @@ +"""Prompt helper: prepend a SKILL-LIBRARY hint to a task prompt so the agent reuses the library. + +Kept at the prompt level (not system_template) because webwright merges system_template by +replacement; a task-prompt hint is non-invasive and leaves default behavior unchanged when unused. +""" +from __future__ import annotations + +_HINT = """## Skill library (reuse before solving from scratch) +A library of previously-built executable code skills may contain one that helps this task. +BEFORE planning from scratch, query it ONCE from bash: + + python -m webwright.tools.skill_use --task "{task}" --library "{library}" + +It returns JSON {{verdict, skill_id, source_path, how_to_reuse}}: +- "use" : read source_path, copy it into your final_script, fill THIS task's params. +- "adapt" : read source_path, reuse its login/navigation/extraction core, change ONLY the last step. +- "skip" : no useful skill — solve from scratch. +Record your choice in skill_decision.json ({{skill_id, verdict, reason}}) before acting. + +--- +""" + + +def with_skill_hint(task_prompt: str, *, task: str, library: str) -> str: + """Prepend the skill-library hint to a task prompt.""" + return _HINT.format(task=task.replace('"', "'"), library=library) + task_prompt From a96336b44875ebeebc9e7403e1b5f4778762ae22 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Tue, 30 Jun 2026 09:06:18 +0000 Subject: [PATCH 03/54] skills: README for the module + env-based model for bare CLI - README: what the module is, the two plug points (skill_use tool + update CLI), components table, gate semantics, backend config, results summary - llm._model(): bare CLI builds model from SKILL_MODEL_NAME/ENDPOINT (or OPENAI_*) env Co-Authored-By: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> --- src/webwright/skills/README.md | 76 ++++++++++++++++++++++++++++++++++ src/webwright/skills/llm.py | 14 ++++++- 2 files changed, 88 insertions(+), 2 deletions(-) create mode 100644 src/webwright/skills/README.md diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md new file mode 100644 index 0000000..6ca27a7 --- /dev/null +++ b/src/webwright/skills/README.md @@ -0,0 +1,76 @@ +# `webwright.skills` — a memory / skill-library module for Webwright + +Turn solved tasks into **reusable, executable code skills**, retrieve and judge them at solve +time, gate what enters the library, and grow the library incrementally. A self-evolving loop: + +``` +solve --(gate: gold | self_verify)--> admit --(evolve: refine + parameterize + primitives)--> library + ^ | + | skill_use tool: retrieve + decide (use / adapt / skip) <--------------------------------------- + +``` + +This is the missing **reuse + accumulation** layer: Webwright already turns a task into a +parameterized script (`crafted_cli`); this module accumulates those across tasks, judges when a +prior skill applies, and improves skills as more solves arrive — with a gate so wrong solves don't +pollute the library. + +## How it plugs into Webwright + +Two touch points, **no change to the agent loop or default config**: + +1. **Reuse at solve time — the `skill_use` tool.** The agent invokes it from bash, exactly like + `self_reflection` / `image_qa`: + ```bash + python -m webwright.tools.skill_use --task "" --library "$SKILL_LIBRARY_ROOT" + ``` + It returns JSON `{verdict: use|adapt|skip, skill_id, source_path, how_to_reuse}`. The agent + reads `source_path` and reuses the skill (use = as-is, adapt = reuse core + change last step, + skip = solve from scratch). `webwright.skills.with_skill_hint(prompt, ...)` prepends a one-line + usage hint to the task prompt so the agent remembers to query the library first. + +2. **Growth after solving — the `update` CLI.** Distill a batch of gate-passed solves into a + library skill (offline, not in the solve loop): + ```bash + python -m webwright.skills.update --manifest batch.json --library ./library + ``` + `batch.json = {"template": "...", "runs": [{"dir","admit","params", ...}, ...]}`. + +## Components + +| file | role | +|---|---| +| `library.py` | `Skill` + `Library(root)`: on-disk skills (`/skill.py` + `meta.json`) | +| `retrieve.py` | `retrieve(task, library)` → ranked `Candidate`s (relevance) | +| `decide.py` | `decide(task, candidates)` → `Decision(verdict, skill_id, reason)` (utility: use/adapt/skip) | +| `gate.py` | `gate(result, method=gold\|self_verify\|none)` → admit? (keeps wrong solves out) | +| `update.py` | `evolve(traces, library)`: grow on the existing library — add / adapt-refine / keep; `_refine` parameterizes + decomposes into primitives, incrementally improving an existing skill | +| `llm.py` | `configure_llm(model)` + `llm()`: **backend-agnostic** via Webwright's `Model` abstraction; a bare CLI builds the model from `SKILL_MODEL_NAME`/`SKILL_MODEL_ENDPOINT` (or `OPENAI_*`) env — no hardcoded endpoint/key | +| `prompt.py` | `with_skill_hint(prompt, task, library)`: non-invasive task-prompt hint | + +## Gate + +`gate(method=...)` is the **admission** check (independent of the solving agent — not the same as +`self_reflection`, which is the agent's own completion condition): +- `gold` — compare against a known answer (benchmarks); strongest. +- `self_verify` — invariant only (non-empty + shape); weak placeholder when no gold exists. It does + not check *correctness*. Note: `self_reflection` cannot serve as the gate — `require_self_reflection_success` + makes it always `predicted_label==1`, so it would admit everything (agent grading itself). +- `none` — admit all (demos). + +## Backend + +Backend-agnostic. Either `configure_llm(model_config_or_Model)` once in-process, or set +`SKILL_MODEL_NAME` / `SKILL_MODEL_ENDPOINT` (falling back to `OPENAI_*`) so a bare tool invocation +uses the same backend as the running agent. No gateway or key is hardcoded. + +## Results (summary) + +Validated with this module (full data + analysis live in the companion research repo, not here): +- **Real website (public GitHub, read-only):** end-to-end loop works — two repos solved from + scratch → `update` distilled a parameterized skill → a held-out repo solved by reusing it + (agent called `skill_use`, verdict `use`, answer correct). +- **Incremental growth:** a second batch improves the existing skill in place (keeps the working + functions, adds robustness) rather than rewriting it. +- **Gate prevents pollution:** a wrong solve is dropped and never enters the library. +- **Reuse value is task-dependent:** step savings are modest on easy tasks (query overhead ≈ the + exploration it saves) and larger on harder tasks with more exploration to skip. diff --git a/src/webwright/skills/llm.py b/src/webwright/skills/llm.py index 2e0703e..278694a 100644 --- a/src/webwright/skills/llm.py +++ b/src/webwright/skills/llm.py @@ -25,8 +25,18 @@ def configure_llm(model: Any) -> None: def _model() -> Any: if _DEFAULT_MODEL is not None: return _DEFAULT_MODEL - # default: openai model from env (OPENAI_API_KEY / OPENAI_BASE_URL respected by the model class) - return get_model({"model_class": "openai"}) + # default: build an openai-style model from env so a bare CLI invocation + # (e.g. `python -m webwright.tools.skill_use`) uses the SAME backend as the running agent. + # Honors SKILL_MODEL_NAME / SKILL_MODEL_ENDPOINT (or OPENAI_* fallbacks); no hardcoded gateway. + import os + cfg = {"model_class": os.environ.get("SKILL_MODEL_CLASS", "openai")} + name = os.environ.get("SKILL_MODEL_NAME") or os.environ.get("OPENAI_MODEL") + endpoint = os.environ.get("SKILL_MODEL_ENDPOINT") or os.environ.get("OPENAI_ENDPOINT") + if name: + cfg["model_name"] = name + if endpoint: + cfg["openai_endpoint"] = endpoint + return get_model(cfg) def llm(system: str, user: str, *, model: Any = None, **_: Any) -> str: From 82fe0ba5a40437d23ce691a1710abb2e345bc7ce Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Tue, 30 Jun 2026 09:06:52 +0000 Subject: [PATCH 04/54] docs+tests: README skill-library section + skills unit tests - README: Skill Library section (what it is, reuse via skill_use tool, grow via update CLI, end-to-end validation summary) - tests/skills: 5 unit tests for library/gate/update/evolve/retrieve+decide Co-Authored-By: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> --- README.md | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/README.md b/README.md index 891bbd7..042c61f 100644 --- a/README.md +++ b/README.md @@ -165,6 +165,25 @@ python assets/task_showcase/app.py \ --- +## 🧠 Skill Library (reuse solved tasks across tasks) + +[`webwright.skills`](src/webwright/skills/) turns solved tasks into **reusable, executable code +skills**, retrieves and judges them at solve time, gates what enters the library, and grows the +library incrementally — a self-evolving *store → retrieve → use/adapt → gate → evolve* loop on top +of Webwright's code-as-action solves. Plugs in with **no change to the agent loop**: + +- **Reuse** — the agent calls `python -m webwright.tools.skill_use --task "..." --library ...` + (like `self_reflection`/`image_qa`); it returns `{verdict: use|adapt|skip, source_path}`. +- **Grow** — `python -m webwright.skills.update --manifest batch.json --library ./library` + distills a batch of gate-passed solves into a parameterized, primitive-decomposed skill. + +Validated end-to-end on a real public website (read-only GitHub): solve two repos from scratch → +`update` builds a parameterized skill → a held-out repo is solved by reusing it (agent calls +`skill_use`, verdict `use`, answer correct); a wrong solve is kept out by the gate; a second batch +improves the existing skill in place. See [`src/webwright/skills/README.md`](src/webwright/skills/README.md). + +--- + ## 🚀 Quick Start ### Prerequisites From 05aab3a081eef593da77bbaa179f7e7038de06d9 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Tue, 30 Jun 2026 09:18:12 +0000 Subject: [PATCH 05/54] skills: translate all comments/docstrings to English Co-Authored-By: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> --- src/webwright/skills/decide.py | 6 ++-- src/webwright/skills/gate.py | 28 +++++++++++------- src/webwright/skills/retrieve.py | 8 +++-- src/webwright/skills/update.py | 51 ++++++++++++++++++-------------- 4 files changed, 53 insertions(+), 40 deletions(-) diff --git a/src/webwright/skills/decide.py b/src/webwright/skills/decide.py index eaddc8d..a966adb 100644 --- a/src/webwright/skills/decide.py +++ b/src/webwright/skills/decide.py @@ -1,8 +1,8 @@ -"""判断用不用：候选 + 任务 → use / adapt / skip（utility）。 +"""Decide whether to use: candidates + task -> use / adapt / skip (utility). -接口稳定（实现可换）： +Stable interface (swappable implementation): decide(task, candidates, *, method="llm") -> Decision -相关 ≠ 有用：retrieve 给"像不像"，decide 给"该不该用、怎么用"。 +Relevant != useful: retrieve gives "how similar", decide gives "whether and how to use it". """ from __future__ import annotations from dataclasses import dataclass diff --git a/src/webwright/skills/gate.py b/src/webwright/skills/gate.py index 4ebc43e..0e9baac 100644 --- a/src/webwright/skills/gate.py +++ b/src/webwright/skills/gate.py @@ -1,17 +1,23 @@ -"""准入闸：只有"对的"解/技能才准进库，防 correct-but-narrow / regression 污染。 -gate 是【独立第二只眼】，与解题 agent 自己的 self_reflection 不同（后者是解题完成条件）。 +"""Admission gate: only "correct" solves/skills enter the library, preventing correct-but-narrow / +regression pollution. The gate is an INDEPENDENT second eye — distinct from the solving agent's own +self_reflection (which is a solve-completion condition, not an admission check). -接口稳定（实现可换），method 可配置： +Stable interface (swappable implementation), configurable method: gate(result, *, gold=None, output_schema=None, method="auto") -> GateResult -- method="gold" : 与 gold 比对（WebArena 等有标准答案；真独立、能挡住抽错的解）。★推荐 -- method="self_verify" : 不变量（result 非空 + shape 合 output_schema）。无 gold 时的弱占位。 - ⚠️ 局限：只查"有没有/形状对不对"，不查"对不对"——抽错但非空的答案照样放行。 - （注：webwright 的 self_reflection 因 require_self_reflection_success 而恒为 - predicted_label==1，故不能用它当 gate；那是解题完成条件，非独立准入。） -- method="none" : 不把关（纯演示复用，不防污染）。 -- method="auto" : 有 gold 用 gold，否则 self_verify。 -升级路径（next step）：真实站用 WebJudge（OM2W 官方 judge）或跨源一致核验，做真独立把关。 +- method="gold" : compare against gold (benchmarks like WebArena; truly independent, catches + mis-extracted solves). Recommended. +- method="self_verify" : invariant only (result non-empty + shape matches output_schema). A weak + placeholder when no gold exists. + Limitation: only checks "present / right shape", not "correct" — a wrong but + non-empty answer is admitted anyway. + (Note: webwright's self_reflection is always predicted_label==1 due to + require_self_reflection_success, so it cannot serve as the gate — that is a + solve-completion condition, not independent admission.) +- method="none" : no gate (demo reuse only, no pollution protection). +- method="auto" : use gold if available, else self_verify. +Upgrade path (next step): for real websites use WebJudge (OM2W's official judge) or cross-source +consistency checks for a truly independent gate. """ from __future__ import annotations from dataclasses import dataclass diff --git a/src/webwright/skills/retrieve.py b/src/webwright/skills/retrieve.py index fae5d1e..5f7a405 100644 --- a/src/webwright/skills/retrieve.py +++ b/src/webwright/skills/retrieve.py @@ -1,8 +1,9 @@ -"""取：任务 → 最相关的候选技能（relevance）。 +"""Retrieve: task -> most relevant candidate skills (relevance only). -接口稳定（实现可换）： +Stable interface (swappable implementation): retrieve(task, library, *, k=3, method="llm") -> [Candidate] -MVP: 单次 LLM 调用，把整库当 flat catalog 列进 prompt 让它选。库大了换 embedding，接口不变。 +MVP: a single LLM call that lists the whole library as a flat catalog in the prompt and lets it +pick. Swap to embeddings when the library grows large — the interface stays the same. """ from __future__ import annotations from dataclasses import dataclass @@ -15,6 +16,7 @@ class Candidate: skill: Skill score: float # relevance 0..1 + reason: str diff --git a/src/webwright/skills/update.py b/src/webwright/skills/update.py index bcb4a80..61101fa 100644 --- a/src/webwright/skills/update.py +++ b/src/webwright/skills/update.py @@ -1,11 +1,13 @@ -"""沉淀（间歇）：把通过 gate 的解蒸馏、写回 library，让库从使用中长大。 +"""Sediment (intermittent): distill gate-passed solves back into the library so it grows from use. -接口稳定（实现可换）： - update(traces, library, *, method="grow") -> [被加/更新的 skill_id] +Stable interface (swappable implementation): + update(traces, library, *, method="grow") -> [added/updated skill_ids] -- method="grow" : 库里还没覆盖这个 template 的，就把这条成功解原样提升为技能（最小形态）。 -- method="refine" : 批量提炼——对齐 N 个 gate 过的解 → 参数化(泛化) + 拆出可复用 primitive + 薄任务层 - → 一个更好的库技能。这是"update 加泛化性 + primitive 复用性"的实现（单次 LLM 批量调用）。 +- method="grow" : if the library does not yet cover this template, promote the successful solve + as-is into a skill (minimal form). +- method="refine" : batch distillation — align N gate-passed solves -> parameterize (generalize) + + factor out reusable primitives + a thin task layer -> one better library skill. + This is where update adds generalization + primitive reusability (one batched LLM call). """ from __future__ import annotations import json @@ -19,10 +21,10 @@ @dataclass class Trace: template: str - code: str # 这条任务的 final_script（已过 gate = 正确） + code: str # this task's final_script (already gate-passed = correct) answer: object = None meta: dict = field(default_factory=dict) # params / site / start_url / output_schema ... - # usage：这条任务是怎么用库的（驱动 update 的信号） + # usage: how this task used the library (the signal that drives update) used_skill_id: str | None = None verdict: str | None = None # use | adapt | skip correct: bool = True @@ -69,14 +71,15 @@ def _extract_code(txt: str) -> str: def _refine(traces: list[Trace], library: Library) -> list[str]: - """批量提炼：对齐 N 个 gate 过的解 → 参数化 + primitive。 - 增量：若库里已有同 template 技能，则在【现有技能基础上】改进/加宽（而非从原始解重写）。""" + """Batch distillation: align N gate-passed solves -> parameterize + primitives. + Incremental: if a skill for the same template already exists, improve/widen it on top of the + existing skill (rather than rewriting from the raw solves).""" if not traces: return [] template = traces[0].template schema = traces[0].meta.get("output_schema") sid = _slug(template) - existing = library.get(sid) # 已有技能？→ 增量演化 + existing = library.get(sid) # skill already exists? -> incremental evolution blocks = [f"## Template\n{template}\n\n## Required output_schema for retrieved_data\n{json.dumps(schema)}\n"] if existing and existing.code: @@ -120,21 +123,23 @@ def _grow(traces: list[Trace], library: Library) -> list[str]: def evolve(traces: list[Trace], library: Library) -> dict: - """统一 update：在【已有库】上，按每条轨迹的 usage(use/adapt/skip)决定怎么改库。 - 这是"可持续增长的库"的核心——不是每次从零建，而是在 v_{n-1} 上长出 v_n。 + """Unified update: evolve the EXISTING library, deciding per trace's usage (use/adapt/skip) how + to change it. This is the core of a continuously-growing library — not rebuilt from scratch each + time, but grown from v_{n-1} into v_n. - - USE 成功的轨迹：技能够好，不动库（只是复用证据）。 - - ADAPT 成功的轨迹：复用了核心、fix 了末端 → 把这批 fix 后的解【提炼回该 template 的技能】 - （加宽/更稳）。这就是"fix 沉淀进库"。 - - SKIP / 库没覆盖：该 template 还没有技能 → 用这批解【新增】一个技能。 + - USE (successful) : the skill is good enough, leave it untouched (just reuse evidence). + - ADAPT (successful) : core reused, last step fixed -> refine this batch's fixed solves + back into the template's skill (widen/harden). This is how a fix + sediments into the library. + - SKIP / not yet covered : the template has no skill yet -> add one from this batch. - 只吃 gate 过(correct=True)的轨迹（防污染）。返回一份 changelog。 + Only consumes gate-passed (correct=True) traces (pollution protection). Returns a changelog. """ good = [t for t in traces if t.correct] changelog = {"use": [], "adapt_refined": [], "added": [], "dropped_wrong": len(traces) - len(good)} existing_templates = {s.meta.get("template"): s.skill_id for s in library.list()} - # 按 template 分组（同族解一起提炼/沉淀） + # group by template (same-family solves are distilled/sedimented together) by_tmpl: dict[str, list[Trace]] = {} for t in good: by_tmpl.setdefault(t.template, []).append(t) @@ -142,15 +147,15 @@ def evolve(traces: list[Trace], library: Library) -> dict: for tmpl, group in by_tmpl.items(): verdicts = {t.verdict for t in group} if tmpl not in existing_templates: - # 库没覆盖 → 新增（用这批解提炼出一个技能） + # not covered -> add (distill a skill from this batch) sid = _refine(group, library)[0] changelog["added"].append(sid) elif "adapt" in verdicts: - # 有 fix 发生 → 把 fix 后的解重新提炼回该技能（加宽/更稳） - sid = _refine(group, library)[0] # _refine 用同 slug，覆盖加宽 + # a fix happened -> refine the fixed solves back into the skill (widen/harden) + sid = _refine(group, library)[0] # _refine uses the same slug, overwrites + widens changelog["adapt_refined"].append(sid) else: - # 全 use 成功 → 技能够好，不动 + # all use-success -> skill is good enough, leave it changelog["use"].append(existing_templates[tmpl]) return changelog From bb0d0cde7b5f346582f0590b0bcb9b96051fa2e5 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Tue, 30 Jun 2026 09:22:07 +0000 Subject: [PATCH 06/54] skills: drop dead code in update (grow path superseded by evolve) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Remove _grow / update() / _UPDATERS dispatch — evolve() is the single entry now; drop the test_update test that exercised the removed grow path. Keep retrieve/llm fallbacks (useful). Co-Authored-By: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> --- src/webwright/skills/__init__.py | 4 ++-- src/webwright/skills/update.py | 21 ------------------- tests/skills/test_update.py | 36 -------------------------------- 3 files changed, 2 insertions(+), 59 deletions(-) delete mode 100644 tests/skills/test_update.py diff --git a/src/webwright/skills/__init__.py b/src/webwright/skills/__init__.py index a4d119d..79a0bf3 100644 --- a/src/webwright/skills/__init__.py +++ b/src/webwright/skills/__init__.py @@ -12,8 +12,8 @@ from .retrieve import retrieve, Candidate from .decide import decide, Decision from .gate import gate, GateResult -from .update import evolve, Trace # NOTE: don't import the `update` function here — it would -from .llm import configure_llm # shadow the `update` submodule. Use update.evolve / update.update. +from .update import evolve, Trace +from .llm import configure_llm from .prompt import with_skill_hint __all__ = [ diff --git a/src/webwright/skills/update.py b/src/webwright/skills/update.py index 61101fa..e451859 100644 --- a/src/webwright/skills/update.py +++ b/src/webwright/skills/update.py @@ -108,20 +108,6 @@ def _refine(traces: list[Trace], library: Library) -> list[str]: return [sid] -def _grow(traces: list[Trace], library: Library) -> list[str]: - existing = {s.meta.get("template") for s in library.list()} - added = [] - for tr in traces: - if not tr.template or tr.template in existing: - continue - sid = _slug(tr.template) - meta = {"template": tr.template, "provenance": "distilled", **tr.meta} - library.add(Skill(skill_id=sid, code=tr.code, meta=meta)) - existing.add(tr.template) - added.append(sid) - return added - - def evolve(traces: list[Trace], library: Library) -> dict: """Unified update: evolve the EXISTING library, deciding per trace's usage (use/adapt/skip) how to change it. This is the core of a continuously-growing library — not rebuilt from scratch each @@ -160,13 +146,6 @@ def evolve(traces: list[Trace], library: Library) -> dict: return changelog -_UPDATERS = {"grow": _grow, "refine": _refine} - - -def update(traces, library: Library, *, method: str = "grow") -> list[str]: - return _UPDATERS[method](traces, library) - - # ---------- CLI: batch update via a manifest ---------- def traces_from_manifest(manifest: dict) -> list["Trace"]: """manifest = {"template": str, "runs": [{"dir","admit","params","answer"?,"verdict"?}, ...]}. diff --git a/tests/skills/test_update.py b/tests/skills/test_update.py deleted file mode 100644 index 2abfffe..0000000 --- a/tests/skills/test_update.py +++ /dev/null @@ -1,36 +0,0 @@ -"""Unit test: update 'grow' (deterministic, no LLM).""" -import sys, tempfile -from pathlib import Path -pass -from webwright.skills.library import Library -from webwright.skills.update import update, Trace - - -def run(): - with tempfile.TemporaryDirectory() as d: - lib = Library(d) - assert lib.list() == [] - - traces = [ - Trace(template="Get the top-{n} best-selling {entity} in {period}", code="print(1)", - answer=["Sprite"], meta={"site": "shopping_admin"}), - Trace(template="Get the reviewers who mention {x}", code="print(2)", answer=["Bob"]), - ] - added = update(traces, lib, method="grow") - assert len(added) == 2, "two new templates -> two skills added" - assert len(lib.list()) == 2, "library grew to 2" - - # idempotent: same templates again -> nothing new - added2 = update(traces, lib, method="grow") - assert added2 == [], "already-covered templates not re-added" - assert len(lib.list()) == 2, "library unchanged" - - # the distilled skill carries its code + provenance - s = lib.get(added[0]) - assert s.code == "print(1)" and s.meta["provenance"] == "distilled" - - print("test_update OK") - - -if __name__ == "__main__": - run() From ba4fa2a3e5095924dac3ec529eafb67db3b1c946 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Thu, 2 Jul 2026 08:21:53 +0000 Subject: [PATCH 07/54] skills: fix refine truncation + timeout + fence stripping (found in eval) Three bugs hit when update.refine emits a large skill on a slow gateway: - llm() ignored max_tokens -> model default ~4000 truncated the refined skill mid-code - llm() had no timeout override -> model default 120s ReadTimeout'd on the ~16k-token refine (now request_timeout_seconds defaults 600, env SKILL_MODEL_TIMEOUT) - _extract_code returned raw text (with ```python fence) when the closing fence was missing (truncated) -> skill failed to compile; now strips the opening fence anyway Co-Authored-By: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> --- src/webwright/skills/llm.py | 13 ++++++++++--- src/webwright/skills/update.py | 1 + 2 files changed, 11 insertions(+), 3 deletions(-) diff --git a/src/webwright/skills/llm.py b/src/webwright/skills/llm.py index 278694a..b79b98d 100644 --- a/src/webwright/skills/llm.py +++ b/src/webwright/skills/llm.py @@ -29,7 +29,10 @@ def _model() -> Any: # (e.g. `python -m webwright.tools.skill_use`) uses the SAME backend as the running agent. # Honors SKILL_MODEL_NAME / SKILL_MODEL_ENDPOINT (or OPENAI_* fallbacks); no hardcoded gateway. import os - cfg = {"model_class": os.environ.get("SKILL_MODEL_CLASS", "openai")} + # long request timeout: refine emits a large skill (~16k tokens) which is slow on a busy + # gateway; the model default (120s) truncates/ReadTimeouts. Overridable via SKILL_MODEL_TIMEOUT. + cfg = {"model_class": os.environ.get("SKILL_MODEL_CLASS", "openai"), + "request_timeout_seconds": int(os.environ.get("SKILL_MODEL_TIMEOUT", "600"))} name = os.environ.get("SKILL_MODEL_NAME") or os.environ.get("OPENAI_MODEL") endpoint = os.environ.get("SKILL_MODEL_ENDPOINT") or os.environ.get("OPENAI_ENDPOINT") if name: @@ -39,13 +42,17 @@ def _model() -> Any: return get_model(cfg) -def llm(system: str, user: str, *, model: Any = None, **_: Any) -> str: - """Single-turn call. Returns raw text. `model` overrides the configured default.""" +def llm(system: str, user: str, *, model: Any = None, max_tokens: int | None = None, **_: Any) -> str: + """Single-turn call. Returns raw text. `model` overrides the configured default. + max_tokens caps the reply length (the model default is small ~4000, which truncates a + refined skill); pass it through to the model so large code outputs aren't cut off.""" m = model if model is not None else _model() messages = [ m.format_message(role="system", content=system), m.format_message(role="user", content=user), ] + if max_tokens is not None: + return m(messages, max_output_tokens=max_tokens) return m(messages) diff --git a/src/webwright/skills/update.py b/src/webwright/skills/update.py index e451859..749992b 100644 --- a/src/webwright/skills/update.py +++ b/src/webwright/skills/update.py @@ -41,6 +41,7 @@ def _extract_code(txt: str) -> str: end = txt.rfind("```") if end > m.end(): return txt[m.end():end] + return txt[m.end():] # opening fence but no close (e.g. truncated) -> strip the fence anyway return txt From 2784fc65e6ac905729e4725477b68b14b7fc3121 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Fri, 3 Jul 2026 02:23:22 +0000 Subject: [PATCH 08/54] skills README: add end-to-end How-to-use (manifest schema, params, solve-time reuse, direct skill run) Co-Authored-By: Claude Fable 5 --- src/webwright/skills/README.md | 112 +++++++++++++++++++++++++++++++-- 1 file changed, 107 insertions(+), 5 deletions(-) diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md index 6ca27a7..ada3a4a 100644 --- a/src/webwright/skills/README.md +++ b/src/webwright/skills/README.md @@ -9,10 +9,13 @@ solve --(gate: gold | self_verify)--> admit --(evolve: refine + parameterize | skill_use tool: retrieve + decide (use / adapt / skip) <--------------------------------------- + ``` -This is the missing **reuse + accumulation** layer: Webwright already turns a task into a -parameterized script (`crafted_cli`); this module accumulates those across tasks, judges when a -prior skill applies, and improves skills as more solves arrive — with a gate so wrong solves don't -pollute the library. +This is the missing **reuse + accumulation** layer: it consumes the `final_script.py` every +Webwright solve already produces (plain or crafted mode — both work), accumulates skills across +tasks, judges when a prior skill applies, and improves skills as more solves arrive — with a gate +so wrong solves don't pollute the library. It complements `crafted_cli`: where `crafted_cli` +parameterizes a single task's script by anticipating what might vary, `update.refine` +parameterizes **across multiple verified solves** — the differences actually observed between +instances become the parameters. ## How it plugs into Webwright @@ -33,7 +36,106 @@ Two touch points, **no change to the agent loop or default config**: ```bash python -m webwright.skills.update --manifest batch.json --library ./library ``` - `batch.json = {"template": "...", "runs": [{"dir","admit","params", ...}, ...]}`. + Manifest schema and a full walkthrough: see **How to use** below. + +## How to use (end-to-end) + +The library grows **offline** from batches of solved tasks, and is consumed **at solve time** by +the agent. Tasks are provided **manually** today — you pick which tasks to solve and batch. The +current focus is **same-template generalization**, so feed several instances of the SAME template +(3+ instances with different parameter values works well): `refine` aligns them, and exactly what +differs between instances becomes the skill's parameters — more instances, wider generalization. +(Planned: bootstrap — automatically expand one seed task into multiple instances.) + +### 1. Solve a few instances of a template (normal Webwright runs) + +```bash +python -m webwright.run.cli main \ + -t "How many commits did kilian make to a11yproject on 3/1/2023?" \ + --task-id t132_a --start-url http://gitlab.example.com -o outputs \ + -c base.yaml -c model_openai.yaml +``` + +Each run leaves a directory containing `final_script.py` (the executable solve) and +`agent_response.json` (the answer). Repeat for 2–3 more instances of the same template with +different values (another user / repo / date). + +### 2. Gate the solves, write the manifest + +Judge each run (`gate(result, method="gold")` against a known answer, or `method="self_verify"` +without one) and write one manifest per batch: + +```jsonc +// batch.json +{ + "template": "How many commits did {{user}} make to {{repo}} on {{date}}?", + "runs": [ + { + "dir": "outputs/t132_a_20260703_120000", // run dir; final_script.py is read from it + "admit": true, // gate verdict — false rows NEVER enter the library + "params": {"user": "kilian", "repo": "a11yproject", "date": "3/1/2023"}, + "verdict": "skip", // how the run used the library: skip = solved from + // scratch; use / adapt = reused a skill + // (adapt triggers refine-back into the skill) + "site": "gitlab", + "output_schema": ["number"] // required shape of retrieved_data + // "answer": optional — read from the run dir's agent_response.json when omitted + }, + { "dir": "outputs/t132_b_20260703_121500", "admit": true, + "params": {"user": "gao", "repo": "2019", "date": "4/6/2023"}, + "verdict": "skip", "site": "gitlab", "output_schema": ["number"] } + ] +} +``` + +`params` is each instance's concrete values — this is what powers generalization: `refine` aligns +the runs, keeps what is identical as the skeleton, and exposes exactly these differing values as +the skill's arguments. + +### 3. Build / evolve the library + +```bash +export OPENAI_API_KEY=... # backend key (never stored by the module) +# optional — defaults to OPENAI_MODEL / OPENAI_ENDPOINT: +export SKILL_MODEL_NAME=gpt-5.4 SKILL_MODEL_ENDPOINT=https://api.openai.com/v1/responses +python -m webwright.skills.update --manifest batch.json --library ./library +``` + +Prints a changelog: `{"added": [...], "adapt_refined": [...], "use": [...], "dropped_wrong": n}`. +Re-run with later batches any time — a new template **adds** a skill, new solves for an existing +template **refine it in place** (keeps its working functions), templates with no new traces are +left untouched. Batches may mix templates. + +### 4. Reuse at solve time + +```python +from webwright.skills import with_skill_hint +prompt = with_skill_hint(prompt, task=task_text, library="./library") +``` + +```bash +SKILL_LIBRARY_ROOT=./library python -m webwright.run.cli main -t "$prompt" ... +``` + +The hint tells the agent to query the library first; the agent runs the `skill_use` tool, gets +`{verdict, skill_id, source_path, how_to_reuse}`, reads the skill source, and reuses it +(use = as-is with new parameter values, adapt = reuse the core + change the last step, +skip = solve from scratch). + +### 5. Run a skill directly (optional) + +Every skill is also a standalone script: it reads a `taskspec.json` (parameters at run time) and +writes `agent_response.json`: + +```bash +cat > taskspec.json <<'EOF' +{"params": {"user": "byte", "repo": "empathy-prompts", "date": "4/2/2023"}, + "start_url": "http://gitlab.example.com", "credentials": null, + "output_schema": ["number"]} +EOF +python library/how_many_commits_did_user_make_to_repo_on_date/skill.py taskspec.json +cat agent_response.json +``` ## Components From c293a48517f496996467f2d6b7eddbbb0c42ef89 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Fri, 3 Jul 2026 02:27:55 +0000 Subject: [PATCH 09/54] skills README: batch pipeline example (tasks.json -> solve loop -> gate+manifest -> update -> reuse); fix output_schema examples to gate's {type} form Co-Authored-By: Claude Fable 5 --- src/webwright/skills/README.md | 64 ++++++++++++++++++++++++++++++++-- 1 file changed, 61 insertions(+), 3 deletions(-) diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md index ada3a4a..a7bd1e6 100644 --- a/src/webwright/skills/README.md +++ b/src/webwright/skills/README.md @@ -78,12 +78,12 @@ without one) and write one manifest per batch: // scratch; use / adapt = reused a skill // (adapt triggers refine-back into the skill) "site": "gitlab", - "output_schema": ["number"] // required shape of retrieved_data + "output_schema": {"type": "number"} // required shape of retrieved_data // "answer": optional — read from the run dir's agent_response.json when omitted }, { "dir": "outputs/t132_b_20260703_121500", "admit": true, "params": {"user": "gao", "repo": "2019", "date": "4/6/2023"}, - "verdict": "skip", "site": "gitlab", "output_schema": ["number"] } + "verdict": "skip", "site": "gitlab", "output_schema": {"type": "number"} } ] } ``` @@ -131,12 +131,70 @@ writes `agent_response.json`: cat > taskspec.json <<'EOF' {"params": {"user": "byte", "repo": "empathy-prompts", "date": "4/2/2023"}, "start_url": "http://gitlab.example.com", "credentials": null, - "output_schema": ["number"]} + "output_schema": {"type": "number"}} EOF python library/how_many_commits_did_user_make_to_repo_on_date/skill.py taskspec.json cat agent_response.json ``` +### 6. The whole pipeline in one go (a batch of tasks) + +Steps 1–3 driven by a single task file. `tasks.json` — one entry per instance of the template +(`gold` is optional; with it the gate compares answers, without it it falls back to `self_verify`): + +```json +[ + {"id": "t132_a", "task": "How many commits did kilian make to a11yproject on 3/1/2023?", + "params": {"user": "kilian", "repo": "a11yproject", "date": "3/1/2023"}, "gold": 1}, + {"id": "t132_b", "task": "How many commits did gao make to 2019 on 4/6/2023?", + "params": {"user": "gao", "repo": "2019", "date": "4/6/2023"}, "gold": 0} +] +``` + +```bash +START_URL=http://gitlab.example.com + +# 1) solve every instance (sequential; add xargs -P N or & to parallelize) +jq -c '.[]' tasks.json | while read -r row; do + python -m webwright.run.cli main -t "$(jq -r .task <<<"$row")" \ + --task-id "$(jq -r .id <<<"$row")" --start-url "$START_URL" -o outputs \ + -c base.yaml -c model_openai.yaml +done + +# 2) gate each run + assemble the manifest +python - <<'PY' +import json, glob +from webwright.skills import gate + +TEMPLATE = "How many commits did {{user}} make to {{repo}} on {{date}}?" +SCHEMA = {"type": "number"} +runs = [] +for t in json.load(open("tasks.json")): + d = sorted(glob.glob(f"outputs/{t['id']}_*"))[-1] # newest run dir of this task + answer = json.load(open(f"{d}/agent_response.json"))["retrieved_data"] + g = gate(answer, gold=t.get("gold"), output_schema=SCHEMA) # gold if present, else self_verify + runs.append({"dir": d, "admit": g.admit, "params": t["params"], "verdict": "skip", + "site": "gitlab", "output_schema": SCHEMA}) +json.dump({"template": TEMPLATE, "runs": runs}, open("batch.json", "w"), indent=2) +print(sum(r["admit"] for r in runs), "of", len(runs), "admitted") +PY + +# 3) evolve the library +python -m webwright.skills.update --manifest batch.json --library ./library + +# 4) solve NEW instances of the template WITH the library: prepend the skill hint to the +# prompt (SKILL_LIBRARY_ROOT alone is not enough — the hint is what tells the agent to query) +TASK="How many commits did byte make to empathy-prompts on 4/2/2023?" +PROMPT=$(python -c 'import sys; from webwright.skills import with_skill_hint +print(with_skill_hint(sys.argv[1], task=sys.argv[1], library="./library"))' "$TASK") +SKILL_LIBRARY_ROOT=./library python -m webwright.run.cli main -t "$PROMPT" \ + --task-id t132_new --start-url "$START_URL" -o outputs -c base.yaml -c model_openai.yaml +``` + +Repeat 1–3 whenever a new batch of solves lands — the library evolves in place (new templates are +added, existing skills are refined, untouched skills stay as they are). This is exactly the loop +our WebArena evaluation runs (train → gate → update → held-out reuse). + ## Components | file | role | From 7deab7661b99bf10b74d8293995021f5c8b02900 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Fri, 3 Jul 2026 02:31:05 +0000 Subject: [PATCH 10/54] skills README: manifest field-by-field table; Results summary with WebArena numbers Co-Authored-By: Claude Fable 5 --- src/webwright/skills/README.md | 33 +++++++++++++++++++++++++++------ 1 file changed, 27 insertions(+), 6 deletions(-) diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md index a7bd1e6..f7d054a 100644 --- a/src/webwright/skills/README.md +++ b/src/webwright/skills/README.md @@ -88,9 +88,18 @@ without one) and write one manifest per batch: } ``` -`params` is each instance's concrete values — this is what powers generalization: `refine` aligns -the runs, keeps what is identical as the skeleton, and exposes exactly these differing values as -the skill's arguments. +Field by field: + +| field | required | meaning | +|---|---|---| +| `template` | yes | the template sentence with `{{param}}` placeholders. **Skills are keyed by it**: a manifest whose template already has a skill refines that skill in place; a new template adds a new skill. Use the same string across batches of the same template. | +| `runs[].dir` | yes | a Webwright run directory; `final_script.py` is read from it | +| `runs[].admit` | yes | the gate verdict; `false` rows are dropped and never enter the library | +| `runs[].params` | yes | this instance's concrete values — `refine` aligns the runs and exposes exactly these differing values as the skill's arguments (this is what powers generalization) | +| `runs[].verdict` | no (default `skip`) | how this run used the library: `skip` = solved from scratch; `use` = reused a skill as-is; `adapt` = reused + fixed the last step (**`adapt` is what triggers refining the fix back into the skill**) | +| `runs[].site` | no | site tag stored in the skill's meta (helps retrieval) | +| `runs[].output_schema` | no | required shape of `retrieved_data`, e.g. `{"type": "number"}` | +| `runs[].answer` | no | this run's answer; read from the run dir's `agent_response.json` when omitted | ### 3. Build / evolve the library @@ -226,11 +235,23 @@ uses the same backend as the running agent. No gateway or key is hardcoded. ## Results (summary) Validated with this module (full data + analysis live in the companion research repo, not here): + +- **WebArena — 10 templates × 3 domains (shopping_admin / gitlab / map), gold gate.** Per template + 3 train solves build the library, 2 held-out instances measure reuse (WITH library vs from + scratch): held-out **70% vs 55% accuracy (+15pp), 14.7 vs 17.1 steps**; train 86% vs 76%. + 4 held-out tasks unsolvable from scratch are solved with the library; net reuse-wins 7 : 1 + regression. Largest saving: 33 steps → 10. +- **Retrieval stays reliable as the library grows:** all 20 held-out solves picked the correct + skill from the shared library (grown to 10 skills), including telling apart two near-duplicate + commit-counting skills. +- **Mixed-template batches evolve safely:** mixed batches add new templates, refine existing + skills in place, and leave skills with no new traces byte-identical — zero cross-contamination; + held-out reuse against a mixed-built library matches the per-template-built one. +- **Incremental growth:** a later batch improves the existing skill in place (keeps the working + functions, adds robustness) rather than rewriting it. +- **Gate prevents pollution:** wrong solves (7 of 30 train) are dropped and never enter the library. - **Real website (public GitHub, read-only):** end-to-end loop works — two repos solved from scratch → `update` distilled a parameterized skill → a held-out repo solved by reusing it (agent called `skill_use`, verdict `use`, answer correct). -- **Incremental growth:** a second batch improves the existing skill in place (keeps the working - functions, adds robustness) rather than rewriting it. -- **Gate prevents pollution:** a wrong solve is dropped and never enters the library. - **Reuse value is task-dependent:** step savings are modest on easy tasks (query overhead ≈ the exploration it saves) and larger on harder tasks with more exploration to skip. From f7b0779826ffc9a687b85c0a8749e0c3311ad0fc Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Fri, 3 Jul 2026 02:50:58 +0000 Subject: [PATCH 11/54] skills: harden pipeline entry points (review findings) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - traces_from_manifest: 'admit' is now REQUIRED per run — a missing gate verdict raises instead of silently defaulting to admitted (was the main pollution risk) - _slug: templates longer than 48 chars get a short content-hash suffix so two templates sharing a long prefix can no longer overwrite each other's skill - skill_use.recommend: the decision's skill_id must be one of the RETRIEVED candidates; anything else (LLM hallucination, even an existing library id) downgrades to skip - tests for all three Co-Authored-By: Claude Fable 5 --- src/webwright/skills/update.py | 15 ++++++++++++--- src/webwright/tools/skill_use.py | 5 +++++ tests/skills/test_evolve.py | 15 +++++++++++++++ tests/skills/test_retrieve_decide.py | 15 +++++++++++++++ 4 files changed, 47 insertions(+), 3 deletions(-) diff --git a/src/webwright/skills/update.py b/src/webwright/skills/update.py index 749992b..cf8f722 100644 --- a/src/webwright/skills/update.py +++ b/src/webwright/skills/update.py @@ -10,6 +10,7 @@ This is where update adds generalization + primitive reusability (one batched LLM call). """ from __future__ import annotations +import hashlib import json import re from dataclasses import dataclass, field @@ -32,7 +33,12 @@ class Trace: def _slug(template: str) -> str: s = re.sub(r"[^a-z0-9]+", "_", template.lower()).strip("_") - return s[:48] or "skill" + if not s: + return "skill" + if len(s) <= 48: + return s + # truncation could collide two templates that share a long prefix -> disambiguate with a hash + return f"{s[:40]}_{hashlib.md5(template.encode()).hexdigest()[:7]}" def _extract_code(txt: str) -> str: @@ -150,11 +156,14 @@ def evolve(traces: list[Trace], library: Library) -> dict: # ---------- CLI: batch update via a manifest ---------- def traces_from_manifest(manifest: dict) -> list["Trace"]: """manifest = {"template": str, "runs": [{"dir","admit","params","answer"?,"verdict"?}, ...]}. - Reads each run's final_script.py; builds a Trace. correct = the run's gate verdict (admit).""" + Reads each run's final_script.py; builds a Trace. correct = the run's gate verdict (admit). + "admit" is REQUIRED per run — a missing gate verdict must fail loudly, not silently enter.""" from pathlib import Path template = manifest.get("template", "") out = [] for r in manifest.get("runs", []): + if "admit" not in r: + raise KeyError(f"manifest run missing required 'admit' (gate verdict): {r.get('dir', r)}") d = Path(r["dir"]) fs = d / "final_script.py" code = fs.read_text(encoding="utf-8") if fs.exists() else "" @@ -165,7 +174,7 @@ def traces_from_manifest(manifest: dict) -> list["Trace"]: except Exception: pass out.append(Trace(template=template, code=code, answer=answer, - correct=bool(r.get("admit", True)), + correct=bool(r["admit"]), verdict=r.get("verdict", "skip"), used_skill_id=r.get("used_skill_id"), meta={"params": r.get("params", {}), "site": r.get("site", ""), diff --git a/src/webwright/tools/skill_use.py b/src/webwright/tools/skill_use.py index 9acc210..09e2a1b 100644 --- a/src/webwright/tools/skill_use.py +++ b/src/webwright/tools/skill_use.py @@ -28,6 +28,11 @@ def recommend(task: str, library_root: str) -> dict: if not cands: return {"verdict": "skip", "skill_id": None, "reason": "library has no relevant skill"} d = decide(task, cands) + # the decision must point at a RETRIEVED candidate — an LLM-hallucinated id (even one that + # happens to exist in the library) must not be recommended + if d.verdict != "skip" and d.skill_id not in {c.skill.skill_id for c in cands}: + return {"verdict": "skip", "skill_id": None, + "reason": f"decided skill '{d.skill_id}' is not among the retrieved candidates"} out = {"verdict": d.verdict, "skill_id": d.skill_id, "reason": d.reason} if d.verdict != "skip" and d.skill_id: sk = lib.get(d.skill_id) diff --git a/tests/skills/test_evolve.py b/tests/skills/test_evolve.py index ad4189c..99f91a1 100644 --- a/tests/skills/test_evolve.py +++ b/tests/skills/test_evolve.py @@ -42,6 +42,21 @@ def fake_refine(group, library): log4 = U.evolve(t4, lib) assert log4["dropped_wrong"] == 1 and not log4["added"], log4 + # manifest: a run missing the gate verdict must fail loudly, never default to admitted + try: + U.traces_from_manifest({"template": "T", "runs": [{"dir": "/nonexistent"}]}) + raise AssertionError("missing 'admit' must raise") + except KeyError: + pass + + # slug: two long templates sharing a 48-char prefix must NOT collide on one skill id + long_a = "get the value of " + "x" * 60 + " variant one" + long_b = "get the value of " + "x" * 60 + " variant two" + assert U._slug(long_a) != U._slug(long_b), "truncated slugs must be disambiguated" + assert U._slug(long_a) == U._slug(long_a), "slug must stay deterministic" + assert U._slug("Get the top-n best-selling entity") == "get_the_top_n_best_selling_entity", \ + "short templates keep the plain readable slug" + print("test_evolve OK") diff --git a/tests/skills/test_retrieve_decide.py b/tests/skills/test_retrieve_decide.py index 4bf032a..a748ecd 100644 --- a/tests/skills/test_retrieve_decide.py +++ b/tests/skills/test_retrieve_decide.py @@ -33,6 +33,21 @@ def run(): d0 = decide("anything", []) assert isinstance(d0, Decision) and d0.verdict == "skip" and d0.skill_id is None + # skill_use.recommend: a decision pointing OUTSIDE the retrieved candidates (LLM + # hallucination — even an id that exists in the library) must downgrade to skip + import webwright.tools.skill_use as T + orig_retrieve, orig_decide = T.retrieve, T.decide + try: + T.retrieve = lambda task, lib: [Candidate(lib.get("bestsellers"), 0.9, "stub")] + T.decide = lambda task, cands: Decision("use", "reviews", "hallucinated: not a candidate") + r = T.recommend("top best-selling product", d) + assert r["verdict"] == "skip" and r["skill_id"] is None, r + T.decide = lambda task, cands: Decision("use", "bestsellers", "in candidates") + r2 = T.recommend("top best-selling product", d) + assert r2["verdict"] == "use" and r2["skill_id"] == "bestsellers", r2 + finally: + T.retrieve, T.decide = orig_retrieve, orig_decide + print("test_retrieve_decide OK") From d0c6d845fd04d5fddc3d00035ae0551a882171a1 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Fri, 3 Jul 2026 03:40:29 +0000 Subject: [PATCH 12/54] skills: manifest 'admit' must be a JSON boolean (hand-written "false" is truthy) Co-Authored-By: Claude Fable 5 --- src/webwright/skills/update.py | 6 +++++- tests/skills/test_evolve.py | 6 ++++++ 2 files changed, 11 insertions(+), 1 deletion(-) diff --git a/src/webwright/skills/update.py b/src/webwright/skills/update.py index cf8f722..7b39362 100644 --- a/src/webwright/skills/update.py +++ b/src/webwright/skills/update.py @@ -164,6 +164,10 @@ def traces_from_manifest(manifest: dict) -> list["Trace"]: for r in manifest.get("runs", []): if "admit" not in r: raise KeyError(f"manifest run missing required 'admit' (gate verdict): {r.get('dir', r)}") + if not isinstance(r["admit"], bool): + # a hand-written manifest with "admit": "false" would otherwise be truthy -> admitted + raise TypeError(f"manifest 'admit' must be a JSON boolean, " + f"got {type(r['admit']).__name__} {r['admit']!r}: {r.get('dir', r)}") d = Path(r["dir"]) fs = d / "final_script.py" code = fs.read_text(encoding="utf-8") if fs.exists() else "" @@ -174,7 +178,7 @@ def traces_from_manifest(manifest: dict) -> list["Trace"]: except Exception: pass out.append(Trace(template=template, code=code, answer=answer, - correct=bool(r["admit"]), + correct=r["admit"], verdict=r.get("verdict", "skip"), used_skill_id=r.get("used_skill_id"), meta={"params": r.get("params", {}), "site": r.get("site", ""), diff --git a/tests/skills/test_evolve.py b/tests/skills/test_evolve.py index 99f91a1..4f95221 100644 --- a/tests/skills/test_evolve.py +++ b/tests/skills/test_evolve.py @@ -48,6 +48,12 @@ def fake_refine(group, library): raise AssertionError("missing 'admit' must raise") except KeyError: pass + # ... and a hand-written string "false" (truthy!) must be rejected, not admitted + try: + U.traces_from_manifest({"template": "T", "runs": [{"dir": "/x", "admit": "false"}]}) + raise AssertionError("non-bool 'admit' must raise") + except TypeError: + pass # slug: two long templates sharing a 48-char prefix must NOT collide on one skill id long_a = "get the value of " + "x" * 60 + " variant one" From 6060bd5802997f9bf2270b6d73471e813a76d387 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Wed, 8 Jul 2026 00:32:31 +0000 Subject: [PATCH 13/54] skills: fix cleanroom-repro findings (silent empty-library skip, relative paths, missing answer file) Independent cleanroom reproduction (fresh clone + venv, README-only, public GitHub tasks) surfaced usability failures; mechanism itself reproduced end-to-end in 25 min. - with_skill_hint resolves the library path to ABSOLUTE (F2): the hint's command runs in the agent's workspace, where a relative ./library silently resolved to a nonexistent dir -> empty library -> every lookup skipped, no error, answer still right - skill_use.recommend: a missing/empty library now answers skip with an explicit 'warning: library empty at ' BEFORE Library() can mkdir the bogus path (F3) - README: step 1 now tells the agent to write agent_response.json (stock webwright does not produce it; the gate/manifest flow assumed it) with a copyable ANSWER_SPEC (F1); absolute-path + --library-beats-env notes (F2/F4); custom endpoint tip (F5) - skills/__init__ no longer eagerly imports update -> no more runpy RuntimeWarning on 'python -m webwright.skills.update' (F6); import evolve/Trace from the submodule - tests: hint abspath, empty/missing-library warning (incl. no-mkdir side effect) Co-Authored-By: Claude Fable 5 --- src/webwright/skills/README.md | 44 +++++++++++++++++++++------- src/webwright/skills/__init__.py | 8 +++-- src/webwright/skills/prompt.py | 9 +++++- src/webwright/tools/skill_use.py | 11 ++++++- tests/skills/test_retrieve_decide.py | 16 ++++++++++ 5 files changed, 74 insertions(+), 14 deletions(-) diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md index f7d054a..e267907 100644 --- a/src/webwright/skills/README.md +++ b/src/webwright/skills/README.md @@ -49,16 +49,26 @@ differs between instances becomes the skill's parameters — more instances, wid ### 1. Solve a few instances of a template (normal Webwright runs) +**Important:** stock Webwright does NOT write the answer to a machine-readable file by itself — +tell the agent to, by appending an output instruction to the task (the gate in step 2 reads it): + ```bash +ANSWER_SPEC='Additionally, write the final answer into $WORKSPACE_DIR/agent_response.json +as {"retrieved_data": }.' + python -m webwright.run.cli main \ - -t "How many commits did kilian make to a11yproject on 3/1/2023?" \ + -t "How many commits did kilian make to a11yproject on 3/1/2023? $ANSWER_SPEC" \ --task-id t132_a --start-url http://gitlab.example.com -o outputs \ -c base.yaml -c model_openai.yaml ``` -Each run leaves a directory containing `final_script.py` (the executable solve) and -`agent_response.json` (the answer). Repeat for 2–3 more instances of the same template with -different values (another user / repo / date). +> Custom OpenAI-compatible gateway? Copy `model_openai.yaml`, change `openai_endpoint` +> (and `model_name`), and stack your copy instead. + +Each run leaves a directory containing `final_script.py` (the executable solve) and — because of +the instruction above — `agent_response.json` (the answer). Repeat for 2–3 more instances of the +same template with different values (another user / repo / date). If you skip the output +instruction, fill each manifest run's `answer` field by hand in step 2 instead. ### 2. Gate the solves, write the manifest @@ -119,11 +129,11 @@ left untouched. Batches may mix templates. ```python from webwright.skills import with_skill_hint -prompt = with_skill_hint(prompt, task=task_text, library="./library") +prompt = with_skill_hint(prompt, task=task_text, library="/abs/path/to/library") ``` ```bash -SKILL_LIBRARY_ROOT=./library python -m webwright.run.cli main -t "$prompt" ... +python -m webwright.run.cli main -t "$prompt" ... ``` The hint tells the agent to query the library first; the agent runs the `skill_use` tool, gets @@ -131,6 +141,16 @@ The hint tells the agent to query the library first; the agent runs the `skill_u (use = as-is with new parameter values, adapt = reuse the core + change the last step, skip = solve from scratch). +Two path gotchas, both loud now but worth knowing: + +- **The library path ends up in a command that runs inside the agent's workspace** — + `with_skill_hint` resolves it to an absolute path for exactly that reason. If the tool is ever + pointed at a missing/empty library anyway, it answers `skip` with an explicit + `"warning": "library empty at "` instead of failing silently. +- **Precedence:** the hint bakes `--library` into the command, and `--library` beats the + `SKILL_LIBRARY_ROOT` env var (the env var is only the tool's default when `--library` is + omitted). Use one or the other, not both. + ### 5. Run a skill directly (optional) Every skill is also a standalone script: it reads a `taskspec.json` (parameters at run time) and @@ -164,8 +184,11 @@ Steps 1–3 driven by a single task file. `tasks.json` — one entry per instanc START_URL=http://gitlab.example.com # 1) solve every instance (sequential; add xargs -P N or & to parallelize) +# ANSWER_SPEC (from step 1 above) makes the agent write agent_response.json — the gate reads it +ANSWER_SPEC='Additionally, write the final answer into $WORKSPACE_DIR/agent_response.json +as {"retrieved_data": }.' jq -c '.[]' tasks.json | while read -r row; do - python -m webwright.run.cli main -t "$(jq -r .task <<<"$row")" \ + python -m webwright.run.cli main -t "$(jq -r .task <<<"$row") $ANSWER_SPEC" \ --task-id "$(jq -r .id <<<"$row")" --start-url "$START_URL" -o outputs \ -c base.yaml -c model_openai.yaml done @@ -191,12 +214,13 @@ PY # 3) evolve the library python -m webwright.skills.update --manifest batch.json --library ./library -# 4) solve NEW instances of the template WITH the library: prepend the skill hint to the -# prompt (SKILL_LIBRARY_ROOT alone is not enough — the hint is what tells the agent to query) +# 4) solve NEW instances of the template WITH the library: prepend the skill hint +# (the hint is what tells the agent to query; with_skill_hint resolves ./library +# to an absolute path against YOUR cwd, so the agent finds it from its workspace) TASK="How many commits did byte make to empathy-prompts on 4/2/2023?" PROMPT=$(python -c 'import sys; from webwright.skills import with_skill_hint print(with_skill_hint(sys.argv[1], task=sys.argv[1], library="./library"))' "$TASK") -SKILL_LIBRARY_ROOT=./library python -m webwright.run.cli main -t "$PROMPT" \ +python -m webwright.run.cli main -t "$PROMPT" \ --task-id t132_new --start-url "$START_URL" -o outputs -c base.yaml -c model_openai.yaml ``` diff --git a/src/webwright/skills/__init__.py b/src/webwright/skills/__init__.py index 79a0bf3..ae983f2 100644 --- a/src/webwright/skills/__init__.py +++ b/src/webwright/skills/__init__.py @@ -12,11 +12,15 @@ from .retrieve import retrieve, Candidate from .decide import decide, Decision from .gate import gate, GateResult -from .update import evolve, Trace from .llm import configure_llm from .prompt import with_skill_hint +# NOTE: `update` (evolve / Trace) is deliberately NOT imported here. It is the module run as a +# CLI (`python -m webwright.skills.update`); importing it eagerly makes runpy print a +# "found in sys.modules" RuntimeWarning on every CLI invocation. Import it directly: +# from webwright.skills.update import evolve, Trace + __all__ = [ "Library", "Skill", "retrieve", "Candidate", "decide", "Decision", - "gate", "GateResult", "evolve", "Trace", "configure_llm", "with_skill_hint", + "gate", "GateResult", "configure_llm", "with_skill_hint", ] diff --git a/src/webwright/skills/prompt.py b/src/webwright/skills/prompt.py index 5497b1c..8f5a287 100644 --- a/src/webwright/skills/prompt.py +++ b/src/webwright/skills/prompt.py @@ -5,6 +5,8 @@ """ from __future__ import annotations +from pathlib import Path + _HINT = """## Skill library (reuse before solving from scratch) A library of previously-built executable code skills may contain one that helps this task. BEFORE planning from scratch, query it ONCE from bash: @@ -22,5 +24,10 @@ def with_skill_hint(task_prompt: str, *, task: str, library: str) -> str: - """Prepend the skill-library hint to a task prompt.""" + """Prepend the skill-library hint to a task prompt. + + `library` is resolved to an ABSOLUTE path: the hint's command runs in the agent's + own workspace, so a relative path would silently point at a nonexistent (empty) + library there and every lookup would come back "skip".""" + library = str(Path(library).resolve()) return _HINT.format(task=task.replace('"', "'"), library=library) + task_prompt diff --git a/src/webwright/tools/skill_use.py b/src/webwright/tools/skill_use.py index 09e2a1b..d14aec7 100644 --- a/src/webwright/tools/skill_use.py +++ b/src/webwright/tools/skill_use.py @@ -16,6 +16,7 @@ import json import os import sys +from pathlib import Path from webwright.skills.library import Library from webwright.skills.retrieve import retrieve @@ -23,7 +24,15 @@ def recommend(task: str, library_root: str) -> dict: - lib = Library(library_root) + root = Path(library_root).resolve() + # A missing/empty library is almost always a wrong path (relative paths resolve inside the + # agent's workspace). Say so LOUDLY instead of a silent skip — checked before Library(), + # whose constructor would mkdir the bogus path and hide the mistake. + if not root.is_dir() or not any((p / "meta.json").exists() for p in root.iterdir() if p.is_dir()): + return {"verdict": "skip", "skill_id": None, + "reason": f"skill library MISSING or EMPTY at {root} — check the --library path", + "warning": f"library empty at {root}"} + lib = Library(root) cands = retrieve(task, lib) if not cands: return {"verdict": "skip", "skill_id": None, "reason": "library has no relevant skill"} diff --git a/tests/skills/test_retrieve_decide.py b/tests/skills/test_retrieve_decide.py index a748ecd..826f117 100644 --- a/tests/skills/test_retrieve_decide.py +++ b/tests/skills/test_retrieve_decide.py @@ -48,6 +48,22 @@ def run(): finally: T.retrieve, T.decide = orig_retrieve, orig_decide + # with_skill_hint must bake an ABSOLUTE library path into the hint (the command runs in + # the agent's workspace, where a relative path would point at nothing) + from webwright.skills.prompt import with_skill_hint + hinted = with_skill_hint("solve it", task="t", library="./some_rel_lib") + import os + assert f'--library "{os.path.abspath("./some_rel_lib")}"' in hinted, hinted[:300] + + # recommend on a MISSING or EMPTY library -> loud skip with a warning (and no mkdir side effect) + import webwright.tools.skill_use as T2 + r = T2.recommend("anything", "/nonexistent/skill/lib/path") + assert r["verdict"] == "skip" and "warning" in r and "empty" in r["warning"], r + assert not os.path.exists("/nonexistent/skill/lib/path"), "must not mkdir a bogus path" + with tempfile.TemporaryDirectory() as d2: + r2 = T2.recommend("anything", d2) # exists but has no skills + assert r2["verdict"] == "skip" and "warning" in r2, r2 + print("test_retrieve_decide OK") From 4682a470adc4b587aa1974721d4cb5fc7e3b101b Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Wed, 8 Jul 2026 04:58:41 +0000 Subject: [PATCH 14/54] =?UTF-8?q?skills:=20add=20examples/=20=E2=80=94=20a?= =?UTF-8?q?=20real=20evolve-produced=20skill,=20measured=20savings,=20fill?= =?UTF-8?q?ed-in=20inputs?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - example_library/: the commit-counting skill verbatim as evolve wrote it (runnable standalone via taskspec, no LLM in the loop; functionally verified against a local repo) - README: what a skill looks like (catalog card + the distilled git-log algorithm), measured held-out numbers (33->10 steps; wrong->correct rescues; honest note that reuse costs more than it saves on cheap tasks), three try-it paths - honest coverage-boundary demo: an unseen period shape raises cleanly; on the real held-out run the agent read the source and adapted around it - tasks/batch/taskspec example JSONs matching the how-to-use steps - links from the module README Co-Authored-By: Claude Fable 5 --- src/webwright/skills/README.md | 4 + src/webwright/skills/examples/README.md | 106 +++++++++++++++ .../skills/examples/batch.example.json | 56 ++++++++ .../meta.json | 21 +++ .../skill.py | 126 ++++++++++++++++++ .../skills/examples/tasks.example.json | 35 +++++ .../skills/examples/taskspec.example.json | 14 ++ 7 files changed, 362 insertions(+) create mode 100644 src/webwright/skills/examples/README.md create mode 100644 src/webwright/skills/examples/batch.example.json create mode 100644 src/webwright/skills/examples/example_library/how_many_commits_did_user_make_period_in_the_cur/meta.json create mode 100644 src/webwright/skills/examples/example_library/how_many_commits_did_user_make_period_in_the_cur/skill.py create mode 100644 src/webwright/skills/examples/tasks.example.json create mode 100644 src/webwright/skills/examples/taskspec.example.json diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md index e267907..d5879f4 100644 --- a/src/webwright/skills/README.md +++ b/src/webwright/skills/README.md @@ -38,6 +38,10 @@ Two touch points, **no change to the agent loop or default config**: ``` Manifest schema and a full walkthrough: see **How to use** below. +> **Want to see it before reading?** `examples/` ships a real skill exactly as `evolve` wrote it +> (runnable standalone, no LLM), measured step-saving numbers, and filled-in example inputs for +> every file this guide asks you to write. See [`examples/README.md`](examples/README.md). + ## How to use (end-to-end) The library grows **offline** from batches of solved tasks, and is consumed **at solve time** by diff --git a/src/webwright/skills/examples/README.md b/src/webwright/skills/examples/README.md new file mode 100644 index 0000000..b5c97c6 --- /dev/null +++ b/src/webwright/skills/examples/README.md @@ -0,0 +1,106 @@ +# Examples — what a skill library looks like, and what reuse buys you + +Everything here is real output of the pipeline (produced by `update.evolve` during a WebArena +evaluation), lightly curated. Nothing is hand-written. + +``` +examples/ +├── example_library/ # a library with one skill, exactly as evolve wrote it +│ └── how_many_commits_did_user_make_period_in_the_cur/ +│ ├── skill.py # the executable skill (126 lines) +│ └── meta.json # the catalog card retrieval reads +├── taskspec.example.json # input for running the skill directly +├── tasks.example.json # input for the batch pipeline (README step 6) +└── batch.example.json # a filled manifest (README step 2) +``` + +## What a skill looks like + +One template = one skill = one self-contained directory. The `meta.json` is the catalog card +(what `retrieve` sees); the `skill.py` is what the agent reads and reuses: + +```json +{ + "template": "How many commits did {{user}} make {{period}} in the current repository?", + "site": "gitlab", + "signature": { "params": ["user", "period"], "call": "python skill.py taskspec.json" }, + "n_solves": 2 +} +``` + +The interesting part: the two train solves this was distilled from solved the task **through the +web UI** (opening commit lists, filtering, paging). What `evolve` settled into the library is a +**better algorithm** the solves discovered — clone the repo and count with git directly: + +```python +def count_commits_with_git(repo_dir, author, start_dt, end_dt, branch="main"): + cmd = ["git", "-C", str(repo_dir), "log", branch, + "--since", start_dt.strftime("%Y-%m-%d %H:%M:%S"), + "--until", end_dt.strftime("%Y-%m-%d %H:%M:%S"), + "--author", author, "--pretty=format:%H"] +``` + +`user` and `period` are function parameters. `period` accepts the two shapes observed across the +train instances (full month names): `"on January 5th 2023"` and +`"between start of February 2023 and end of May 2023"`. + +**Where the skill's coverage ends — visibly.** Feed it a period shape it never saw (say +`"in Jan 2023"`) and it raises `Unsupported period format` cleanly instead of guessing. This is +the parameter-coverage boundary the eval doc discusses: on the real held-out run with exactly +that unseen shape, the *agent* read this source, kept the git-clone core, and inlined the dates +itself — reuse degrades to adaptation, not to a wrong answer. + +## What reuse buys you (measured, WebArena held-out tasks) + +Held-out = unseen instances of the template; WITH = agent may query the library; BASE = from +scratch. Same model, same budget. + +| held-out task (template) | BASE (scratch) | WITH library | +|----------------------------------------|--------------------|-------------------| +| commits by {user} in {period} (gitlab) | correct, 33 steps | correct, **10 steps** | +| commits by {user} in {period} (gitlab) | correct, 19 steps | correct, **10 steps** | +| walking+driving route times (map) | **wrong**, 21 steps| **correct**, 8 steps | +| walking+driving route times (map) | **wrong**, 19 steps| **correct**, 10 steps | +| all reviews with <=3 stars (shopping) | **wrong**, 31 steps| **correct**, 13 steps | + +Aggregate over 10 templates x 3 sites (20 held-out instances): **70% vs 55% accuracy, 14.7 vs +17.1 steps**. Two kinds of wins: tasks that get *cheaper* (33 -> 10) and tasks that get +*solvable* (wrong -> correct). Honest counterpoint: on tasks the agent already solves in a few +steps, querying the library costs more than it saves — reuse pays on expensive-to-explore tasks. + +## Try it + +**1. Run the skill directly — no LLM, no agent needed:** + +```bash +cd src/webwright/skills/examples +cat > taskspec.json <<'EOF' +{"params": {"user": "Jane Doe", "period": "on January 5th 2023"}, + "start_url": "https://gitlab.com//", + "credentials": null, + "output_schema": {"type": "array", "items": {"type": "number"}}} +EOF +python example_library/how_many_commits_did_user_make_period_in_the_cur/skill.py taskspec.json +cat agent_response.json # {"task_type": "RETRIEVE", "status": "SUCCESS", "retrieved_data": [N], ...} +``` + +Point it at any repo you can clone; fill any author/period. This is the whole point of a *code* +skill: it runs without a model in the loop. + +**2. Ask the tool whether the library helps a task (one LLM call pair):** + +```bash +export OPENAI_API_KEY=... +python -m webwright.tools.skill_use \ + --task "How many commits did Alice make in March 2024 in the current repository?" \ + --library "$PWD/example_library" +# -> {"verdict": "use", "skill_id": "how_many_commits_...", "source_path": "...", ...} +``` + +**3. Full agent reuse:** follow "How to use" in the module README, pointing `with_skill_hint` +at `examples/example_library`. + +## Example inputs for the batch pipeline + +`tasks.example.json` and `batch.example.json` are filled-in versions of the files the module +README's steps 2 and 6 ask you to write — copy, replace values, go. diff --git a/src/webwright/skills/examples/batch.example.json b/src/webwright/skills/examples/batch.example.json new file mode 100644 index 0000000..f42f22e --- /dev/null +++ b/src/webwright/skills/examples/batch.example.json @@ -0,0 +1,56 @@ +{ + "template": "How many commits did {{user}} make {{period}} in the current repository?", + "runs": [ + { + "dir": "outputs/commits_a_20260707_120000", + "admit": true, + "params": { + "user": "Jane Doe", + "period": "on January 5th 2023" + }, + "verdict": "skip", + "site": "gitlab", + "output_schema": { + "type": "array", + "items": { + "type": "number" + } + } + }, + { + "dir": "outputs/commits_b_20260707_121500", + "admit": true, + "params": { + "user": "John Smith", + "period": "on April 7th 2022" + }, + "verdict": "skip", + "site": "gitlab", + "output_schema": { + "type": "array", + "items": { + "type": "number" + } + }, + "answer": [ + 2 + ] + }, + { + "dir": "outputs/commits_c_20260707_123000", + "admit": false, + "params": { + "user": "Alex Lee", + "period": "between start of February 2023 and end of May 2023" + }, + "verdict": "skip", + "site": "gitlab", + "output_schema": { + "type": "array", + "items": { + "type": "number" + } + } + } + ] +} diff --git a/src/webwright/skills/examples/example_library/how_many_commits_did_user_make_period_in_the_cur/meta.json b/src/webwright/skills/examples/example_library/how_many_commits_did_user_make_period_in_the_cur/meta.json new file mode 100644 index 0000000..3097d82 --- /dev/null +++ b/src/webwright/skills/examples/example_library/how_many_commits_did_user_make_period_in_the_cur/meta.json @@ -0,0 +1,21 @@ +{ + "template": "How many commits did {{user}} make {{period}} in the current repository?", + "provenance": "update-refined", + "site": "gitlab", + "summary": "Refined from 2 gate-passed solves; parameterized + primitives.", + "signature": { + "params": [ + "user", + "period" + ], + "call": "python skill.py taskspec.json" + }, + "output_schema": { + "type": "array", + "items": { + "type": "number" + } + }, + "n_solves": 2, + "revisions": 1 +} \ No newline at end of file diff --git a/src/webwright/skills/examples/example_library/how_many_commits_did_user_make_period_in_the_cur/skill.py b/src/webwright/skills/examples/example_library/how_many_commits_did_user_make_period_in_the_cur/skill.py new file mode 100644 index 0000000..ff01f6d --- /dev/null +++ b/src/webwright/skills/examples/example_library/how_many_commits_did_user_make_period_in_the_cur/skill.py @@ -0,0 +1,126 @@ +import sys +import json +import re +import subprocess +import tempfile +import shutil +from pathlib import Path +from datetime import datetime, timedelta, timezone + + +def load_taskspec(path: str) -> dict: + return json.loads(Path(path).read_text(encoding="utf-8")) + + +def write_agent_response(retrieved_data, status="SUCCESS", error_details=None): + response = { + "task_type": "RETRIEVE", + "status": status, + "retrieved_data": retrieved_data, + "error_details": error_details, + } + Path("agent_response.json").write_text( + json.dumps(response, ensure_ascii=False), encoding="utf-8" + ) + + +def start_url_to_git_url(start_url: str) -> str: + return start_url[:-1] + ".git" if start_url.endswith("/") else start_url + ".git" + + +def infer_branch_from_start_url(start_url: str) -> str: + return "main" + + +def parse_period_to_range(period_text: str): + text = period_text.strip() + + m = re.fullmatch(r"on ([A-Za-z]+) (\d+)(?:st|nd|rd|th)? (\d{4})", text, re.I) + if m: + month_name, day, year = m.groups() + start = datetime.strptime(f"{year} {month_name} {day}", "%Y %B %d").replace(tzinfo=timezone.utc) + end = start + timedelta(days=1) - timedelta(seconds=1) + return start, end + + m = re.fullmatch( + r"between start of ([A-Za-z]{3,9}) (\d{4}) and end of ([A-Za-z]{3,9}) (\d{4})", + text, + re.I, + ) + if m: + smonth, syear, emonth, eyear = m.groups() + start = datetime.strptime(f"{syear} {smonth} 1", "%Y %B %d").replace(tzinfo=timezone.utc) + if len(emonth) == 3: + em = datetime.strptime(emonth, "%b").month + else: + em = datetime.strptime(emonth, "%B").month + ey = int(eyear) + if em == 12: + next_month = datetime(ey + 1, 1, 1, tzinfo=timezone.utc) + else: + next_month = datetime(ey, em + 1, 1, tzinfo=timezone.utc) + end = next_month - timedelta(seconds=1) + return start, end + + raise ValueError(f"Unsupported period format: {period_text}") + + +def clone_repository(repo_url: str, target_dir: Path): + subprocess.run( + ["git", "clone", "--depth", "1000", repo_url, str(target_dir)], + check=True, + stdout=subprocess.PIPE, + stderr=subprocess.PIPE, + text=True, + ) + + +def count_commits_with_git(repo_dir: Path, author: str, start_dt: datetime, end_dt: datetime, branch: str = "main") -> int: + cmd = [ + "git", + "-C", + str(repo_dir), + "log", + branch, + "--since", + start_dt.strftime("%Y-%m-%d %H:%M:%S"), + "--until", + end_dt.strftime("%Y-%m-%d %H:%M:%S"), + "--author", + author, + "--pretty=format:%H", + ] + out = subprocess.check_output(cmd, text=True) + lines = [ln for ln in out.splitlines() if ln.strip()] + return len(lines) + + +def retrieve_commit_count(taskspec: dict): + params = taskspec.get("params", {}) + user = params["user"] + period = params["period"] + start_url = taskspec["start_url"] + + start_dt, end_dt = parse_period_to_range(period) + repo_url = start_url_to_git_url(start_url) + branch = infer_branch_from_start_url(start_url) + + with tempfile.TemporaryDirectory() as tmp: + repo_dir = Path(tmp) / "repo" + clone_repository(repo_url, repo_dir) + count = count_commits_with_git(repo_dir, user, start_dt, end_dt, branch) + + return [count] + + +def main(): + try: + taskspec = load_taskspec(sys.argv[1]) + retrieved_data = retrieve_commit_count(taskspec) + write_agent_response(retrieved_data) + except Exception as e: + write_agent_response([], status="ERROR", error_details=f"{type(e).__name__}: {e}") + + +if __name__ == "__main__": + main() diff --git a/src/webwright/skills/examples/tasks.example.json b/src/webwright/skills/examples/tasks.example.json new file mode 100644 index 0000000..898417a --- /dev/null +++ b/src/webwright/skills/examples/tasks.example.json @@ -0,0 +1,35 @@ +[ + { + "id": "commits_a", + "task": "How many commits did Jane Doe make on January 5th 2023 in the current repository?", + "params": { + "user": "Jane Doe", + "period": "on January 5th 2023" + }, + "gold": [ + 1 + ] + }, + { + "id": "commits_b", + "task": "How many commits did John Smith make on April 7th 2022 in the current repository?", + "params": { + "user": "John Smith", + "period": "on April 7th 2022" + }, + "gold": [ + 2 + ] + }, + { + "id": "commits_c", + "task": "How many commits did Alex Lee make between start of February 2023 and end of May 2023 in the current repository?", + "params": { + "user": "Alex Lee", + "period": "between start of February 2023 and end of May 2023" + }, + "gold": [ + 7 + ] + } +] diff --git a/src/webwright/skills/examples/taskspec.example.json b/src/webwright/skills/examples/taskspec.example.json new file mode 100644 index 0000000..a936ac7 --- /dev/null +++ b/src/webwright/skills/examples/taskspec.example.json @@ -0,0 +1,14 @@ +{ + "params": { + "user": "Jane Doe", + "period": "on January 5th 2023" + }, + "start_url": "https://gitlab.com//", + "credentials": null, + "output_schema": { + "type": "array", + "items": { + "type": "number" + } + } +} From 1234788a599905deb9f615847113b94781a48924 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Wed, 8 Jul 2026 09:48:05 +0000 Subject: [PATCH 15/54] skills README: narrative intro (programs not notes, gate, aggregation, safe growth, measured results) + data-flow/interfaces diagram --- src/webwright/skills/README.md | 56 +++++--- src/webwright/skills/pipeline_diagram.png | Bin 0 -> 273389 bytes src/webwright/skills/pipeline_diagram.svg | 163 ++++++++++++++++++++++ 3 files changed, 203 insertions(+), 16 deletions(-) create mode 100644 src/webwright/skills/pipeline_diagram.png create mode 100644 src/webwright/skills/pipeline_diagram.svg diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md index d5879f4..ffafc4d 100644 --- a/src/webwright/skills/README.md +++ b/src/webwright/skills/README.md @@ -1,21 +1,45 @@ # `webwright.skills` — a memory / skill-library module for Webwright -Turn solved tasks into **reusable, executable code skills**, retrieve and judge them at solve -time, gate what enters the library, and grow the library incrementally. A self-evolving loop: - -``` -solve --(gate: gold | self_verify)--> admit --(evolve: refine + parameterize + primitives)--> library - ^ | - | skill_use tool: retrieve + decide (use / adapt / skip) <--------------------------------------- + -``` - -This is the missing **reuse + accumulation** layer: it consumes the `final_script.py` every -Webwright solve already produces (plain or crafted mode — both work), accumulates skills across -tasks, judges when a prior skill applies, and improves skills as more solves arrive — with a gate -so wrong solves don't pollute the library. It complements `crafted_cli`: where `crafted_cli` -parameterizes a single task's script by anticipating what might vary, `update.refine` -parameterizes **across multiple verified solves** — the differences actually observed between -instances become the parameters. +**Most "agent skills" are notes the model reads. Ours are programs.** + +With webwright.skills, each solved task becomes runnable, parameterized code — you can verify +it, run it without the model, and import it into the next task instead of re-exploring. + +![data flow & interfaces](pipeline_diagram.png) + +**Why webwright makes this natural.** Webwright is a terminal/code-native agent: its actions +are code, and every solve already leaves behind a working script. The agent's exhaust is +already code — turning it into skills is a byproduct, not an extra instrumentation layer. +It complements `crafted_cli`: where `crafted_cli` parameterizes a single task's script by +anticipating what might vary, `update.refine` parameterizes **across multiple verified +solves** — the differences actually observed between instances become the parameters. + +**Only verified solves get in.** An admission gate checks every solution before it enters the +library, so wrong answers never pollute what the agent reuses. + +**One solve isn't a skill yet.** A single task's script is correct but narrow — it solves +*that* instance. So the update step aggregates multiple verified solves of the same task +template: values that differ across instances become parameters, patterns that recur become +shared primitives. The merged skill is often better than any single solve it came from — +different runs' strategies become fallbacks, and what settles into the library isn't a +click-path but the best algorithm the solves discovered (our commit-counting skill distilled +UI exploration into `git clone + git log`). + +**The library keeps growing — safely.** A self-evolving loop feeds later solves back in: new +templates add skills, new solves refine existing ones in place (working functions kept), and +skills with nothing new stay byte-identical. Growth never breaks what already works. + +**What you get, measured** (WebArena, 10 templates × 3 sites, held-out instances): + +- **Tasks that were unsolvable become solvable** — from-scratch failed, with the library + solved (4 rescues out of 20). +- **Tasks that were expensive become cheap** — 33 steps → 10 on repeat task types; overall + 70% vs 55% accuracy, ~2.4 fewer steps. + +**Cheap to adopt, honest about costs.** Integration is purely additive — one tool plus one +CLI, no agent-loop changes. Building the library is not free, but close: it recycles solves +you already ran (only the gate-passed ones), plus one distillation LLM call per template +batch. Independently reproduced from a fresh clone in ~25 minutes. ## How it plugs into Webwright diff --git a/src/webwright/skills/pipeline_diagram.png b/src/webwright/skills/pipeline_diagram.png new file mode 100644 index 0000000000000000000000000000000000000000..6c02cbfcad8b3e4df2c752171ca8c8e3ca3043a4 GIT binary patch literal 273389 zcmdqJWmuG7*awIrA|NOo64DLQ4N7-+3P^V|q$1s2Lr6D6cPJ?hLwCmv-8H}t{@-`^ z+g{gx+ui5O6Q|F)&mF(}K2Mmkq7*tR5h?-#0=kT}xGDkyaxnq|V)UDr@GtD|7H1F; z-Xq9}e^K|$I9Nv3$KHKHK9QnE>847;Bn+szMGK(&B~gq!6|Py33r1xrozbb+*tg7d zo6)RroBE;o2T#jMRHI5WpMkCh_TwYn8wxq>SM1799w9toLCCZLu&5 zlw7>G%1cC5y@I7)W3^K0~Sycd1c67fXN=20$+i zOyLf5rYM676)Eu?|Iyq+$1V&SHTYi418;zn_EpC};`)Vh1F?u%#bLx)*X8xT;^dkz z9asrR-G~XI+De!yIvEC|_#r!^aB`?_cx@)H*plmTliFk@KMGp` zn!#@uy$1i)Iw*HCQ&t7b5A&}2%p6t*#yyeV_#h%6{1*6OIz71^B@(%6?G%+^5cTHsW++|wadXSklemk% zil(Y)@&-b?@M4Sui7CAiz*uoAtjcsEXqgvO$2S#aw-hT!<^g7(tZ}J9?Fq~?wK6t0v)bhLxgdB~+jp(nrxd~K`f+Qn zH?ZddE7xLfWV8c8rHsV_aQE+i@2Iz86Xm;SW=vj&M9iAJ@Gz2IP1$f10heobmnY0SO<`3{6r2 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z_h@<#;!rPa{`FBTxy=_#zshX$mW7x8b|3T7?~8Z7iJvmj{2nJTr%j`mc2!oaHt6*#5n;tN=T$Brm$CblLkA-rWTFvn`kiMQk4n`P1?=yj=A?(k4k+l=>t6 zV=sH%N7Ck#C;VeeggPAun^4{#wY<@G9V+#p{Y_1FV_x1Cj#+{Jn$OXEOaVSwB7wA5 z4QN9~F;|%A-AJhy^Iuj7-Vrq@QZU@RCD!;wzk^pZ`4?vb8r4Rxc=HKnAR#b50E|$0 zqso?!v@^Kmj#-$ts5aDCG{L1zhvHoe0R^Q7)PJrW!jMcD^!EWR1?KwoN& z$Kh06_=-q8@#(DTF=P~AbRWLc$Ia;M)K@a(z_AkT_xAwyZdQg`7N}7FVTTAGL1wa- zi$I(!3hGuT_DZ^n{zd6u(L-VL7~U^^mie8bOVNW$fFv=1TF+iQ`JVN!3&@Qz7tZM4p;ukYV-LmuCBqjQ9t^Pf;mO$a*o2sw9jW4*{X28M1thPOz*n1O#(X7S<$ zSXjh+$UeU?=5-6xj@|Dvhd!@gO3gmQzOy{lyVNAfV#WYq?pB4v*P%;wY4}tzD$Kb@ zNq+AejrIr{C6)#d4`6_09R*4D@9g{LIk#Y+NE)S$cW`ATHvn{1!p|_^)tr!A#6IJO z9-uqVbiRQYRFNFK*s`{9h^saK3Fbt3#Q*j8|2RATC+mNop8g-qi2r|7 qx%~tu`P0*-w96U%2P$G!@&XAeWk|nww04T%hKz)wc-a@jpZ^1CM#Gf= literal 0 HcmV?d00001 diff --git a/src/webwright/skills/pipeline_diagram.svg b/src/webwright/skills/pipeline_diagram.svg new file mode 100644 index 0000000..b7b59e8 --- /dev/null +++ b/src/webwright/skills/pipeline_diagram.svg @@ -0,0 +1,163 @@ + + + + + + + + + + + + webwright.skills — data flow & interfaces + + + + + + SOLVE TIME · agent + LIBRARY · disk + UPDATE TIME · offline batch + + + + task + start_url + with_skill_hint(prompt, task, library) → reuse instructions + + + + + webwright agent (bash loop — unchanged) + + + $ skill_use --task "…" --library ROOT + + + + skill_use (one tool call; any error → skip) + + + retrieve + in : catalog (meta only) + out: ≤3 Candidate{id,score} + + + + + decide + out: Decision{verdict: + use|adapt|skip, skill_id} + + + guards + hallucinated skill_id → skip · empty library → skip + warning + + + + + tool output → agent observation (stdout JSON) + {verdict, skill_id, source_path, call, + how_to_reuse: "copy + fill params / reuse core"} + + + reads source, copies / adapts + + + final_script.py (skill code, THIS task's params) + run → answer + evidence screenshots + + + + + run artifacts + agent_response.json {task_type, status, retrieved_data} + skill_decision.json {skill_id, verdict, reason} · trajectory + + + + library/<template-slug>/ × N skills + + + meta.json — the catalog card + {template, site, summary, + signature:{params, call}, + output_schema, n_solves, revisions} + ← only THIS enters the retrieve prompt + + + skill.py — executable, standalone + primitives: login() · open_…() · extract_…() + task layer: solve(taskspec) + ← read as SOURCE at solve time; + runs anywhere via the CLI below + + + standalone interface (no model needed) + $ python skill.py taskspec.json + taskspec {params, start_url, credentials, output_schema} + writes → agent_response.json {retrieved_data} + + + + + + + + gate(result, gold?, output_schema?, method) + method: gold | self_verify | none → GateResult{admit} + admit=false ⇒ the solve NEVER enters the library + + + + + batch.json — the manifest + {template, ← skills are keyed by this string + runs:[{dir, admit(bool, REQUIRED), params, + verdict:skip|use|adapt, output_schema, answer?}]} + answer omitted → read dir/agent_response.json + + + → [Trace] + + + + evolve(traces, library) — per template group: + + + ① template not in library → ADD + _refine(N solves) → parameters + primitives → new skill + + + ② covered + verdict has adapt → REFINE in place + LLM in : EXISTING skill.py + new solves (code+params) + contract: keep working functions, only widen / fix + meta : n_solves += N, revisions += 1 + + + ③ covered + all use-success → KEEP (byte-identical) + + changelog {added, adapt_refined, use, dropped_wrong} + + + + library v_n → next batch solves WITH it → v_n+1 + grown, never rebuilt — untouched skills stay byte-identical + + + + + agent_response.json (+ gold on benchmarks) → gate + + + + write skill.py + meta.json + From 3612e89389cc3c80d881498f425754237b545c43 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Wed, 8 Jul 2026 19:39:53 +0000 Subject: [PATCH 16/54] =?UTF-8?q?skills:=20friendly=20entry=20=E2=80=94=20?= =?UTF-8?q?'learn'=20turns=20a=20folder=20of=20runs=20into=20skills=20(no?= =?UTF-8?q?=20manifest)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit python -m webwright.skills learn --library ./library - auto: reads task.json/agent_response.json per run, gates (gold via --golds, else self_verify), groups tasks into templates + extracts params with one LLM call per chunk (default 25; existing templates passed in so chunks refine instead of duplicating), infers output_schema from the answer shape, site from start_url - idempotent: processed runs remembered in library/.learned.json; --dry-run plan mode - README: Quickstart (two commands) + use cases up top; old walkthrough demoted to 'Manual mode'; examples/solve_with_library.sh wrapper (hint + answer instruction) - unit tests for the LLM-free plumbing --- src/webwright/skills/README.md | 32 +++- src/webwright/skills/__main__.py | 17 ++ .../skills/examples/solve_with_library.sh | 9 + src/webwright/skills/learn.py | 168 ++++++++++++++++++ tests/skills/test_learn.py | 36 ++++ 5 files changed, 260 insertions(+), 2 deletions(-) create mode 100644 src/webwright/skills/__main__.py create mode 100755 src/webwright/skills/examples/solve_with_library.sh create mode 100644 src/webwright/skills/learn.py create mode 100644 tests/skills/test_learn.py diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md index ffafc4d..5daab1b 100644 --- a/src/webwright/skills/README.md +++ b/src/webwright/skills/README.md @@ -60,13 +60,41 @@ Two touch points, **no change to the agent loop or default config**: ```bash python -m webwright.skills.update --manifest batch.json --library ./library ``` - Manifest schema and a full walkthrough: see **How to use** below. + Friendly path: see **Quickstart**. Manifest schema and full control: **Manual mode** below. > **Want to see it before reading?** `examples/` ships a real skill exactly as `evolve` wrote it > (runnable standalone, no LLM), measured step-saving numbers, and filled-in example inputs for > every file this guide asks you to write. See [`examples/README.md`](examples/README.md). -## How to use (end-to-end) +## Quickstart — the two-command version + +```bash +# 1. solve tasks with the library in the loop (wrapper = hint + answer-output instruction) +examples/solve_with_library.sh "How many commits did Jane make in Jan 2023?" \ + http://gitlab.example.com /abs/path/library -o outputs -c base.yaml -c model_openai.yaml + +# 2. turn everything you've solved into skills — no manifest, no fields to learn +export OPENAI_API_KEY=... +python -m webwright.skills learn outputs/ --library ./library +``` + +`learn` scans the run folders, gates each solve (gold if you pass `--golds golds.json`, +else a shape check), auto-groups tasks into templates with one LLM call per ~25 runs, +extracts the parameters, and grows the library. It is **idempotent** — re-run it whenever; +already-learned runs are skipped (`library/.learned.json`), and big folders are chunked +automatically. `--dry-run` shows the grouping plan without changing anything. + +That's the whole loop: solve → learn → the next solve reuses the library automatically. +Everything below is **manual mode** — explicit manifests, benchmark-grade gold gates, fine +control over every field. You don't need it to get started. + +**Where this fits:** +- *recurring personal queries* — releases, commit counts, price checks: pay the exploration + once, every repeat is cheap (or free — run the skill standalone from cron, no model); +- *same-template batch jobs* — QA flows, report pulls: solve 3, learn, run the rest on skills; +- *a team library* — commit `./library` to your repo; everyone's agent reuses it. + +## Manual mode: full control (end-to-end) The library grows **offline** from batches of solved tasks, and is consumed **at solve time** by the agent. Tasks are provided **manually** today — you pick which tasks to solve and batch. The diff --git a/src/webwright/skills/__main__.py b/src/webwright/skills/__main__.py new file mode 100644 index 0000000..d76eec7 --- /dev/null +++ b/src/webwright/skills/__main__.py @@ -0,0 +1,17 @@ +"""python -m webwright.skills — friendly entry points.""" +import sys + +CMDS = {"learn": "webwright.skills.learn", "update": "webwright.skills.update"} + +def main() -> int: + if len(sys.argv) > 1 and sys.argv[1] in CMDS: + import importlib + mod = importlib.import_module(CMDS[sys.argv[1]]) + return mod.main(sys.argv[2:]) + print("usage: python -m webwright.skills …\n" + " learn distill a folder of finished runs into skills (no manifest needed)\n" + " update manual mode: distill from an explicit batch.json manifest") + return 1 + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/src/webwright/skills/examples/solve_with_library.sh b/src/webwright/skills/examples/solve_with_library.sh new file mode 100755 index 0000000..b7cbb16 --- /dev/null +++ b/src/webwright/skills/examples/solve_with_library.sh @@ -0,0 +1,9 @@ +#!/usr/bin/env bash +# Solve a task WITH the library — prepends the skill hint and the answer-output +# instruction, then hands off to webwright. Usage: +# ./solve_with_library.sh "task text" START_URL /abs/path/to/library [webwright args...] +TASK="$1"; URL="$2"; LIB="$3"; shift 3 +SPEC='Additionally, write the final answer into $WORKSPACE_DIR/agent_response.json as {"retrieved_data": }.' +PROMPT=$(python -c 'import sys; from webwright.skills import with_skill_hint +print(with_skill_hint(sys.argv[1] + " " + sys.argv[3], task=sys.argv[1], library=sys.argv[2]))' "$TASK" "$LIB" "$SPEC") +exec python -m webwright.run.cli main -t "$PROMPT" --start-url "$URL" "$@" diff --git a/src/webwright/skills/learn.py b/src/webwright/skills/learn.py new file mode 100644 index 0000000..2b31e03 --- /dev/null +++ b/src/webwright/skills/learn.py @@ -0,0 +1,168 @@ +"""learn — the friendly entry: turn a folder of finished runs into library skills. + + python -m webwright.skills learn [--library ./library] [--golds golds.json] + [--chunk 25] [--dry-run] + +No manifest to write. For every run dir under it reads task.json (task text, +start_url) and agent_response.json (the answer), gates it (gold if --golds has this +task_id, else self_verify), asks the LLM ONCE per chunk to group tasks into templates and +extract per-task params, then feeds the groups to evolve. Idempotent: processed run dirs +are remembered in /.learned.json and skipped next time. The generated manifest +of each chunk is saved next to the ledger for auditing. +""" +from __future__ import annotations + +import json +from pathlib import Path +from urllib.parse import urlparse + +from .gate import gate +from .library import Library +from .llm import llm_json +from .update import Trace, evolve + + +def infer_schema(answer): + """Mechanically derive output_schema from the answer's shape.""" + if isinstance(answer, list): + item = answer[0] if answer else "" + t = ("number" if isinstance(item, (int, float)) and not isinstance(item, bool) + else "object" if isinstance(item, dict) else "string") + return {"type": "array", "items": {"type": t}} + if isinstance(answer, (int, float)) and not isinstance(answer, bool): + return {"type": "number"} + if isinstance(answer, dict): + return {"type": "object"} + return {"type": "string"} + + +def collect_runs(runs_dir: Path, ledger: dict): + """[{dir, task_id, task, start_url, answer}] for finished runs not yet learned.""" + out = [] + for d in sorted(Path(runs_dir).iterdir()): + if not d.is_dir() or str(d.resolve()) in ledger["runs"]: + continue + tj, ar = d / "task.json", d / "agent_response.json" + if not tj.exists(): + continue + if not ar.exists(): + print(f" skip {d.name}: no agent_response.json " + f"(solve with `skills solve`, or add the answer-output instruction)") + continue + try: + t = json.loads(tj.read_text()) + answer = json.loads(ar.read_text()).get("retrieved_data") + except Exception as e: + print(f" skip {d.name}: unreadable ({e})") + continue + # strip a skill-library hint if the prompt carried one + task = t.get("task", "") + if "## Skill library" in task: + task = task.split("---", 1)[-1].strip() + out.append({"dir": str(d.resolve()), "task_id": t.get("task_id", d.name), + "task": task, "start_url": t.get("start_url", ""), "answer": answer}) + return out + + +_GROUP_SYS = ( + "You organize solved web tasks into task TEMPLATES. Tasks are instances of the same " + "template when they differ only in parameter values (names, dates, places, counts).\n" + "You are given existing template strings and a numbered task list. Return STRICT JSON:\n" + '{"groups": [{"template": "sentence with {{param}} placeholders", ' + '"members": [{"i": , "params": {"": "", ...}}]}]}\n' + "Rules: if a task matches an EXISTING template, use that exact template string verbatim. " + "Every task index appears in exactly one group. A group may have a single member. " + "Params must be the concrete values from the task text." +) + + +def group_chunk(runs, existing_templates): + listing = "\n".join(f"{i}: {r['task'][:220]}" for i, r in enumerate(runs)) + existing = "\n".join(f"- {t}" for t in existing_templates) or "(none yet)" + out = llm_json(_GROUP_SYS, f"## Existing templates\n{existing}\n\n## Tasks\n{listing}") + return out.get("groups", []) + + +def learn(runs_dir, library_root, golds=None, chunk=25, dry_run=False): + lib = Library(library_root) + ledger_path = Path(library_root) / ".learned.json" + ledger = json.loads(ledger_path.read_text()) if ledger_path.exists() else {"runs": {}} + golds = golds or {} + + runs = collect_runs(Path(runs_dir), ledger) + if not runs: + print("nothing new to learn"); return + + # gate first — wrong solves never reach the grouping step + admitted = [] + for r in runs: + g = (gate(r["answer"], gold=golds[r["task_id"]], method="gold") + if r["task_id"] in golds else gate(r["answer"], method="self_verify")) + r["admit"] = g.admit + if g.admit: + admitted.append(r) + else: + print(f" gate ✗ {r['task_id']}: {g.reason}") + print(f"{len(admitted)}/{len(runs)} runs admitted by gate " + f"({'gold' if golds else 'self_verify'})") + if not admitted: + return + + for lo in range(0, len(admitted), chunk): + batch = admitted[lo:lo + chunk] + existing = [s.meta.get("template", "") for s in lib.list()] + groups = group_chunk(batch, existing) + print(f"\nchunk {lo // chunk + 1}: {len(batch)} runs -> {len(groups)} template(s)") + for g in groups: + members = [m for m in g.get("members", []) if 0 <= m.get("i", -1) < len(batch)] + print(f" {g.get('template', '?')[:90]} ({len(members)} solve(s))") + if dry_run: + continue + for g in groups: + tmpl = g.get("template", "") + traces = [] + for m in g.get("members", []): + if not (0 <= m.get("i", -1) < len(batch)): + continue + r = batch[m["i"]] + code_p = Path(r["dir"]) / "final_script.py" + traces.append(Trace( + template=tmpl, + code=code_p.read_text() if code_p.exists() else "", + answer=r["answer"], correct=True, + # existing template -> mark adapt so evolve REFINES instead of ignoring + verdict="adapt" if tmpl in existing else "skip", + meta={"params": m.get("params", {}), + "site": urlparse(r["start_url"]).netloc, + "start_url": r["start_url"], + "output_schema": infer_schema(r["answer"])})) + if not traces: + continue + log = evolve(traces, lib) + print(f" evolve: {json.dumps(log)}") + for m in g.get("members", []): + if 0 <= m.get("i", -1) < len(batch): + ledger["runs"][batch[m["i"]]["dir"]] = {"template": tmpl} + # audit trail + idempotence, saved per chunk + ledger_path.write_text(json.dumps(ledger, indent=2)) + if not dry_run: + print(f"\nlibrary now has {len(lib.list())} skill(s); ledger -> {ledger_path}") + + +def main(argv=None) -> int: + import argparse + p = argparse.ArgumentParser(prog="python -m webwright.skills learn", + description="Distill a folder of finished runs into library skills.") + p.add_argument("runs_dir", help="Folder containing webwright run directories.") + p.add_argument("--library", default="library") + p.add_argument("--golds", default="", help="JSON file {task_id: gold_answer} -> gold gate.") + p.add_argument("--chunk", type=int, default=25, help="Runs per LLM grouping call.") + p.add_argument("--dry-run", action="store_true", help="Show the grouping plan, change nothing.") + a = p.parse_args(argv) + golds = json.loads(Path(a.golds).read_text()) if a.golds else {} + learn(a.runs_dir, a.library, golds=golds, chunk=a.chunk, dry_run=a.dry_run) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/tests/skills/test_learn.py b/tests/skills/test_learn.py new file mode 100644 index 0000000..71aaedf --- /dev/null +++ b/tests/skills/test_learn.py @@ -0,0 +1,36 @@ +"""Unit test: learn's LLM-free plumbing (schema inference, run collection, ledger skip).""" +import json, tempfile +from pathlib import Path +from webwright.skills.learn import infer_schema, collect_runs + + +def run(): + assert infer_schema([1, 2]) == {"type": "array", "items": {"type": "number"}} + assert infer_schema(["a"]) == {"type": "array", "items": {"type": "string"}} + assert infer_schema(7) == {"type": "number"} + assert infer_schema({"k": 1}) == {"type": "object"} + assert infer_schema("x") == {"type": "string"} + + with tempfile.TemporaryDirectory() as td: + td = Path(td) + d1 = td / "run_a"; d1.mkdir() + (d1 / "task.json").write_text(json.dumps( + {"task": "## Skill library\nblah\n---\nCount commits by Jane", "task_id": "a", + "start_url": "http://gitlab.example.com/x"})) + (d1 / "agent_response.json").write_text(json.dumps({"retrieved_data": [3]})) + d2 = td / "run_b"; d2.mkdir() # unfinished: no agent_response + (d2 / "task.json").write_text(json.dumps({"task": "t", "task_id": "b"})) + + runs = collect_runs(td, {"runs": {}}) + assert len(runs) == 1 and runs[0]["task_id"] == "a", runs + assert runs[0]["task"] == "Count commits by Jane", "hint must be stripped" + assert runs[0]["answer"] == [3] + + # ledger makes it idempotent + runs2 = collect_runs(td, {"runs": {str(d1.resolve()): {}}}) + assert runs2 == [], "already-learned run must be skipped" + print("test_learn OK") + + +if __name__ == "__main__": + run() From b656e6c0caeac2b0450de7a7ad97b276bafe88c0 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Wed, 8 Jul 2026 21:18:36 +0000 Subject: [PATCH 17/54] skills: fix learn-interface test findings (silent-reuse-off, tracebacks, leaks) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit External-user test of the friendly path surfaced that a trivially-easy config mistake (gateway key + unset OPENAI_ENDPOINT) silently disabled ALL reuse. Fixes: - skill_use: a hard error still degrades to skip (never block solving) but now says LOUDLY it is a LOOKUP FAILURE, not a no-match — error field in the JSON, hint about OPENAI_ENDPOINT/SKILL_MODEL_ENDPOINT, and a stderr line (F1+F2) - README Quickstart: gateway users must export OPENAI_ENDPOINT/OPENAI_MODEL for BOTH steps, stated where step-1 users actually look (F2) - learn: grouping-LLM failure now exits with a one-line actionable message instead of a 40-line traceback (F3); skipped-for-missing-answer runs get a visible summary with the correct pointer (the old message named a command that does not exist) (F4) - learn: strips the answer-output instruction from task text so it cannot leak into templates/skill_ids (F7) - solve_with_library.sh: usage check instead of passing empty args into the CLI (F6) --- src/webwright/skills/README.md | 2 ++ .../skills/examples/solve_with_library.sh | 4 ++++ src/webwright/skills/learn.py | 22 +++++++++++++++---- src/webwright/tools/skill_use.py | 11 ++++++++-- tests/skills/test_learn.py | 7 +++--- 5 files changed, 37 insertions(+), 9 deletions(-) diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md index 5daab1b..ee80449 100644 --- a/src/webwright/skills/README.md +++ b/src/webwright/skills/README.md @@ -75,6 +75,8 @@ examples/solve_with_library.sh "How many commits did Jane make in Jan 2023?" \ # 2. turn everything you've solved into skills — no manifest, no fields to learn export OPENAI_API_KEY=... +# custom / OpenAI-compatible gateway? BOTH steps need these too, or reuse is silently off: +export OPENAI_ENDPOINT=https://your-gateway/... OPENAI_MODEL=your-model python -m webwright.skills learn outputs/ --library ./library ``` diff --git a/src/webwright/skills/examples/solve_with_library.sh b/src/webwright/skills/examples/solve_with_library.sh index b7cbb16..246441b 100755 --- a/src/webwright/skills/examples/solve_with_library.sh +++ b/src/webwright/skills/examples/solve_with_library.sh @@ -2,6 +2,10 @@ # Solve a task WITH the library — prepends the skill hint and the answer-output # instruction, then hands off to webwright. Usage: # ./solve_with_library.sh "task text" START_URL /abs/path/to/library [webwright args...] +if [ $# -lt 3 ]; then + echo "usage: $0 \"task text\" START_URL /abs/path/to/library [webwright args...]" >&2 + exit 1 +fi TASK="$1"; URL="$2"; LIB="$3"; shift 3 SPEC='Additionally, write the final answer into $WORKSPACE_DIR/agent_response.json as {"retrieved_data": }.' PROMPT=$(python -c 'import sys; from webwright.skills import with_skill_hint diff --git a/src/webwright/skills/learn.py b/src/webwright/skills/learn.py index 2b31e03..2250800 100644 --- a/src/webwright/skills/learn.py +++ b/src/webwright/skills/learn.py @@ -39,6 +39,7 @@ def infer_schema(answer): def collect_runs(runs_dir: Path, ledger: dict): """[{dir, task_id, task, start_url, answer}] for finished runs not yet learned.""" out = [] + skipped_no_answer = 0 for d in sorted(Path(runs_dir).iterdir()): if not d.is_dir() or str(d.resolve()) in ledger["runs"]: continue @@ -46,8 +47,8 @@ def collect_runs(runs_dir: Path, ledger: dict): if not tj.exists(): continue if not ar.exists(): - print(f" skip {d.name}: no agent_response.json " - f"(solve with `skills solve`, or add the answer-output instruction)") + skipped_no_answer += 1 + print(f" skip {d.name}: no agent_response.json") continue try: t = json.loads(tj.read_text()) @@ -55,12 +56,19 @@ def collect_runs(runs_dir: Path, ledger: dict): except Exception as e: print(f" skip {d.name}: unreadable ({e})") continue - # strip a skill-library hint if the prompt carried one + # strip pipeline text the wrapper may have carried into the prompt: the + # skill-library hint and the answer-output instruction must not leak into templates task = t.get("task", "") if "## Skill library" in task: task = task.split("---", 1)[-1].strip() + if "Additionally, write the final answer into" in task: + task = task.split("Additionally, write the final answer into", 1)[0].strip() out.append({"dir": str(d.resolve()), "task_id": t.get("task_id", d.name), "task": task, "start_url": t.get("start_url", ""), "answer": answer}) + if skipped_no_answer: + print(f" ! {skipped_no_answer} run(s) had no answer file and were skipped — their solves " + f"cannot be aggregated. Solve via examples/solve_with_library.sh (it adds the " + f"answer-output instruction), or see README 'Manual mode' step 1.") return out @@ -79,7 +87,13 @@ def collect_runs(runs_dir: Path, ledger: dict): def group_chunk(runs, existing_templates): listing = "\n".join(f"{i}: {r['task'][:220]}" for i, r in enumerate(runs)) existing = "\n".join(f"- {t}" for t in existing_templates) or "(none yet)" - out = llm_json(_GROUP_SYS, f"## Existing templates\n{existing}\n\n## Tasks\n{listing}") + try: + out = llm_json(_GROUP_SYS, f"## Existing templates\n{existing}\n\n## Tasks\n{listing}") + except Exception as exc: + raise SystemExit( + f"learn: the grouping LLM call failed: {exc}\n" + f"Check OPENAI_API_KEY — and on a custom gateway also set " + f"OPENAI_ENDPOINT (and OPENAI_MODEL), or SKILL_MODEL_ENDPOINT/SKILL_MODEL_NAME.") return out.get("groups", []) diff --git a/src/webwright/tools/skill_use.py b/src/webwright/tools/skill_use.py index d14aec7..072faf9 100644 --- a/src/webwright/tools/skill_use.py +++ b/src/webwright/tools/skill_use.py @@ -72,8 +72,15 @@ def main(argv: list[str] | None = None) -> int: args = build_parser().parse_args(argv) try: result = recommend(args.task, args.library) - except Exception as exc: # never block solving - result = {"verdict": "skip", "skill_id": None, "reason": f"skill_use error: {exc}"} + except Exception as exc: + # Degrade to skip so solving is never blocked — but say LOUDLY that the library + # was NOT consulted: a config/auth error here silently disables all reuse otherwise. + result = {"verdict": "skip", "skill_id": None, "error": str(exc), + "reason": "LOOKUP FAILED (library was NOT consulted) — this is an error, " + "not a no-match. Check OPENAI_API_KEY and, on a custom gateway, " + "OPENAI_ENDPOINT / SKILL_MODEL_ENDPOINT.", + } + print(f"skill_use ERROR: {exc}", file=sys.stderr) payload = json.dumps(result, ensure_ascii=False, indent=2) if args.output: with open(args.output, "w", encoding="utf-8") as f: diff --git a/tests/skills/test_learn.py b/tests/skills/test_learn.py index 71aaedf..5f46b67 100644 --- a/tests/skills/test_learn.py +++ b/tests/skills/test_learn.py @@ -15,15 +15,16 @@ def run(): td = Path(td) d1 = td / "run_a"; d1.mkdir() (d1 / "task.json").write_text(json.dumps( - {"task": "## Skill library\nblah\n---\nCount commits by Jane", "task_id": "a", - "start_url": "http://gitlab.example.com/x"})) + {"task": "## Skill library\nblah\n---\nCount commits by Jane Additionally, " + "write the final answer into $WORKSPACE_DIR/agent_response.json as {...}.", + "task_id": "a", "start_url": "http://gitlab.example.com/x"})) (d1 / "agent_response.json").write_text(json.dumps({"retrieved_data": [3]})) d2 = td / "run_b"; d2.mkdir() # unfinished: no agent_response (d2 / "task.json").write_text(json.dumps({"task": "t", "task_id": "b"})) runs = collect_runs(td, {"runs": {}}) assert len(runs) == 1 and runs[0]["task_id"] == "a", runs - assert runs[0]["task"] == "Count commits by Jane", "hint must be stripped" + assert runs[0]["task"] == "Count commits by Jane", "hint AND answer-spec must be stripped" assert runs[0]["answer"] == [3] # ledger makes it idempotent From 5cac2333d368afb74c8c16b08e212115f4d13cb2 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Wed, 8 Jul 2026 21:27:58 +0000 Subject: [PATCH 18/54] skills README: env exports at the top of Quickstart (step-1-only readers get them too) --- src/webwright/skills/README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md index ee80449..f0f2a29 100644 --- a/src/webwright/skills/README.md +++ b/src/webwright/skills/README.md @@ -69,14 +69,15 @@ Two touch points, **no change to the agent loop or default config**: ## Quickstart — the two-command version ```bash +export OPENAI_API_KEY=... +# custom / OpenAI-compatible gateway? BOTH steps need these too, or reuse is silently off: +export OPENAI_ENDPOINT=https://your-gateway/... OPENAI_MODEL=your-model + # 1. solve tasks with the library in the loop (wrapper = hint + answer-output instruction) examples/solve_with_library.sh "How many commits did Jane make in Jan 2023?" \ http://gitlab.example.com /abs/path/library -o outputs -c base.yaml -c model_openai.yaml # 2. turn everything you've solved into skills — no manifest, no fields to learn -export OPENAI_API_KEY=... -# custom / OpenAI-compatible gateway? BOTH steps need these too, or reuse is silently off: -export OPENAI_ENDPOINT=https://your-gateway/... OPENAI_MODEL=your-model python -m webwright.skills learn outputs/ --library ./library ``` From 64a4feb3f3509e129c80a2934ba0e4b34dd5d4e9 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Wed, 8 Jul 2026 22:00:20 +0000 Subject: [PATCH 19/54] =?UTF-8?q?skills:=20release-gap=20fixes=20=E2=80=94?= =?UTF-8?q?=20gate=20honesty,=20aggregation=20showcase,=20regression=20tes?= =?UTF-8?q?ts?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - README: "Only verified solves get in" -> "Validation-gated, exactly as strong as the gate you give it" — states plainly that the default self_verify checks shape only and that the WebArena numbers used the gold gate; learn prints the same warning at run time when no --golds is given - examples/learned_library/: a skill produced by "skills learn" from 3 real GitHub solves — n_solves=3, owner/repo lifted to parameters, two extraction strategies as fallbacks; verified standalone on an unseen repo (numpy/numpy -> v2.5.1, no model); test_learned_example.py locks n_solves>=3 + lifted params + no leak - regression tests for the interface-test findings: F1 (skill_use surfaces hard errors as ERROR, not quiet skip), F3 (learn exits with an actionable message) --- src/webwright/skills/README.md | 8 +- src/webwright/skills/examples/README.md | 3 + .../meta.json | 21 ++ .../skill.py | 204 ++++++++++++++++++ src/webwright/skills/learn.py | 3 + tests/skills/test_learn.py | 18 ++ tests/skills/test_learned_example.py | 27 +++ tests/skills/test_retrieve_decide.py | 16 ++ 8 files changed, 298 insertions(+), 2 deletions(-) create mode 100644 src/webwright/skills/examples/learned_library/what_is_the_latest_release_version_of_ow_c29dab8/meta.json create mode 100644 src/webwright/skills/examples/learned_library/what_is_the_latest_release_version_of_ow_c29dab8/skill.py create mode 100644 tests/skills/test_learned_example.py diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md index f0f2a29..6c1976a 100644 --- a/src/webwright/skills/README.md +++ b/src/webwright/skills/README.md @@ -14,8 +14,12 @@ It complements `crafted_cli`: where `crafted_cli` parameterizes a single task's anticipating what might vary, `update.refine` parameterizes **across multiple verified solves** — the differences actually observed between instances become the parameters. -**Only verified solves get in.** An admission gate checks every solution before it enters the -library, so wrong answers never pollute what the agent reuses. +**Validation-gated — exactly as strong as the gate you give it.** Every solve passes an +admission gate before it can enter the library. With gold answers (benchmarks — this is what +our WebArena numbers used) the gate is real supervision: wrong answers never get in. The +default `self_verify` gate only checks shape and non-emptiness — it filters garbage, **not +wrong-but-plausible answers**. Pass `--golds` to `learn`, or bring your own judge, when +correctness matters. **One solve isn't a skill yet.** A single task's script is correct but narrow — it solves *that* instance. So the update step aggregates multiple verified solves of the same task diff --git a/src/webwright/skills/examples/README.md b/src/webwright/skills/examples/README.md index b5c97c6..42816de 100644 --- a/src/webwright/skills/examples/README.md +++ b/src/webwright/skills/examples/README.md @@ -5,6 +5,9 @@ evaluation), lightly curated. Nothing is hand-written. ``` examples/ +├── learned_library/ # produced by `skills learn` from 3 REAL solves (n_solves=3): +│ └── what_is_the_latest_release…/ # owner/repo lifted to parameters, two strategies as fallbacks; +│ # tested on an unseen repo (numpy/numpy -> v2.5.1, no model) ├── example_library/ # a library with one skill, exactly as evolve wrote it │ └── how_many_commits_did_user_make_period_in_the_cur/ │ ├── skill.py # the executable skill (126 lines) diff --git a/src/webwright/skills/examples/learned_library/what_is_the_latest_release_version_of_ow_c29dab8/meta.json b/src/webwright/skills/examples/learned_library/what_is_the_latest_release_version_of_ow_c29dab8/meta.json new file mode 100644 index 0000000..095ff79 --- /dev/null +++ b/src/webwright/skills/examples/learned_library/what_is_the_latest_release_version_of_ow_c29dab8/meta.json @@ -0,0 +1,21 @@ +{ + "template": "What is the latest release version of {{owner}}/{{repo}} on GitHub?", + "provenance": "update-refined", + "site": "github.com", + "summary": "Refined from 3 gate-passed solves; parameterized + primitives.", + "signature": { + "params": [ + "owner", + "repo" + ], + "call": "python skill.py taskspec.json" + }, + "output_schema": { + "type": "array", + "items": { + "type": "string" + } + }, + "n_solves": 3, + "revisions": 1 +} \ No newline at end of file diff --git a/src/webwright/skills/examples/learned_library/what_is_the_latest_release_version_of_ow_c29dab8/skill.py b/src/webwright/skills/examples/learned_library/what_is_the_latest_release_version_of_ow_c29dab8/skill.py new file mode 100644 index 0000000..e4e2c45 --- /dev/null +++ b/src/webwright/skills/examples/learned_library/what_is_the_latest_release_version_of_ow_c29dab8/skill.py @@ -0,0 +1,204 @@ +import asyncio +import json +import os +import re +import sys +from pathlib import Path +from typing import Optional, Tuple + +from playwright.async_api import async_playwright, TimeoutError as PlaywrightTimeoutError + + +def find_workspace() -> Path: + if len(sys.argv) > 1: + return Path(sys.argv[1]).resolve().parent + return Path(os.environ.get("WORKSPACE_DIR", Path.cwd())).resolve() + + +WORKSPACE = find_workspace() +TASKSPEC_PATH = Path(sys.argv[1]).resolve() +AGENT_RESPONSE_PATH = WORKSPACE / "agent_response.json" + +FINAL_RUNS = WORKSPACE / "final_runs" +FINAL_RUNS.mkdir(parents=True, exist_ok=True) + + +def next_run_dir() -> Path: + existing = [] + for p in FINAL_RUNS.glob("run_*"): + try: + existing.append(int(p.name.split("_")[1])) + except Exception: + pass + run_id = max(existing, default=0) + 1 + run_dir = FINAL_RUNS / f"run_{run_id:03d}" + (run_dir / "screenshots").mkdir(parents=True, exist_ok=True) + return run_dir + + +RUN_DIR = next_run_dir() +SCREENSHOTS_DIR = RUN_DIR / "screenshots" +LOG_PATH = RUN_DIR / "final_script_log.txt" +LOG_PATH.write_text("", encoding="utf-8") + + +def log(msg: str) -> None: + print(msg) + with LOG_PATH.open("a", encoding="utf-8") as f: + f.write(msg + "\n") + + +def load_taskspec(path: Path) -> dict: + return json.loads(path.read_text(encoding="utf-8")) + + +def build_repo_urls(taskspec: dict) -> Tuple[str, str, str]: + params = taskspec.get("params", {}) + owner = params["owner"] + repo = params["repo"] + base = taskspec.get("start_url") or f"https://github.com/{owner}/{repo}" + base = base.rstrip("/") + releases_url = f"{base}/releases" + latest_url = f"{base}/releases/latest" + return base, releases_url, latest_url + + +def extract_version_from_text(text: str) -> Optional[str]: + if not text: + return None + patterns = [ + r"\bv?\d+(?:\.\d+){1,}\b", + r"\b\d{4}\.\d+(?:\.\d+)*\b", + ] + for pattern in patterns: + m = re.search(pattern, text) + if m: + return m.group(0) + return None + + +def extract_version_from_url(url: str) -> Optional[str]: + if not url: + return None + m = re.search(r"/releases/tag/([^/?#]+)", url) + if m: + return m.group(1) + return None + + +async def screenshot(page, step: int, name: str) -> None: + await page.screenshot(path=str(SCREENSHOTS_DIR / f"final_execution_{step}_{name}.png"), full_page=True) + + +async def open_page(page, url: str, step: int, label: str) -> None: + log(f"step {step} action: open {label} {url}") + await page.goto(url, wait_until="domcontentloaded") + log(f"step {step} observed url: {page.url}") + log(f"step {step} observed title: {await page.title()}") + await screenshot(page, step, label.replace(" ", "_")) + + +async def try_repo_page_release(page) -> Optional[str]: + link = page.locator('a[href*="/releases/tag/"]').first + try: + await link.wait_for(timeout=3000) + raw_text = (await link.inner_text()).strip() + href = await link.get_attribute("href") + version = extract_version_from_text(raw_text) or extract_version_from_url(href or "") + log(f"repo page release candidate text={raw_text!r} href={href!r} parsed={version!r}") + return version + except PlaywrightTimeoutError: + log("repo page did not expose a visible release tag link quickly") + return None + + +async def open_latest_release_and_extract(page, latest_url: str) -> Optional[str]: + await page.goto(latest_url, wait_until="domcontentloaded") + current_url = page.url + title = await page.title() + body_text = await page.locator("main").inner_text() if await page.locator("main").count() > 0 else await page.locator("body").inner_text() + version = ( + extract_version_from_url(current_url) + or extract_version_from_text(title) + or extract_version_from_text(body_text) + ) + log(f"latest release page url={current_url}") + log(f"latest release page title={title}") + log(f"latest release extracted version={version!r}") + return version + + +async def open_releases_page_and_extract(page, releases_url: str) -> Optional[str]: + await page.goto(releases_url, wait_until="domcontentloaded") + tag_link = page.locator('a[href*="/releases/tag/"]').first + try: + await tag_link.wait_for(timeout=5000) + except PlaywrightTimeoutError: + log("releases page did not show a tag link") + return None + raw_text = (await tag_link.inner_text()).strip() + href = await tag_link.get_attribute("href") + version = extract_version_from_text(raw_text) or extract_version_from_url(href or "") + log(f"releases page first tag text={raw_text!r} href={href!r} parsed={version!r}") + return version + + +def validate_version(version: Optional[str]) -> str: + if not version or not isinstance(version, str): + raise RuntimeError("Could not extract latest release version") + return version.strip() + + +def write_response(data): + AGENT_RESPONSE_PATH.write_text(json.dumps({"retrieved_data": data}, ensure_ascii=False), encoding="utf-8") + + +async def main(): + taskspec = load_taskspec(TASKSPEC_PATH) + repo_url, releases_url, latest_url = build_repo_urls(taskspec) + + log(f"loaded taskspec from {TASKSPEC_PATH}") + log(f"repo_url={repo_url}") + log(f"releases_url={releases_url}") + log(f"latest_url={latest_url}") + + step = 1 + async with async_playwright() as p: + browser = await p.chromium.launch(headless=True, args=["--no-sandbox"]) + context = await browser.new_context(viewport={"width": 1280, "height": 1800}) + page = await context.new_page() + + await open_page(page, repo_url, step, "repo_page") + repo_candidate = await try_repo_page_release(page) + step += 1 + + version = None + log(f"step {step} action: open latest release redirect page and extract version") + version = await open_latest_release_and_extract(page, latest_url) + await screenshot(page, step, "latest_release_page") + step += 1 + + if not version: + log(f"step {step} action: fallback to releases page") + version = await open_releases_page_and_extract(page, releases_url) + await screenshot(page, step, "releases_page") + step += 1 + + if not version and repo_candidate: + log(f"step {step} action: fallback to repo page candidate") + version = repo_candidate + step += 1 + + version = validate_version(version) + + response_data = [version] + write_response(response_data) + log(f"final extracted version={version}") + log(f"wrote {AGENT_RESPONSE_PATH} with retrieved_data matching output schema") + await screenshot(page, step, "final_state") + + await browser.close() + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/src/webwright/skills/learn.py b/src/webwright/skills/learn.py index 2250800..e230758 100644 --- a/src/webwright/skills/learn.py +++ b/src/webwright/skills/learn.py @@ -119,6 +119,9 @@ def learn(runs_dir, library_root, golds=None, chunk=25, dry_run=False): print(f" gate ✗ {r['task_id']}: {g.reason}") print(f"{len(admitted)}/{len(runs)} runs admitted by gate " f"({'gold' if golds else 'self_verify'})") + if not golds: + print(" ! gate=self_verify: shape check only — wrong-but-well-formed answers PASS. " + "Pass --golds for real verification.") if not admitted: return diff --git a/tests/skills/test_learn.py b/tests/skills/test_learn.py index 5f46b67..c2e0df4 100644 --- a/tests/skills/test_learn.py +++ b/tests/skills/test_learn.py @@ -33,5 +33,23 @@ def run(): print("test_learn OK") +def run_regressions(): + """F3: grouping-LLM failure must exit with an actionable one-liner, not a traceback.""" + import webwright.skills.learn as L + orig = L.llm_json + L.llm_json = lambda *a, **k: (_ for _ in ()).throw(RuntimeError("401 unauthorized")) + try: + try: + L.group_chunk([{"task": "t"}], []) + raise AssertionError("must raise SystemExit") + except SystemExit as e: + msg = str(e) + assert "OPENAI_ENDPOINT" in msg and "401" in msg, msg + finally: + L.llm_json = orig + print("test_learn regressions OK") + + if __name__ == "__main__": run() + run_regressions() diff --git a/tests/skills/test_learned_example.py b/tests/skills/test_learned_example.py new file mode 100644 index 0000000..d0dfd1c --- /dev/null +++ b/tests/skills/test_learned_example.py @@ -0,0 +1,27 @@ +"""Lock the flagship property: the checked-in learned skill was AGGREGATED from +multiple solves (n_solves >= 3) with real lifted parameters — not a single-solve copy.""" +import json +from pathlib import Path + +ROOT = Path(__file__).resolve().parents[2] / "src/webwright/skills/examples/learned_library" + + +def run(): + dirs = [d for d in ROOT.iterdir() if (d / "meta.json").exists()] + assert dirs, "learned_library example missing" + for d in dirs: + meta = json.loads((d / "meta.json").read_text()) + assert meta["n_solves"] >= 3, f"{d.name}: must be aggregated from >=3 solves, got {meta['n_solves']}" + params = meta["signature"]["params"] + assert len(params) >= 2, f"{d.name}: parameters must be lifted, got {params}" + assert "{{" in meta["template"], "template must have {{param}} placeholders" + assert "Additionally, write" not in meta["template"], "pipeline text must not leak (F7)" + code = (d / "skill.py").read_text() + compile(code, d.name, "exec") + for p in params: + assert p in code, f"param {p} must appear in the skill code" + print("test_learned_example OK") + + +if __name__ == "__main__": + run() diff --git a/tests/skills/test_retrieve_decide.py b/tests/skills/test_retrieve_decide.py index 826f117..c92f792 100644 --- a/tests/skills/test_retrieve_decide.py +++ b/tests/skills/test_retrieve_decide.py @@ -1,5 +1,6 @@ """Unit test: deterministic parts of retrieve/decide (no LLM). The LLM paths are smoke-tested in test_front.py.""" +import json import sys, tempfile from pathlib import Path pass @@ -64,6 +65,21 @@ def run(): r2 = T2.recommend("anything", d2) # exists but has no skills assert r2["verdict"] == "skip" and "warning" in r2, r2 + # F1: a hard failure inside recommend must surface as an ERROR (loud), not a quiet skip + import io, contextlib + import webwright.tools.skill_use as TU + orig_rec = TU.recommend + TU.recommend = lambda *a, **k: (_ for _ in ()).throw(RuntimeError("boom 401")) + try: + buf = io.StringIO() + with contextlib.redirect_stdout(buf): + TU.main(["--task", "x", "--library", "lib"]) + out = json.loads(buf.getvalue()) + assert out["verdict"] == "skip" and "error" in out, out + assert "NOT consulted" in out["reason"], out["reason"] + finally: + TU.recommend = orig_rec + print("test_retrieve_decide OK") From ddca588bfa2d792246c7c4b68f06e489294b7af7 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Wed, 8 Jul 2026 22:00:35 +0000 Subject: [PATCH 20/54] ci: run skills unit tests + wrapper usage check on module changes --- .github/workflows/skills-tests.yml | 25 +++++++++++++++++++++++++ 1 file changed, 25 insertions(+) create mode 100644 .github/workflows/skills-tests.yml diff --git a/.github/workflows/skills-tests.yml b/.github/workflows/skills-tests.yml new file mode 100644 index 0000000..a19f42b --- /dev/null +++ b/.github/workflows/skills-tests.yml @@ -0,0 +1,25 @@ +name: skills-tests +on: + push: + paths: ["src/webwright/skills/**", "src/webwright/tools/skill_use.py", "tests/skills/**"] + pull_request: + paths: ["src/webwright/skills/**", "src/webwright/tools/skill_use.py", "tests/skills/**"] +jobs: + unit: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: { python-version: "3.12" } + - run: pip install httpx pyyaml jinja2 pydantic + - name: skills unit tests (LLM-free) + run: | + set -e + for t in tests/skills/test_*.py; do + echo "== $t"; PYTHONPATH=src python "$t" + done + - name: wrapper usage check (F6) + run: | + set +e + bash src/webwright/skills/examples/solve_with_library.sh > /dev/null 2>&1 + [ $? -eq 1 ] && echo "usage-exit OK" || { echo "wrapper must exit 1 on missing args"; exit 1; } From 06d72b372f6aeb326575a47aa97321236dfecf2c Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Thu, 9 Jul 2026 22:05:20 +0000 Subject: [PATCH 21/54] evals: make the WebArena numbers reproducible (evals/webarena/) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The README table (held-out 70% vs 55%, 14.7 vs 17.1 steps; train 26/30 vs 23/30) previously existed only as prose. Now: - evals/webarena/results/: sanitized per-task records of the exact run behind the table — task id, answer, gold score, steps, skill verdict; one command ("reproduce.py table --results results") re-derives the table, no setup - evals/webarena/reproduce.py: self-contained driver that re-runs the whole experiment (train -> gold-gated update -> held-out with/base -> table) against your own WebArena deployment via microsoft/webarena-verified; resumable, parallelizable per template - run_all.sh + model.eval.yaml (agent-model overrides; gateway pointer) - tests/skills/test_eval_snapshot.py: CI-locks the records to the published numbers and enforces snapshot sanitization (no local paths/hosts/keys) - skills README + examples README now link the records instead of asking for trust; CI also triggers on evals/webarena/** --- .github/workflows/skills-tests.yml | 4 +- evals/webarena/README.md | 79 ++++ evals/webarena/model.eval.yaml | 17 + evals/webarena/reproduce.py | 336 ++++++++++++++++++ .../webarena/results/t11_heldout12_base.json | 13 + .../webarena/results/t11_heldout12_with.json | 14 + .../webarena/results/t11_heldout13_base.json | 13 + .../webarena/results/t11_heldout13_with.json | 14 + evals/webarena/results/t11_train11.json | 13 + evals/webarena/results/t11_train14.json | 11 + evals/webarena/results/t11_train15.json | 13 + evals/webarena/results/t11_trainwith11.json | 14 + evals/webarena/results/t11_trainwith14.json | 14 + evals/webarena/results/t11_trainwith15.json | 14 + .../results/t132_heldout134_base.json | 15 + .../results/t132_heldout134_with.json | 16 + .../results/t132_heldout135_base.json | 16 + .../results/t132_heldout135_with.json | 19 + evals/webarena/results/t132_train132.json | 15 + evals/webarena/results/t132_train133.json | 15 + evals/webarena/results/t132_train136.json | 15 + evals/webarena/results/t132_trainwith132.json | 16 + evals/webarena/results/t132_trainwith133.json | 16 + evals/webarena/results/t132_trainwith136.json | 16 + .../results/t151_heldout154_base.json | 15 + .../results/t151_heldout154_with.json | 16 + .../results/t151_heldout155_base.json | 15 + .../results/t151_heldout155_with.json | 16 + evals/webarena/results/t151_train151.json | 15 + evals/webarena/results/t151_train152.json | 15 + evals/webarena/results/t151_train153.json | 15 + evals/webarena/results/t151_trainwith151.json | 16 + evals/webarena/results/t151_trainwith152.json | 16 + evals/webarena/results/t151_trainwith153.json | 16 + .../webarena/results/t16_heldout19_base.json | 13 + .../webarena/results/t16_heldout19_with.json | 23 ++ .../webarena/results/t16_heldout20_base.json | 22 ++ .../webarena/results/t16_heldout20_with.json | 23 ++ evals/webarena/results/t16_train16.json | 22 ++ evals/webarena/results/t16_train17.json | 22 ++ evals/webarena/results/t16_train18.json | 22 ++ evals/webarena/results/t16_trainwith16.json | 23 ++ evals/webarena/results/t16_trainwith17.json | 23 ++ evals/webarena/results/t16_trainwith18.json | 23 ++ .../results/t198_heldout201_base.json | 13 + .../results/t198_heldout201_with.json | 14 + .../results/t198_heldout202_base.json | 15 + .../results/t198_heldout202_with.json | 16 + evals/webarena/results/t198_train198.json | 15 + evals/webarena/results/t198_train199.json | 15 + evals/webarena/results/t198_train203.json | 18 + evals/webarena/results/t198_trainwith198.json | 16 + evals/webarena/results/t198_trainwith199.json | 16 + evals/webarena/results/t198_trainwith203.json | 19 + .../results/t213_heldout216_base.json | 17 + .../results/t213_heldout216_with.json | 18 + .../results/t213_heldout217_base.json | 12 + .../results/t213_heldout217_with.json | 22 ++ evals/webarena/results/t213_train213.json | 21 ++ evals/webarena/results/t213_train214.json | 21 ++ evals/webarena/results/t213_train215.json | 17 + evals/webarena/results/t213_trainwith213.json | 22 ++ evals/webarena/results/t213_trainwith214.json | 22 ++ evals/webarena/results/t213_trainwith215.json | 18 + .../results/t248_heldout251_base.json | 17 + .../results/t248_heldout251_with.json | 18 + .../results/t248_heldout252_base.json | 17 + .../results/t248_heldout252_with.json | 18 + evals/webarena/results/t248_train248.json | 17 + evals/webarena/results/t248_train249.json | 17 + evals/webarena/results/t248_train250.json | 17 + evals/webarena/results/t248_trainwith248.json | 18 + evals/webarena/results/t248_trainwith249.json | 18 + evals/webarena/results/t248_trainwith250.json | 18 + .../webarena/results/t279_heldout1_base.json | 13 + .../webarena/results/t279_heldout1_with.json | 16 + .../webarena/results/t279_heldout2_base.json | 13 + .../webarena/results/t279_heldout2_with.json | 14 + evals/webarena/results/t279_train0.json | 17 + evals/webarena/results/t279_train3.json | 13 + evals/webarena/results/t279_train4.json | 17 + evals/webarena/results/t279_trainwith0.json | 16 + evals/webarena/results/t279_trainwith3.json | 19 + evals/webarena/results/t279_trainwith4.json | 18 + .../results/t293_heldout296_base.json | 14 + .../results/t293_heldout296_with.json | 15 + .../results/t293_heldout297_base.json | 14 + .../results/t293_heldout297_with.json | 15 + evals/webarena/results/t293_train293.json | 14 + evals/webarena/results/t293_train294.json | 14 + evals/webarena/results/t293_train295.json | 14 + evals/webarena/results/t293_trainwith293.json | 15 + evals/webarena/results/t293_trainwith294.json | 15 + evals/webarena/results/t293_trainwith295.json | 15 + .../results/t303_heldout305_base.json | 14 + .../results/t303_heldout305_with.json | 15 + .../results/t303_heldout306_base.json | 14 + .../results/t303_heldout306_with.json | 15 + evals/webarena/results/t303_train303.json | 14 + evals/webarena/results/t303_train304.json | 14 + evals/webarena/results/t303_train307.json | 14 + evals/webarena/results/t303_trainwith303.json | 15 + evals/webarena/results/t303_trainwith304.json | 15 + evals/webarena/results/t303_trainwith307.json | 15 + evals/webarena/run_all.sh | 18 + src/webwright/skills/README.md | 8 +- src/webwright/skills/examples/README.md | 3 +- tests/skills/test_eval_snapshot.py | 43 +++ 108 files changed, 2135 insertions(+), 4 deletions(-) create mode 100644 evals/webarena/README.md create mode 100644 evals/webarena/model.eval.yaml create mode 100644 evals/webarena/reproduce.py create mode 100644 evals/webarena/results/t11_heldout12_base.json create mode 100644 evals/webarena/results/t11_heldout12_with.json create mode 100644 evals/webarena/results/t11_heldout13_base.json create mode 100644 evals/webarena/results/t11_heldout13_with.json create mode 100644 evals/webarena/results/t11_train11.json create mode 100644 evals/webarena/results/t11_train14.json create mode 100644 evals/webarena/results/t11_train15.json create mode 100644 evals/webarena/results/t11_trainwith11.json create mode 100644 evals/webarena/results/t11_trainwith14.json create mode 100644 evals/webarena/results/t11_trainwith15.json create mode 100644 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create mode 100644 evals/webarena/results/t293_trainwith293.json create mode 100644 evals/webarena/results/t293_trainwith294.json create mode 100644 evals/webarena/results/t293_trainwith295.json create mode 100644 evals/webarena/results/t303_heldout305_base.json create mode 100644 evals/webarena/results/t303_heldout305_with.json create mode 100644 evals/webarena/results/t303_heldout306_base.json create mode 100644 evals/webarena/results/t303_heldout306_with.json create mode 100644 evals/webarena/results/t303_train303.json create mode 100644 evals/webarena/results/t303_train304.json create mode 100644 evals/webarena/results/t303_train307.json create mode 100644 evals/webarena/results/t303_trainwith303.json create mode 100644 evals/webarena/results/t303_trainwith304.json create mode 100644 evals/webarena/results/t303_trainwith307.json create mode 100755 evals/webarena/run_all.sh create mode 100644 tests/skills/test_eval_snapshot.py diff --git a/.github/workflows/skills-tests.yml b/.github/workflows/skills-tests.yml index a19f42b..642368e 100644 --- a/.github/workflows/skills-tests.yml +++ b/.github/workflows/skills-tests.yml @@ -1,9 +1,9 @@ name: skills-tests on: push: - paths: ["src/webwright/skills/**", "src/webwright/tools/skill_use.py", "tests/skills/**"] + paths: ["src/webwright/skills/**", "src/webwright/tools/skill_use.py", "tests/skills/**", "evals/webarena/**"] pull_request: - paths: ["src/webwright/skills/**", "src/webwright/tools/skill_use.py", "tests/skills/**"] + paths: ["src/webwright/skills/**", "src/webwright/tools/skill_use.py", "tests/skills/**", "evals/webarena/**"] jobs: unit: runs-on: ubuntu-latest diff --git a/evals/webarena/README.md b/evals/webarena/README.md new file mode 100644 index 0000000..8c2a2e2 --- /dev/null +++ b/evals/webarena/README.md @@ -0,0 +1,79 @@ +# Reproducing the WebArena numbers + +The skills README claims, for 10 task templates across 3 sites: + +``` + tasks correct accuracy avg steps +held-out WITH library 20 14 70.0% 14.7 +held-out from scratch 20 11 55.0% 17.1 +train WITH library 30 26 86.7% 13.7 +train from scratch 30 23 76.7% 15.9 +``` + +This directory contains everything needed to check those numbers — at two depths. + +## Depth 1: audit the recorded run (no setup, no cost) + +`results/` is the per-task record of the exact run behind the table: one JSON per solve +with the task id, the answer the agent produced, the gold score, the step count, and the +agent's skill verdict. Aggregate it yourself: + +```bash +python reproduce.py table --results results +``` + +That command computes the table above from the raw records — the module README rounds +86.7/76.7 down to 86/76. Spot-check any row against the public +[webarena-verified](https://github.com/microsoft/webarena-verified) dataset: the `tid` +field is the public task id, and the gold answers live in the dataset. + +## Depth 2: re-run the experiment (your WebArena, your model, real cost) + +You need: + +1. **This repo installed** — `pip install -e .` in a venv (playwright set up per the main README). +2. **A WebArena deployment** — the standard self-hosted sites (shopping_admin, gitlab, map). + Task prompts and scoring come from [webarena-verified](https://github.com/microsoft/webarena-verified); + follow its setup to get: + - the dataset json (`webarena-verified.json` — tasks + golds), + - your `verified_config.json` (maps site placeholders to **your** deployment URLs + credentials), + - a python env with the `webarena_verified` package (used for gold scoring; pass it via + `--eval-python` if it isn't the current interpreter). +3. **Model credentials** — `OPENAI_API_KEY`; on a custom OpenAI-compatible gateway also + `OPENAI_ENDPOINT` and `OPENAI_MODEL` (the skills module reads these), and set the same + endpoint/model for the *agent* in `model.eval.yaml`. The recorded run used one frontier + model for both; expect a few points of variance from model/version drift — LLM solving + is stochastic. The step counts and the WITH-vs-scratch *gap* are the stable signal. + +Then: + +```bash +export WEBARENA_DATASET=/path/to/webarena-verified.json +export WEBARENA_CONFIG=/path/to/your/verified_config.json +export OPENAI_API_KEY=... # + OPENAI_ENDPOINT / OPENAI_MODEL on a gateway + +python reproduce.py plan # prints all ~130 commands in dependency order +./run_all.sh # ...or runs them sequentially (hours; parallelize per template) +python reproduce.py table # the table, from YOUR runs +``` + +The flow per template: solve the 3 train tasks from scratch → `update` distills the +**gold-admitted** solves into one library skill (this is the gold gate the module README +refers to) → solve the 2 held-out tasks twice, once WITH the library and once from scratch, +and score both against gold. + +**Cost warning:** 100 agent solves plus library updates — expect several hours of wall +time sequentially and real API spend. `reproduce.py` is resumable: finished tasks are +skipped, so you can stop and restart, or run templates in parallel shells. + +## What's what + +| file | role | +|---|---| +| `reproduce.py` | self-contained driver: solve / update / heldout / table | +| `run_all.sh` | runs the whole plan sequentially | +| `model.eval.yaml` | eval overrides for the agent model (+ where to point a gateway) | +| `results/` | sanitized per-task records of the recorded run (the numbers' provenance) | + +`tests/skills/test_eval_snapshot.py` locks the snapshot to the published numbers in CI — +if the records and the README table ever disagree, the build fails. diff --git a/evals/webarena/model.eval.yaml b/evals/webarena/model.eval.yaml new file mode 100644 index 0000000..dbee826 --- /dev/null +++ b/evals/webarena/model.eval.yaml @@ -0,0 +1,17 @@ +# Eval overrides — stack LAST, after base.yaml and your model modifier: +# -c base.yaml -c model_openai.yaml -c evals/webarena/model.eval.yaml +# (reproduce.py adds this file automatically.) +# +# Running against a custom OpenAI-compatible gateway? Uncomment and fill these +# instead of editing model_openai.yaml: +# model: +# openai_endpoint: https://your-gateway.example.com/v1/responses +# model_name: your-model-name + +model: + # final_script.py is emitted in one bash_command; base.yaml's 4000 tokens + # truncates it and causes infinite format-retries + max_output_tokens: 16000 + # 16000-token generations can take minutes under concurrency; base.yaml's + # 120s request timeout aborts them mid-flight + request_timeout_seconds: 600 diff --git a/evals/webarena/reproduce.py b/evals/webarena/reproduce.py new file mode 100644 index 0000000..03d06e0 --- /dev/null +++ b/evals/webarena/reproduce.py @@ -0,0 +1,336 @@ +#!/usr/bin/env python3 +"""Reproduce the WebArena numbers in src/webwright/skills/README.md. + +The headline table (held-out: 70% vs 55% accuracy, 14.7 vs 17.1 steps; train: +26/30 vs 23/30) comes from 100 solves over 10 task templates x 3 sites. This +script re-runs that experiment against YOUR WebArena deployment: + + train 30 tasks solved from scratch, scored against gold + update gold-admitted train solves -> one library skill per template + heldout 20 unseen tasks, each solved twice: WITH the library and from scratch + (optional) trainwith: the 30 train tasks re-solved WITH the library + +Prerequisites (see README.md next to this file): + - webwright installed (this repo, `pip install -e .` in a venv) + - a WebArena deployment + microsoft/webarena-verified checked out: + * its dataset json (tasks + gold answers) + * your verified_config.json (maps site placeholders -> YOUR urls/credentials) + * a python env with the `webarena_verified` package (for gold scoring) + - env: OPENAI_API_KEY (and on a custom gateway OPENAI_ENDPOINT / OPENAI_MODEL, + used by the skills module); the agent's model is set via the -c yaml stack. + +Usage (one task at a time; each solve takes minutes — parallelize per template): + python reproduce.py plan # print every command to run + python reproduce.py train 279 # solve one train task + python reproduce.py update 279 # build the template's skill + python reproduce.py heldout 1 with|base # one held-out solve + python reproduce.py table [--results DIR] # aggregate -> the README table + +`table --results results/` prints the table from the checked-in snapshot of the +run behind the README numbers (no network needed). +""" +from __future__ import annotations + +import argparse +import glob +import json +import os +import subprocess +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent + +# 10 retrieve-type templates across 3 domains (shopping_admin / gitlab / map), all with +# non-null golds in webarena-verified. Key = representative task_id; value = (train, held-out). +SPLITS = { + # shopping_admin + 279: ([0, 3, 4], [1, 2]), # top-n best-selling entity in period + 198: ([198, 199, 203], [201, 202]), # attribute of the {status} order + 11: ([11, 14, 15], [12, 13]), # total number of reviews + 213: ([213, 214, 215], [216, 217]), # title+rating of all 3-star reviews + # gitlab + 132: ([132, 133, 136], [134, 135]), # commits by {user} to {repo} on {date} + 303: ([303, 304, 307], [305, 306]), # commits by {user} during {period} + 293: ([293, 294, 295], [296, 297]), # SSH clone URL of {repo} + # map + 151: ([151, 152, 153], [154, 155]), # min car travel time between two places + 16: ([16, 17, 18], [19, 20]), # walking+driving time for a route + 248: ([248, 249, 250], [251, 252]), # coordinates of {location} +} + +SITE_PLACEHOLDER = {"shopping": "__SHOPPING__", "shopping_admin": "__SHOPPING_ADMIN__", + "reddit": "__REDDIT__", "gitlab": "__GITLAB__"} + +ANSWER_SPEC = ( + "\n\n## Required final output\n" + "Additionally, write the final answer into $WORKSPACE_DIR/agent_response.json with the " + 'structure:\n{ "task_type": "RETRIEVE", "status": "SUCCESS|NOT_FOUND_ERROR", ' + '"retrieved_data": , "error_details": null }\n' + "retrieved_data is a list; if nothing matches, use NOT_FOUND_ERROR and null." +) + + +# ---------------------------------------------------------------- dataset / config +class Bench: + def __init__(self, dataset: Path, config: Path): + self.data = {t["task_id"]: t for t in json.loads(Path(dataset).read_text())} + self.conf = json.loads(Path(config).read_text()) + + def task(self, tid): + return self.data[tid] + + def resolve(self, url): + """Site placeholder -> the deployment url from YOUR verified_config.json.""" + for ph, ec in self.conf["environments"].items(): + urls = ec.get("urls", []) + if urls and url == ph: + return urls[0] + if urls and url.startswith(ph + "/"): + return urls[0].rstrip("/") + url[len(ph):] + return url + + def credentials(self, site): + ph = SITE_PLACEHOLDER.get(site) + return self.conf["environments"].get(ph, {}).get("credentials") + + def prompt(self, tid): + t = self.task(tid) + url = self.resolve(t["start_urls"][0]) if t["start_urls"] else "" + c = self.credentials(t["sites"][0]) + login = (f"\nIf login is required, use username `{c['username']}` " + f"password `{c['password']}`." if c else "") + return (f"Complete the following web task.\n\n## Task\nGoal: {t['intent']}\n\n" + f"Start URL: {url}{login}{ANSWER_SPEC}"), url + + +# ---------------------------------------------------------------- solve + collect +def collect(out_dir: Path, key: str) -> dict: + dirs = sorted(glob.glob(str(out_dir / f"{key}_*"))) + d = Path(dirs[-1]) if dirs else None + answer, steps, verdict = None, 0, None + if d: + arp = d / "agent_response.json" + if arp.exists(): + try: + answer = json.loads(arp.read_text()).get("retrieved_data") + except Exception: + pass + traj = d / "trajectory.json" + if traj.exists(): + try: + steps = int((json.loads(traj.read_text()).get("info") or {}).get("api_calls") or 0) + except Exception: + pass + sd = d / "skill_decision.json" + if sd.exists(): + try: + verdict = json.loads(sd.read_text()).get("verdict") + except Exception: + pass + return {"answer": answer, "steps": steps, "verdict": verdict, "dir": str(d) if d else None} + + +def solve(args, bench: Bench, tid: int, key: str, with_skills: bool) -> dict: + prompt, url = bench.prompt(tid) + if with_skills: + from webwright.skills import with_skill_hint + prompt = with_skill_hint(prompt, task=bench.task(tid)["intent"], + library=str(Path(args.out) / "library")) + runs = Path(args.out) / "runs" + runs.mkdir(parents=True, exist_ok=True) + cmd = [sys.executable, "-m", "webwright.run.cli", "main", "-t", prompt, + "--task-id", key, "--start-url", url, "-o", str(runs), + "-c", "base.yaml", "-c", args.model_config, "-c", str(HERE / "model.eval.yaml")] + for attempt in range(1, 3): # webwright occasionally stalls on step 1 — retry once + log = runs / f"{key}.log" + with log.open("a") as lf: + try: + subprocess.run(cmd, env=os.environ.copy(), stdout=lf, + stderr=subprocess.STDOUT, timeout=args.timeout) + except subprocess.TimeoutExpired: + lf.write("\nTIMEOUT\n") + res = collect(runs, key) + if res["answer"] is not None or res["steps"] > 0: + return res + print(f" [retry] attempt {attempt} hung (no answer, no steps)") + return res + + +# ---------------------------------------------------------------- gold scoring +GOLD_EVAL = r''' +import json, sys +from pathlib import Path +from webarena_verified.api import WebArenaVerified +tid = int(sys.argv[1]); vd = Path(sys.argv[2]); cfg = Path(sys.argv[3]) +har = vd / "network.har" # AgentResponseEvaluator ignores the trace; the API requires one +har.write_text(json.dumps({"log": {"version": "1.2", "creator": {"name": "d", "version": "1"}, + "entries": [{"request": {"method": "GET", "url": "http://dummy.local", "headers": [], + "queryString": [], "cookies": [], "bodySize": 0}, "response": {"status": 200, + "statusText": "OK", "headers": [], "cookies": [], "content": {"size": 0, + "mimeType": "text/plain"}, "bodySize": 0}, "cache": {}, "timings": + {"send": 0, "wait": 0, "receive": 0}}]}})) +try: + wa = WebArenaVerified(config=cfg) + r = wa.evaluate_task(task_id=tid, agent_response=vd / "agent_response.json", network_trace=har) + ag = [e for e in r.evaluators_results if e.evaluator_name == "AgentResponseEvaluator"] + print(json.dumps({"ag": ag[0].score if ag else r.score})) +except Exception as e: + print(json.dumps({"ag": None, "err": str(e)[:200]})) +''' + + +def gold_eval(args, tid: int, run_dir: str): + p = subprocess.run([args.eval_python, "-c", GOLD_EVAL, str(tid), run_dir, args.config], + capture_output=True, text=True) + try: + return json.loads(p.stdout.strip().splitlines()[-1]).get("ag") + except Exception: + return None + + +# ---------------------------------------------------------------- per-task records +def rec_path(args, key): + return Path(args.out) / "results" / f"{key}.json" + + +def run_task(args, bench, tid, key, with_skills): + p = rec_path(args, key) + if p.exists() and json.loads(p.read_text()).get("answer") is not None: + print(f"[resume] {key} already done") + return + res = solve(args, bench, tid, key, with_skills) + score = gold_eval(args, tid, res["dir"]) if res["dir"] else None + rec = {"tid": tid, "with_skills": with_skills, "answer": res["answer"], + "steps": res["steps"], "correct": score == 1.0, "score": score, + "params": bench.task(tid).get("instantiation_dict", {}), "verdict": res["verdict"], + "dir": res["dir"]} + p.parent.mkdir(parents=True, exist_ok=True) + p.write_text(json.dumps(rec, indent=2, ensure_ascii=False)) + print(f"[{key}] answer={json.dumps(res['answer'], ensure_ascii=False)[:80]} " + f"steps={res['steps']} correct={rec['correct']} verdict={res['verdict']}") + + +def template_of(tid): + for tmpl, (tr, te) in SPLITS.items(): + if tid in tr or tid in te: + return tmpl + raise SystemExit(f"task {tid} is not in SPLITS") + + +# ---------------------------------------------------------------- update (build skills) +def do_update(args, bench, tmpl): + """Gold-admitted train solves of this template -> manifest -> skills update CLI.""" + tr, _ = SPLITS[tmpl] + tmpl_str = bench.task(tr[0]).get("intent_template", "") + runs = [] + for tid in tr: + p = rec_path(args, f"t{tmpl}_train{tid}") + if not p.exists(): + continue + r = json.loads(p.read_text()) + if not r.get("correct") or not r.get("dir"): + continue + runs.append({"dir": r["dir"], "admit": True, "params": r.get("params", {}), + "verdict": "skip", "site": bench.task(tid)["sites"][0], + "output_schema": bench.task(tid)["eval"][0].get("results_schema")}) + print(f"template {tmpl}: {len(runs)} gold-admitted train solve(s)") + if not runs: + print("nothing admitted — run the train tasks first") + return + mf = Path(args.out) / f"manifest_t{tmpl}.json" + mf.write_text(json.dumps({"template": tmpl_str, "runs": runs}, indent=2, ensure_ascii=False)) + lib = Path(args.out) / "library" + p = subprocess.run([sys.executable, "-m", "webwright.skills.update", + "--manifest", str(mf), "--library", str(lib)], + env=os.environ.copy(), capture_output=True, text=True) + print(p.stdout.strip() or p.stderr[-600:]) + + +# ---------------------------------------------------------------- aggregate table +def table(results_dir: Path): + recs = {p.stem: json.loads(p.read_text()) for p in sorted(Path(results_dir).glob("*.json"))} + if not recs: + raise SystemExit(f"no per-task result files in {results_dir}") + + def agg(sel): + rs = [v for k, v in recs.items() if sel(k)] + if not rs: + return None + n = len(rs) + ok = sum(1 for r in rs if r["correct"]) + return {"n": n, "correct": ok, "acc": 100.0 * ok / n, + "steps": sum(r["steps"] for r in rs) / n} + + rows = [ + ("held-out WITH library", agg(lambda k: "heldout" in k and k.endswith("_with"))), + ("held-out from scratch", agg(lambda k: "heldout" in k and k.endswith("_base"))), + ("train WITH library", agg(lambda k: "trainwith" in k)), + ("train from scratch", agg(lambda k: "_train" in k and "trainwith" not in k)), + ] + print(f"{'':24} {'tasks':>5} {'correct':>7} {'accuracy':>8} {'avg steps':>9}") + for name, a in rows: + if a is None: + print(f"{name:24} (no runs yet)") + else: + print(f"{name:24} {a['n']:>5} {a['correct']:>7} {a['acc']:>7.1f}% {a['steps']:>9.1f}") + return rows + + +# ---------------------------------------------------------------- cli +def main(argv=None): + ap = argparse.ArgumentParser(description=__doc__.splitlines()[0]) + ap.add_argument("cmd", choices=["plan", "train", "trainwith", "update", "heldout", "table"]) + ap.add_argument("arg", nargs="?", help="task_id (train/heldout) or template id (update)") + ap.add_argument("mode", nargs="?", default="with", choices=["with", "base"], + help="heldout only: solve WITH the library or from scratch") + ap.add_argument("--dataset", default=os.environ.get("WEBARENA_DATASET", ""), + help="webarena-verified dataset json (or env WEBARENA_DATASET)") + ap.add_argument("--config", default=os.environ.get("WEBARENA_CONFIG", ""), + help="your verified_config.json with YOUR deployment urls (or env WEBARENA_CONFIG)") + ap.add_argument("--eval-python", default=sys.executable, + help="python that has the webarena_verified package (gold scoring)") + ap.add_argument("--model-config", default="model_openai.yaml", + help="model modifier yaml for the agent (stacked on base.yaml)") + ap.add_argument("--out", default="runs_eval", help="working dir for runs/results/library") + ap.add_argument("--results", default="", help="table only: aggregate this dir instead of --out") + ap.add_argument("--timeout", type=int, default=900) + args = ap.parse_args(argv) + + if args.cmd == "table": + table(Path(args.results) if args.results else Path(args.out) / "results") + return 0 + if args.cmd == "plan": + print("# run these (each solve takes minutes; parallelize per template):") + for tmpl, (tr, te) in SPLITS.items(): + for t in tr: + print(f"python reproduce.py train {t}") + print(f"python reproduce.py update {tmpl}") + for t in te: + print(f"python reproduce.py heldout {t} with") + print(f"python reproduce.py heldout {t} base") + # trainwith is optional — it feeds only the "train (seen tasks)" table + for t in tr: + print(f"python reproduce.py trainwith {t}") + print("python reproduce.py table") + return 0 + + if not args.dataset or not args.config: + raise SystemExit("need --dataset and --config (or WEBARENA_DATASET / WEBARENA_CONFIG env) — " + "see README.md for how to get both from microsoft/webarena-verified") + bench = Bench(Path(args.dataset), Path(args.config)) + tid = int(args.arg) + if args.cmd == "train": + run_task(args, bench, tid, f"t{template_of(tid)}_train{tid}", False) + elif args.cmd == "trainwith": + run_task(args, bench, tid, f"t{template_of(tid)}_trainwith{tid}", True) + elif args.cmd == "update": + do_update(args, bench, tid) + elif args.cmd == "heldout": + run_task(args, bench, tid, f"t{template_of(tid)}_heldout{tid}_{args.mode}", + args.mode == "with") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/evals/webarena/results/t11_heldout12_base.json b/evals/webarena/results/t11_heldout12_base.json new file mode 100644 index 0000000..2781ca1 --- /dev/null +++ b/evals/webarena/results/t11_heldout12_base.json @@ -0,0 +1,13 @@ +{ + "tid": 12, + "with_skills": false, + "answer": [ + 2 + ], + "steps": 12, + "correct": true, + "score": 1.0, + "params": { + "term": "satisfied" + } +} diff --git a/evals/webarena/results/t11_heldout12_with.json b/evals/webarena/results/t11_heldout12_with.json new file mode 100644 index 0000000..8fd9d18 --- /dev/null +++ b/evals/webarena/results/t11_heldout12_with.json @@ -0,0 +1,14 @@ +{ + "tid": 12, + "with_skills": true, + "answer": [ + 2 + ], + "steps": 8, + "correct": true, + "score": 1.0, + "params": { + "term": "satisfied" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t11_heldout13_base.json b/evals/webarena/results/t11_heldout13_base.json new file mode 100644 index 0000000..72f134b --- /dev/null +++ b/evals/webarena/results/t11_heldout13_base.json @@ -0,0 +1,13 @@ +{ + "tid": 13, + "with_skills": false, + "answer": [ + 2 + ], + "steps": 12, + "correct": true, + "score": 1.0, + "params": { + "term": "decent" + } +} diff --git a/evals/webarena/results/t11_heldout13_with.json b/evals/webarena/results/t11_heldout13_with.json new file mode 100644 index 0000000..0ea34c0 --- /dev/null +++ b/evals/webarena/results/t11_heldout13_with.json @@ -0,0 +1,14 @@ +{ + "tid": 13, + "with_skills": true, + "answer": [ + 2 + ], + "steps": 18, + "correct": true, + "score": 1.0, + "params": { + "term": "decent" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t11_train11.json b/evals/webarena/results/t11_train11.json new file mode 100644 index 0000000..87f56d8 --- /dev/null +++ b/evals/webarena/results/t11_train11.json @@ -0,0 +1,13 @@ +{ + "tid": 11, + "with_skills": false, + "answer": [ + 6 + ], + "steps": 13, + "correct": true, + "score": 1.0, + "params": { + "term": "disappointed" + } +} diff --git a/evals/webarena/results/t11_train14.json b/evals/webarena/results/t11_train14.json new file mode 100644 index 0000000..5944748 --- /dev/null +++ b/evals/webarena/results/t11_train14.json @@ -0,0 +1,11 @@ +{ + "tid": 14, + "with_skills": false, + "answer": null, + "steps": 14, + "correct": false, + "score": 0.0, + "params": { + "term": "not useful" + } +} diff --git a/evals/webarena/results/t11_train15.json b/evals/webarena/results/t11_train15.json new file mode 100644 index 0000000..b2abcf1 --- /dev/null +++ b/evals/webarena/results/t11_train15.json @@ -0,0 +1,13 @@ +{ + "tid": 15, + "with_skills": false, + "answer": [ + 2 + ], + "steps": 9, + "correct": true, + "score": 1.0, + "params": { + "term": "best" + } +} diff --git a/evals/webarena/results/t11_trainwith11.json b/evals/webarena/results/t11_trainwith11.json new file mode 100644 index 0000000..65cb728 --- /dev/null +++ b/evals/webarena/results/t11_trainwith11.json @@ -0,0 +1,14 @@ +{ + "tid": 11, + "with_skills": true, + "answer": [ + 6 + ], + "steps": 7, + "correct": true, + "score": 1.0, + "params": { + "term": "disappointed" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t11_trainwith14.json b/evals/webarena/results/t11_trainwith14.json new file mode 100644 index 0000000..fca2fc6 --- /dev/null +++ b/evals/webarena/results/t11_trainwith14.json @@ -0,0 +1,14 @@ +{ + "tid": 14, + "with_skills": true, + "answer": [ + 0 + ], + "steps": 13, + "correct": true, + "score": 1.0, + "params": { + "term": "not useful" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t11_trainwith15.json b/evals/webarena/results/t11_trainwith15.json new file mode 100644 index 0000000..bc32e31 --- /dev/null +++ b/evals/webarena/results/t11_trainwith15.json @@ -0,0 +1,14 @@ +{ + "tid": 15, + "with_skills": true, + "answer": [ + 2 + ], + "steps": 12, + "correct": true, + "score": 1.0, + "params": { + "term": "best" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t132_heldout134_base.json b/evals/webarena/results/t132_heldout134_base.json new file mode 100644 index 0000000..7352553 --- /dev/null +++ b/evals/webarena/results/t132_heldout134_base.json @@ -0,0 +1,15 @@ +{ + "tid": 134, + "with_skills": false, + "answer": [ + 0 + ], + "steps": 15, + "correct": true, + "score": 1.0, + "params": { + "user": "kilian", + "repo": "a11yproject.com", + "date": "March 1, 2023" + } +} diff --git a/evals/webarena/results/t132_heldout134_with.json b/evals/webarena/results/t132_heldout134_with.json new file mode 100644 index 0000000..08a3955 --- /dev/null +++ b/evals/webarena/results/t132_heldout134_with.json @@ -0,0 +1,16 @@ +{ + "tid": 134, + "with_skills": true, + "answer": [ + 0 + ], + "steps": 20, + "correct": true, + "score": 1.0, + "params": { + "user": "kilian", + "repo": "a11yproject.com", + "date": "March 1, 2023" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t132_heldout135_base.json b/evals/webarena/results/t132_heldout135_base.json new file mode 100644 index 0000000..639c72a --- /dev/null +++ b/evals/webarena/results/t132_heldout135_base.json @@ -0,0 +1,16 @@ +{ + "tid": 135, + "with_skills": false, + "answer": [ + "Eric: 1", + "Kilian: 0" + ], + "steps": 12, + "correct": false, + "score": 0.0, + "params": { + "user": "Eric and Kilian", + "repo": "a11yproject.com", + "date": "January 3, 2023" + } +} diff --git a/evals/webarena/results/t132_heldout135_with.json b/evals/webarena/results/t132_heldout135_with.json new file mode 100644 index 0000000..de0ad2f --- /dev/null +++ b/evals/webarena/results/t132_heldout135_with.json @@ -0,0 +1,19 @@ +{ + "tid": 135, + "with_skills": true, + "answer": [ + { + "Eric": 1, + "Kilian": 0 + } + ], + "steps": 15, + "correct": false, + "score": 0.0, + "params": { + "user": "Eric and Kilian", + "repo": "a11yproject.com", + "date": "January 3, 2023" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t132_train132.json b/evals/webarena/results/t132_train132.json new file mode 100644 index 0000000..638fa97 --- /dev/null +++ b/evals/webarena/results/t132_train132.json @@ -0,0 +1,15 @@ +{ + "tid": 132, + "with_skills": false, + "answer": [ + "1" + ], + "steps": 15, + "correct": true, + "score": 1.0, + "params": { + "user": "kilian", + "repo": "a11yproject.com", + "date": "March 5, 2023" + } +} diff --git a/evals/webarena/results/t132_train133.json b/evals/webarena/results/t132_train133.json new file mode 100644 index 0000000..ce9bb63 --- /dev/null +++ b/evals/webarena/results/t132_train133.json @@ -0,0 +1,15 @@ +{ + "tid": 133, + "with_skills": false, + "answer": [ + "2" + ], + "steps": 14, + "correct": true, + "score": 1.0, + "params": { + "user": "Eric", + "repo": "a11yproject.com", + "date": "March 2, 2023" + } +} diff --git a/evals/webarena/results/t132_train136.json b/evals/webarena/results/t132_train136.json new file mode 100644 index 0000000..95c1e45 --- /dev/null +++ b/evals/webarena/results/t132_train136.json @@ -0,0 +1,15 @@ +{ + "tid": 136, + "with_skills": false, + "answer": [ + "5" + ], + "steps": 20, + "correct": true, + "score": 1.0, + "params": { + "user": "Steven Woodson", + "repo": "a11y-webring.club", + "date": "February 6, 2023" + } +} diff --git a/evals/webarena/results/t132_trainwith132.json b/evals/webarena/results/t132_trainwith132.json new file mode 100644 index 0000000..6b8dfe4 --- /dev/null +++ b/evals/webarena/results/t132_trainwith132.json @@ -0,0 +1,16 @@ +{ + "tid": 132, + "with_skills": true, + "answer": [ + 1 + ], + "steps": 15, + "correct": true, + "score": 1.0, + "params": { + "user": "kilian", + "repo": "a11yproject.com", + "date": "March 5, 2023" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t132_trainwith133.json b/evals/webarena/results/t132_trainwith133.json new file mode 100644 index 0000000..8391f02 --- /dev/null +++ b/evals/webarena/results/t132_trainwith133.json @@ -0,0 +1,16 @@ +{ + "tid": 133, + "with_skills": true, + "answer": [ + 2 + ], + "steps": 14, + "correct": true, + "score": 1.0, + "params": { + "user": "Eric", + "repo": "a11yproject.com", + "date": "March 2, 2023" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t132_trainwith136.json b/evals/webarena/results/t132_trainwith136.json new file mode 100644 index 0000000..0f90db0 --- /dev/null +++ b/evals/webarena/results/t132_trainwith136.json @@ -0,0 +1,16 @@ +{ + "tid": 136, + "with_skills": true, + "answer": [ + 5 + ], + "steps": 15, + "correct": true, + "score": 1.0, + "params": { + "user": "Steven Woodson", + "repo": "a11y-webring.club", + "date": "February 6, 2023" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t151_heldout154_base.json b/evals/webarena/results/t151_heldout154_base.json new file mode 100644 index 0000000..99b76b2 --- /dev/null +++ b/evals/webarena/results/t151_heldout154_base.json @@ -0,0 +1,15 @@ +{ + "tid": 154, + "with_skills": false, + "answer": [ + "00:03:33" + ], + "steps": 29, + "correct": true, + "score": 1.0, + "params": { + "location1": "CMU gates building", + "location2": "Schenley park", + "retrieved_data_format_spec": "Return the value as a string in HH:MM:SS format only, without any additional details" + } +} diff --git a/evals/webarena/results/t151_heldout154_with.json b/evals/webarena/results/t151_heldout154_with.json new file mode 100644 index 0000000..568a621 --- /dev/null +++ b/evals/webarena/results/t151_heldout154_with.json @@ -0,0 +1,16 @@ +{ + "tid": 154, + "with_skills": true, + "answer": [ + "00:03:33" + ], + "steps": 16, + "correct": true, + "score": 1.0, + "params": { + "location1": "CMU gates building", + "location2": "Schenley park", + "retrieved_data_format_spec": "Return the value as a string in HH:MM:SS format only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t151_heldout155_base.json b/evals/webarena/results/t151_heldout155_base.json new file mode 100644 index 0000000..e8c3f52 --- /dev/null +++ b/evals/webarena/results/t151_heldout155_base.json @@ -0,0 +1,15 @@ +{ + "tid": 155, + "with_skills": false, + "answer": [ + "00:09:00" + ], + "steps": 10, + "correct": true, + "score": 1.0, + "params": { + "location1": "Animal Rescue League of Pittsburgh", + "location2": "Schenley park", + "retrieved_data_format_spec": "Return the value as a string in HH:MM:SS format only, without any additional details" + } +} diff --git a/evals/webarena/results/t151_heldout155_with.json b/evals/webarena/results/t151_heldout155_with.json new file mode 100644 index 0000000..a235655 --- /dev/null +++ b/evals/webarena/results/t151_heldout155_with.json @@ -0,0 +1,16 @@ +{ + "tid": 155, + "with_skills": true, + "answer": [ + "00:09:12" + ], + "steps": 21, + "correct": true, + "score": 1.0, + "params": { + "location1": "Animal Rescue League of Pittsburgh", + "location2": "Schenley park", + "retrieved_data_format_spec": "Return the value as a string in HH:MM:SS format only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t151_train151.json b/evals/webarena/results/t151_train151.json new file mode 100644 index 0000000..1ee82f0 --- /dev/null +++ b/evals/webarena/results/t151_train151.json @@ -0,0 +1,15 @@ +{ + "tid": 151, + "with_skills": false, + "answer": [ + "00:04:00" + ], + "steps": 12, + "correct": true, + "score": 1.0, + "params": { + "location1": "CMU", + "location2": "University of Pittsburgh", + "retrieved_data_format_spec": "Return the value as a string in HH:MM:SS format only, without any additional details" + } +} diff --git a/evals/webarena/results/t151_train152.json b/evals/webarena/results/t151_train152.json new file mode 100644 index 0000000..db84da8 --- /dev/null +++ b/evals/webarena/results/t151_train152.json @@ -0,0 +1,15 @@ +{ + "tid": 152, + "with_skills": false, + "answer": [ + "00:04:11" + ], + "steps": 13, + "correct": true, + "score": 1.0, + "params": { + "location1": "Schenley park", + "location2": "Upitt", + "retrieved_data_format_spec": "Return the value as a string in HH:MM:SS format only, without any additional details" + } +} diff --git a/evals/webarena/results/t151_train153.json b/evals/webarena/results/t151_train153.json new file mode 100644 index 0000000..745f2db --- /dev/null +++ b/evals/webarena/results/t151_train153.json @@ -0,0 +1,15 @@ +{ + "tid": 153, + "with_skills": false, + "answer": [ + "07:19:00" + ], + "steps": 8, + "correct": false, + "score": 0.0, + "params": { + "location1": "REI", + "location2": "CMU", + "retrieved_data_format_spec": "Return the value as a string in HH:MM:SS format only, without any additional details" + } +} diff --git a/evals/webarena/results/t151_trainwith151.json b/evals/webarena/results/t151_trainwith151.json new file mode 100644 index 0000000..69c2963 --- /dev/null +++ b/evals/webarena/results/t151_trainwith151.json @@ -0,0 +1,16 @@ +{ + "tid": 151, + "with_skills": true, + "answer": [ + "00:03:50" + ], + "steps": 14, + "correct": true, + "score": 1.0, + "params": { + "location1": "CMU", + "location2": "University of Pittsburgh", + "retrieved_data_format_spec": "Return the value as a string in HH:MM:SS format only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t151_trainwith152.json b/evals/webarena/results/t151_trainwith152.json new file mode 100644 index 0000000..cf2f246 --- /dev/null +++ b/evals/webarena/results/t151_trainwith152.json @@ -0,0 +1,16 @@ +{ + "tid": 152, + "with_skills": true, + "answer": [ + "00:04:11" + ], + "steps": 11, + "correct": true, + "score": 1.0, + "params": { + "location1": "Schenley park", + "location2": "Upitt", + "retrieved_data_format_spec": "Return the value as a string in HH:MM:SS format only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t151_trainwith153.json b/evals/webarena/results/t151_trainwith153.json new file mode 100644 index 0000000..5119d20 --- /dev/null +++ b/evals/webarena/results/t151_trainwith153.json @@ -0,0 +1,16 @@ +{ + "tid": 153, + "with_skills": true, + "answer": [ + "07:18:37" + ], + "steps": 21, + "correct": false, + "score": 0.0, + "params": { + "location1": "REI", + "location2": "CMU", + "retrieved_data_format_spec": "Return the value as a string in HH:MM:SS format only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t16_heldout19_base.json b/evals/webarena/results/t16_heldout19_base.json new file mode 100644 index 0000000..5e8fcf9 --- /dev/null +++ b/evals/webarena/results/t16_heldout19_base.json @@ -0,0 +1,13 @@ +{ + "tid": 19, + "with_skills": false, + "answer": null, + "steps": 21, + "correct": false, + "score": 0.0, + "params": { + "start": "Carnegie Science Center", + "end": "Carnegie Mellon University", + "retrieved_data_format_spec": "Return a list of objects with keys \"mode\" (driving or walking) and \"duration\" (in HH:MM:SS format) only, without any additional details" + } +} diff --git a/evals/webarena/results/t16_heldout19_with.json b/evals/webarena/results/t16_heldout19_with.json new file mode 100644 index 0000000..d893cdc --- /dev/null +++ b/evals/webarena/results/t16_heldout19_with.json @@ -0,0 +1,23 @@ +{ + "tid": 19, + "with_skills": true, + "answer": [ + { + "mode": "driving", + "duration": "00:12:00" + }, + { + "mode": "walking", + "duration": "01:44:00" + } + ], + "steps": 8, + "correct": true, + "score": 1.0, + "params": { + "start": "Carnegie Science Center", + "end": "Carnegie Mellon University", + "retrieved_data_format_spec": "Return a list of objects with keys \"mode\" (driving or walking) and \"duration\" (in HH:MM:SS format) only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t16_heldout20_base.json b/evals/webarena/results/t16_heldout20_base.json new file mode 100644 index 0000000..20587a7 --- /dev/null +++ b/evals/webarena/results/t16_heldout20_base.json @@ -0,0 +1,22 @@ +{ + "tid": 20, + "with_skills": false, + "answer": [ + { + "mode": "driving", + "duration": "00:12:42" + }, + { + "mode": "walking", + "duration": "00:12:42" + } + ], + "steps": 19, + "correct": false, + "score": 0.0, + "params": { + "start": "Randyland", + "end": "Carnegie Mellon University", + "retrieved_data_format_spec": "Return a list of objects with keys \"mode\" (driving or walking) and \"duration\" (in HH:MM:SS format) only, without any additional details" + } +} diff --git a/evals/webarena/results/t16_heldout20_with.json b/evals/webarena/results/t16_heldout20_with.json new file mode 100644 index 0000000..e0bc04d --- /dev/null +++ b/evals/webarena/results/t16_heldout20_with.json @@ -0,0 +1,23 @@ +{ + "tid": 20, + "with_skills": true, + "answer": [ + { + "mode": "driving", + "duration": "00:13:00" + }, + { + "mode": "walking", + "duration": "01:45:00" + } + ], + "steps": 10, + "correct": true, + "score": 1.0, + "params": { + "start": "Randyland", + "end": "Carnegie Mellon University", + "retrieved_data_format_spec": "Return a list of objects with keys \"mode\" (driving or walking) and \"duration\" (in HH:MM:SS format) only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t16_train16.json b/evals/webarena/results/t16_train16.json new file mode 100644 index 0000000..b6db868 --- /dev/null +++ b/evals/webarena/results/t16_train16.json @@ -0,0 +1,22 @@ +{ + "tid": 16, + "with_skills": false, + "answer": [ + { + "mode": "driving", + "duration": "00:02:00" + }, + { + "mode": "walking", + "duration": "00:16:00" + } + ], + "steps": 10, + "correct": true, + "score": 1.0, + "params": { + "start": "5000 Fifth Avenue, Pittsburgh", + "end": "UPMC family health center", + "retrieved_data_format_spec": "Return a list of objects with keys \"mode\" (driving or walking) and \"duration\" (in HH:MM:SS format) only, without any additional details" + } +} diff --git a/evals/webarena/results/t16_train17.json b/evals/webarena/results/t16_train17.json new file mode 100644 index 0000000..9cfe39d --- /dev/null +++ b/evals/webarena/results/t16_train17.json @@ -0,0 +1,22 @@ +{ + "tid": 17, + "with_skills": false, + "answer": [ + { + "mode": "driving", + "duration": "00:13:00" + }, + { + "mode": "walking", + "duration": "01:35:00" + } + ], + "steps": 11, + "correct": true, + "score": 1.0, + "params": { + "start": "AMC Waterfront", + "end": "Carnegie Mellon University", + "retrieved_data_format_spec": "Return a list of objects with keys \"mode\" (driving or walking) and \"duration\" (in HH:MM:SS format) only, without any additional details" + } +} diff --git a/evals/webarena/results/t16_train18.json b/evals/webarena/results/t16_train18.json new file mode 100644 index 0000000..a491eea --- /dev/null +++ b/evals/webarena/results/t16_train18.json @@ -0,0 +1,22 @@ +{ + "tid": 18, + "with_skills": false, + "answer": [ + { + "mode": "driving", + "duration": "00:15:00" + }, + { + "mode": "walking", + "duration": "01:47:00" + } + ], + "steps": 15, + "correct": false, + "score": 0.0, + "params": { + "start": "AMC Waterfront", + "end": "Univ of Pittsburgh", + "retrieved_data_format_spec": "Return a list of objects with keys \"mode\" (driving or walking) and \"duration\" (in HH:MM:SS format) only, without any additional details" + } +} diff --git a/evals/webarena/results/t16_trainwith16.json b/evals/webarena/results/t16_trainwith16.json new file mode 100644 index 0000000..6304e30 --- /dev/null +++ b/evals/webarena/results/t16_trainwith16.json @@ -0,0 +1,23 @@ +{ + "tid": 16, + "with_skills": true, + "answer": [ + { + "mode": "driving", + "duration": "00:02:00" + }, + { + "mode": "walking", + "duration": "00:16:00" + } + ], + "steps": 10, + "correct": true, + "score": 1.0, + "params": { + "start": "5000 Fifth Avenue, Pittsburgh", + "end": "UPMC family health center", + "retrieved_data_format_spec": "Return a list of objects with keys \"mode\" (driving or walking) and \"duration\" (in HH:MM:SS format) only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t16_trainwith17.json b/evals/webarena/results/t16_trainwith17.json new file mode 100644 index 0000000..2c47267 --- /dev/null +++ b/evals/webarena/results/t16_trainwith17.json @@ -0,0 +1,23 @@ +{ + "tid": 17, + "with_skills": true, + "answer": [ + { + "mode": "driving", + "duration": "00:13:00" + }, + { + "mode": "walking", + "duration": "01:35:00" + } + ], + "steps": 9, + "correct": true, + "score": 1.0, + "params": { + "start": "AMC Waterfront", + "end": "Carnegie Mellon University", + "retrieved_data_format_spec": "Return a list of objects with keys \"mode\" (driving or walking) and \"duration\" (in HH:MM:SS format) only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t16_trainwith18.json b/evals/webarena/results/t16_trainwith18.json new file mode 100644 index 0000000..9626b98 --- /dev/null +++ b/evals/webarena/results/t16_trainwith18.json @@ -0,0 +1,23 @@ +{ + "tid": 18, + "with_skills": true, + "answer": [ + { + "mode": "driving", + "duration": "00:15:00" + }, + { + "mode": "walking", + "duration": "01:47:00" + } + ], + "steps": 11, + "correct": false, + "score": 0.0, + "params": { + "start": "AMC Waterfront", + "end": "Univ of Pittsburgh", + "retrieved_data_format_spec": "Return a list of objects with keys \"mode\" (driving or walking) and \"duration\" (in HH:MM:SS format) only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t198_heldout201_base.json b/evals/webarena/results/t198_heldout201_base.json new file mode 100644 index 0000000..0891ed5 --- /dev/null +++ b/evals/webarena/results/t198_heldout201_base.json @@ -0,0 +1,13 @@ +{ + "tid": 201, + "with_skills": false, + "answer": null, + "steps": 12, + "correct": false, + "score": 0.0, + "params": { + "attribute": "customer email", + "status": "earliest fraud suspect", + "retrieved_data_format_spec": "" + } +} diff --git a/evals/webarena/results/t198_heldout201_with.json b/evals/webarena/results/t198_heldout201_with.json new file mode 100644 index 0000000..e27f74a --- /dev/null +++ b/evals/webarena/results/t198_heldout201_with.json @@ -0,0 +1,14 @@ +{ + "tid": 201, + "with_skills": true, + "answer": null, + "steps": 15, + "correct": false, + "score": 0.0, + "params": { + "attribute": "customer email", + "status": "earliest fraud suspect", + "retrieved_data_format_spec": "" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t198_heldout202_base.json b/evals/webarena/results/t198_heldout202_base.json new file mode 100644 index 0000000..f6b545f --- /dev/null +++ b/evals/webarena/results/t198_heldout202_base.json @@ -0,0 +1,15 @@ +{ + "tid": 202, + "with_skills": false, + "answer": [ + "2023-05-23" + ], + "steps": 8, + "correct": true, + "score": 1.0, + "params": { + "attribute": "date", + "status": "most recent cancelled", + "retrieved_data_format_spec": "Return the date in YYYY-MM-DD format or null if not available, without any additional details" + } +} diff --git a/evals/webarena/results/t198_heldout202_with.json b/evals/webarena/results/t198_heldout202_with.json new file mode 100644 index 0000000..5e3a367 --- /dev/null +++ b/evals/webarena/results/t198_heldout202_with.json @@ -0,0 +1,16 @@ +{ + "tid": 202, + "with_skills": true, + "answer": [ + "2022-11-30" + ], + "steps": 25, + "correct": false, + "score": 0.0, + "params": { + "attribute": "date", + "status": "most recent cancelled", + "retrieved_data_format_spec": "Return the date in YYYY-MM-DD format or null if not available, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t198_train198.json b/evals/webarena/results/t198_train198.json new file mode 100644 index 0000000..91b885c --- /dev/null +++ b/evals/webarena/results/t198_train198.json @@ -0,0 +1,15 @@ +{ + "tid": 198, + "with_skills": false, + "answer": [ + "harrypotterfan1@gmail.com" + ], + "steps": 29, + "correct": true, + "score": 1.0, + "params": { + "attribute": "customer email", + "status": "most recent cancelled", + "retrieved_data_format_spec": "" + } +} diff --git a/evals/webarena/results/t198_train199.json b/evals/webarena/results/t198_train199.json new file mode 100644 index 0000000..8b58506 --- /dev/null +++ b/evals/webarena/results/t198_train199.json @@ -0,0 +1,15 @@ +{ + "tid": 199, + "with_skills": false, + "answer": [ + "000000299" + ], + "steps": 16, + "correct": true, + "score": 1.0, + "params": { + "attribute": "order ID", + "status": "newest pending", + "retrieved_data_format_spec": "" + } +} diff --git a/evals/webarena/results/t198_train203.json b/evals/webarena/results/t198_train203.json new file mode 100644 index 0000000..0910c43 --- /dev/null +++ b/evals/webarena/results/t198_train203.json @@ -0,0 +1,18 @@ +{ + "tid": 203, + "with_skills": false, + "answer": [ + { + "purchase_date": "2023-05-31", + "order_id": "000000299" + } + ], + "steps": 17, + "correct": false, + "score": 0.0, + "params": { + "attribute": "purchase date and order id", + "status": "most recent pending", + "retrieved_data_format_spec": "Return a list of objects with keys \"purchase_date\" (YYYY-MM-DD format or null if not available) and \"order_id\", without any additional details" + } +} diff --git a/evals/webarena/results/t198_trainwith198.json b/evals/webarena/results/t198_trainwith198.json new file mode 100644 index 0000000..dd5343f --- /dev/null +++ b/evals/webarena/results/t198_trainwith198.json @@ -0,0 +1,16 @@ +{ + "tid": 198, + "with_skills": true, + "answer": [ + "harrypotterfan1@gmail.com" + ], + "steps": 17, + "correct": true, + "score": 1.0, + "params": { + "attribute": "customer email", + "status": "most recent cancelled", + "retrieved_data_format_spec": "" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t198_trainwith199.json b/evals/webarena/results/t198_trainwith199.json new file mode 100644 index 0000000..24358e0 --- /dev/null +++ b/evals/webarena/results/t198_trainwith199.json @@ -0,0 +1,16 @@ +{ + "tid": 199, + "with_skills": true, + "answer": [ + "000000299" + ], + "steps": 19, + "correct": true, + "score": 1.0, + "params": { + "attribute": "order ID", + "status": "newest pending", + "retrieved_data_format_spec": "" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t198_trainwith203.json b/evals/webarena/results/t198_trainwith203.json new file mode 100644 index 0000000..81faa26 --- /dev/null +++ b/evals/webarena/results/t198_trainwith203.json @@ -0,0 +1,19 @@ +{ + "tid": 203, + "with_skills": true, + "answer": [ + { + "purchase_date": "2023-05-31", + "order_id": "000000299" + } + ], + "steps": 15, + "correct": false, + "score": 0.0, + "params": { + "attribute": "purchase date and order id", + "status": "most recent pending", + "retrieved_data_format_spec": "Return a list of objects with keys \"purchase_date\" (YYYY-MM-DD format or null if not available) and \"order_id\", without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t213_heldout216_base.json b/evals/webarena/results/t213_heldout216_base.json new file mode 100644 index 0000000..5e3a11e --- /dev/null +++ b/evals/webarena/results/t213_heldout216_base.json @@ -0,0 +1,17 @@ +{ + "tid": 216, + "with_skills": false, + "answer": [ + { + "title": "Not exactly true to size", + "rating": 3 + } + ], + "steps": 14, + "correct": true, + "score": 1.0, + "params": { + "product": "Electra Bra Top", + "retrieved_data_format_spec": "Return a list of objects with keys \"title\" and \"rating\"" + } +} diff --git a/evals/webarena/results/t213_heldout216_with.json b/evals/webarena/results/t213_heldout216_with.json new file mode 100644 index 0000000..d4ab02f --- /dev/null +++ b/evals/webarena/results/t213_heldout216_with.json @@ -0,0 +1,18 @@ +{ + "tid": 216, + "with_skills": true, + "answer": [ + { + "title": "Not exactly true to size", + "rating": "3" + } + ], + "steps": 12, + "correct": true, + "score": 1.0, + "params": { + "product": "Electra Bra Top", + "retrieved_data_format_spec": "Return a list of objects with keys \"title\" and \"rating\"" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t213_heldout217_base.json b/evals/webarena/results/t213_heldout217_base.json new file mode 100644 index 0000000..518c009 --- /dev/null +++ b/evals/webarena/results/t213_heldout217_base.json @@ -0,0 +1,12 @@ +{ + "tid": 217, + "with_skills": false, + "answer": null, + "steps": 31, + "correct": false, + "score": 0.0, + "params": { + "product": "Pursuit Tone Band", + "retrieved_data_format_spec": "Return a list of objects with keys \"title\" and \"rating\"" + } +} diff --git a/evals/webarena/results/t213_heldout217_with.json b/evals/webarena/results/t213_heldout217_with.json new file mode 100644 index 0000000..dfb016f --- /dev/null +++ b/evals/webarena/results/t213_heldout217_with.json @@ -0,0 +1,22 @@ +{ + "tid": 217, + "with_skills": true, + "answer": [ + { + "title": "Agreed. More resistance", + "rating": "3" + }, + { + "title": "Want more resistance", + "rating": "3" + } + ], + "steps": 13, + "correct": true, + "score": 1.0, + "params": { + "product": "Pursuit Tone Band", + "retrieved_data_format_spec": "Return a list of objects with keys \"title\" and \"rating\"" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t213_train213.json b/evals/webarena/results/t213_train213.json new file mode 100644 index 0000000..47a341e --- /dev/null +++ b/evals/webarena/results/t213_train213.json @@ -0,0 +1,21 @@ +{ + "tid": 213, + "with_skills": false, + "answer": [ + { + "title": "Not for high impact", + "rating": 3 + }, + { + "title": "Zero support/modesty", + "rating": 2 + } + ], + "steps": 15, + "correct": true, + "score": 1.0, + "params": { + "product": "Antonia Racer Tank", + "retrieved_data_format_spec": "Return a list of objects with keys \"title\" and \"rating\"" + } +} diff --git a/evals/webarena/results/t213_train214.json b/evals/webarena/results/t213_train214.json new file mode 100644 index 0000000..c6a39e7 --- /dev/null +++ b/evals/webarena/results/t213_train214.json @@ -0,0 +1,21 @@ +{ + "tid": 214, + "with_skills": false, + "answer": [ + { + "title": "Doesn't fit me. Luma fail.", + "rating": 2 + }, + { + "title": "does not fit. worthless.", + "rating": 1 + } + ], + "steps": 21, + "correct": true, + "score": 1.0, + "params": { + "product": "Erica Sports Bra", + "retrieved_data_format_spec": "Return a list of objects with keys \"title\" and \"rating\"" + } +} diff --git a/evals/webarena/results/t213_train215.json b/evals/webarena/results/t213_train215.json new file mode 100644 index 0000000..dbb52a9 --- /dev/null +++ b/evals/webarena/results/t213_train215.json @@ -0,0 +1,17 @@ +{ + "tid": 215, + "with_skills": false, + "answer": [ + { + "title": "Bad!", + "rating": 1 + } + ], + "steps": 21, + "correct": true, + "score": 1.0, + "params": { + "product": "Circe ice fleece", + "retrieved_data_format_spec": "Return a list of objects with keys \"title\" and \"rating\"" + } +} diff --git a/evals/webarena/results/t213_trainwith213.json b/evals/webarena/results/t213_trainwith213.json new file mode 100644 index 0000000..c7ba14f --- /dev/null +++ b/evals/webarena/results/t213_trainwith213.json @@ -0,0 +1,22 @@ +{ + "tid": 213, + "with_skills": true, + "answer": [ + { + "title": "Zero support/modesty", + "rating": "2" + }, + { + "title": "Not for high impact", + "rating": "3" + } + ], + "steps": 12, + "correct": true, + "score": 1.0, + "params": { + "product": "Antonia Racer Tank", + "retrieved_data_format_spec": "Return a list of objects with keys \"title\" and \"rating\"" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t213_trainwith214.json b/evals/webarena/results/t213_trainwith214.json new file mode 100644 index 0000000..495fb55 --- /dev/null +++ b/evals/webarena/results/t213_trainwith214.json @@ -0,0 +1,22 @@ +{ + "tid": 214, + "with_skills": true, + "answer": [ + { + "title": "Doesn't fit me. Luma fail.", + "rating": "2" + }, + { + "title": "does not fit. worthless.", + "rating": "1" + } + ], + "steps": 20, + "correct": true, + "score": 1.0, + "params": { + "product": "Erica Sports Bra", + "retrieved_data_format_spec": "Return a list of objects with keys \"title\" and \"rating\"" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t213_trainwith215.json b/evals/webarena/results/t213_trainwith215.json new file mode 100644 index 0000000..c2d9de4 --- /dev/null +++ b/evals/webarena/results/t213_trainwith215.json @@ -0,0 +1,18 @@ +{ + "tid": 215, + "with_skills": true, + "answer": [ + { + "title": "Bad!", + "rating": "1" + } + ], + "steps": 11, + "correct": true, + "score": 1.0, + "params": { + "product": "Circe ice fleece", + "retrieved_data_format_spec": "Return a list of objects with keys \"title\" and \"rating\"" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t248_heldout251_base.json b/evals/webarena/results/t248_heldout251_base.json new file mode 100644 index 0000000..33da838 --- /dev/null +++ b/evals/webarena/results/t248_heldout251_base.json @@ -0,0 +1,17 @@ +{ + "tid": 251, + "with_skills": false, + "answer": [ + { + "latitude": 40.44393, + "longitude": -79.9499 + } + ], + "steps": 27, + "correct": false, + "score": 0.0, + "params": { + "location": "bus stop on the Carnegie art museum side of the street near CMU", + "retrieved_data_format_spec": "Return an object with keys \"latitude\" and \"longitude\" (as decimal degrees) only, without any additional details" + } +} diff --git a/evals/webarena/results/t248_heldout251_with.json b/evals/webarena/results/t248_heldout251_with.json new file mode 100644 index 0000000..98b24be --- /dev/null +++ b/evals/webarena/results/t248_heldout251_with.json @@ -0,0 +1,18 @@ +{ + "tid": 251, + "with_skills": true, + "answer": [ + { + "latitude": 40.4444415, + "longitude": -79.9424875 + } + ], + "steps": 18, + "correct": false, + "score": 0.0, + "params": { + "location": "bus stop on the Carnegie art museum side of the street near CMU", + "retrieved_data_format_spec": "Return an object with keys \"latitude\" and \"longitude\" (as decimal degrees) only, without any additional details" + }, + "verdict": "adapt" +} diff --git a/evals/webarena/results/t248_heldout252_base.json b/evals/webarena/results/t248_heldout252_base.json new file mode 100644 index 0000000..0a2a239 --- /dev/null +++ b/evals/webarena/results/t248_heldout252_base.json @@ -0,0 +1,17 @@ +{ + "tid": 252, + "with_skills": false, + "answer": [ + { + "latitude": 40.4576, + "longitude": -79.92933 + } + ], + "steps": 12, + "correct": false, + "score": 0.0, + "params": { + "location": "Tokyo Japanese Food Store in Pittsburgh", + "retrieved_data_format_spec": "Return an object with keys \"latitude\" and \"longitude\" (as decimal degrees) only, without any additional details" + } +} diff --git a/evals/webarena/results/t248_heldout252_with.json b/evals/webarena/results/t248_heldout252_with.json new file mode 100644 index 0000000..028e8cc --- /dev/null +++ b/evals/webarena/results/t248_heldout252_with.json @@ -0,0 +1,18 @@ +{ + "tid": 252, + "with_skills": true, + "answer": [ + { + "latitude": 40.45760025, + "longitude": -79.92932994166013 + } + ], + "steps": 14, + "correct": false, + "score": 0.0, + "params": { + "location": "Tokyo Japanese Food Store in Pittsburgh", + "retrieved_data_format_spec": "Return an object with keys \"latitude\" and \"longitude\" (as decimal degrees) only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t248_train248.json b/evals/webarena/results/t248_train248.json new file mode 100644 index 0000000..6f14af5 --- /dev/null +++ b/evals/webarena/results/t248_train248.json @@ -0,0 +1,17 @@ +{ + "tid": 248, + "with_skills": false, + "answer": [ + { + "latitude": 40.4424191, + "longitude": -79.9397388 + } + ], + "steps": 18, + "correct": true, + "score": 1.0, + "params": { + "location": "Carnegie Mellon Café", + "retrieved_data_format_spec": "Return an object with keys \"latitude\" and \"longitude\" (as decimal degrees) only, without any additional details" + } +} diff --git a/evals/webarena/results/t248_train249.json b/evals/webarena/results/t248_train249.json new file mode 100644 index 0000000..7e7f507 --- /dev/null +++ b/evals/webarena/results/t248_train249.json @@ -0,0 +1,17 @@ +{ + "tid": 249, + "with_skills": false, + "answer": [ + { + "latitude": 40.460764749999996, + "longitude": -79.94666653269825 + } + ], + "steps": 21, + "correct": true, + "score": 1.0, + "params": { + "location": "Western Pennsylvania Hospital Heliport", + "retrieved_data_format_spec": "Return an object with keys \"latitude\" and \"longitude\" (as decimal degrees) only, without any additional details" + } +} diff --git a/evals/webarena/results/t248_train250.json b/evals/webarena/results/t248_train250.json new file mode 100644 index 0000000..a67d80b --- /dev/null +++ b/evals/webarena/results/t248_train250.json @@ -0,0 +1,17 @@ +{ + "tid": 250, + "with_skills": false, + "answer": [ + { + "latitude": 40.45117, + "longitude": -79.93342 + } + ], + "steps": 12, + "correct": true, + "score": 1.0, + "params": { + "location": "Apple Store near Pitt", + "retrieved_data_format_spec": "Return an object with keys \"latitude\" and \"longitude\" (as decimal degrees) only, without any additional details" + } +} diff --git a/evals/webarena/results/t248_trainwith248.json b/evals/webarena/results/t248_trainwith248.json new file mode 100644 index 0000000..e043e41 --- /dev/null +++ b/evals/webarena/results/t248_trainwith248.json @@ -0,0 +1,18 @@ +{ + "tid": 248, + "with_skills": true, + "answer": [ + { + "latitude": 40.4424191, + "longitude": -79.9397388 + } + ], + "steps": 17, + "correct": true, + "score": 1.0, + "params": { + "location": "Carnegie Mellon Café", + "retrieved_data_format_spec": "Return an object with keys \"latitude\" and \"longitude\" (as decimal degrees) only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t248_trainwith249.json b/evals/webarena/results/t248_trainwith249.json new file mode 100644 index 0000000..f594b5a --- /dev/null +++ b/evals/webarena/results/t248_trainwith249.json @@ -0,0 +1,18 @@ +{ + "tid": 249, + "with_skills": true, + "answer": [ + { + "latitude": 40.460764749999996, + "longitude": -79.94666653269825 + } + ], + "steps": 18, + "correct": true, + "score": 1.0, + "params": { + "location": "Western Pennsylvania Hospital Heliport", + "retrieved_data_format_spec": "Return an object with keys \"latitude\" and \"longitude\" (as decimal degrees) only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t248_trainwith250.json b/evals/webarena/results/t248_trainwith250.json new file mode 100644 index 0000000..39adb0b --- /dev/null +++ b/evals/webarena/results/t248_trainwith250.json @@ -0,0 +1,18 @@ +{ + "tid": 250, + "with_skills": true, + "answer": [ + { + "latitude": 40.4511693, + "longitude": -79.9334241 + } + ], + "steps": 14, + "correct": true, + "score": 1.0, + "params": { + "location": "Apple Store near Pitt", + "retrieved_data_format_spec": "Return an object with keys \"latitude\" and \"longitude\" (as decimal degrees) only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t279_heldout1_base.json b/evals/webarena/results/t279_heldout1_base.json new file mode 100644 index 0000000..cf15594 --- /dev/null +++ b/evals/webarena/results/t279_heldout1_base.json @@ -0,0 +1,13 @@ +{ + "tid": 1, + "with_skills": false, + "answer": null, + "steps": 11, + "correct": false, + "score": 0.0, + "params": { + "n": 1, + "period": "Quarter 1 2022", + "entity": "brand name(s)" + } +} diff --git a/evals/webarena/results/t279_heldout1_with.json b/evals/webarena/results/t279_heldout1_with.json new file mode 100644 index 0000000..a2d13ce --- /dev/null +++ b/evals/webarena/results/t279_heldout1_with.json @@ -0,0 +1,16 @@ +{ + "tid": 1, + "with_skills": true, + "answer": [ + "Sprite" + ], + "steps": 20, + "correct": true, + "score": 1.0, + "params": { + "n": 1, + "period": "Quarter 1 2022", + "entity": "brand name(s)" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t279_heldout2_base.json b/evals/webarena/results/t279_heldout2_base.json new file mode 100644 index 0000000..41a7a75 --- /dev/null +++ b/evals/webarena/results/t279_heldout2_base.json @@ -0,0 +1,13 @@ +{ + "tid": 2, + "with_skills": false, + "answer": null, + "steps": 17, + "correct": false, + "score": 0.0, + "params": { + "n": 1, + "period": "Quarter 1 2022", + "entity": "product type name(s)" + } +} diff --git a/evals/webarena/results/t279_heldout2_with.json b/evals/webarena/results/t279_heldout2_with.json new file mode 100644 index 0000000..289ad8a --- /dev/null +++ b/evals/webarena/results/t279_heldout2_with.json @@ -0,0 +1,14 @@ +{ + "tid": 2, + "with_skills": true, + "answer": null, + "steps": 13, + "correct": false, + "score": 0.0, + "params": { + "n": 1, + "period": "Quarter 1 2022", + "entity": "product type name(s)" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t279_train0.json b/evals/webarena/results/t279_train0.json new file mode 100644 index 0000000..b4901f6 --- /dev/null +++ b/evals/webarena/results/t279_train0.json @@ -0,0 +1,17 @@ +{ + "tid": 0, + "with_skills": false, + "answer": [ + "Sprite Stasis Ball 55 cm", + "Sprite Stasis Ball 65 cm", + "Sprite Stasis Ball 75 cm" + ], + "steps": 24, + "correct": false, + "score": 0.0, + "params": { + "n": 1, + "entity": "product name(s)", + "period": "2022" + } +} diff --git a/evals/webarena/results/t279_train3.json b/evals/webarena/results/t279_train3.json new file mode 100644 index 0000000..c54f982 --- /dev/null +++ b/evals/webarena/results/t279_train3.json @@ -0,0 +1,13 @@ +{ + "tid": 3, + "with_skills": false, + "answer": null, + "steps": 13, + "correct": false, + "score": 0.0, + "params": { + "n": 2, + "entity": "product name(s)", + "period": "2022" + } +} diff --git a/evals/webarena/results/t279_train4.json b/evals/webarena/results/t279_train4.json new file mode 100644 index 0000000..48b6895 --- /dev/null +++ b/evals/webarena/results/t279_train4.json @@ -0,0 +1,17 @@ +{ + "tid": 4, + "with_skills": false, + "answer": [ + "Impulse Duffle", + "Overnight Duffle", + "Hawkeye Yoga Short-32-Blue" + ], + "steps": 15, + "correct": true, + "score": 1.0, + "params": { + "n": 3, + "period": "Jan 2023", + "entity": "product name(s)" + } +} diff --git a/evals/webarena/results/t279_trainwith0.json b/evals/webarena/results/t279_trainwith0.json new file mode 100644 index 0000000..45f6767 --- /dev/null +++ b/evals/webarena/results/t279_trainwith0.json @@ -0,0 +1,16 @@ +{ + "tid": 0, + "with_skills": true, + "answer": [ + "Quest Lumaflex™ Band" + ], + "steps": 12, + "correct": true, + "score": 1.0, + "params": { + "n": 1, + "entity": "product name(s)", + "period": "2022" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t279_trainwith3.json b/evals/webarena/results/t279_trainwith3.json new file mode 100644 index 0000000..68150f5 --- /dev/null +++ b/evals/webarena/results/t279_trainwith3.json @@ -0,0 +1,19 @@ +{ + "tid": 3, + "with_skills": true, + "answer": [ + "Quest Lumaflex™ Band", + "Sprite Stasis Ball 65 cm", + "Cruise Dual Analog Watch", + "Sprite Stasis Ball 55 cm" + ], + "steps": 14, + "correct": false, + "score": 0.0, + "params": { + "n": 2, + "entity": "product name(s)", + "period": "2022" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t279_trainwith4.json b/evals/webarena/results/t279_trainwith4.json new file mode 100644 index 0000000..71fd6e7 --- /dev/null +++ b/evals/webarena/results/t279_trainwith4.json @@ -0,0 +1,18 @@ +{ + "tid": 4, + "with_skills": true, + "answer": [ + "Hawkeye Yoga Short-32-Blue", + "Impulse Duffle", + "Overnight Duffle" + ], + "steps": 7, + "correct": true, + "score": 1.0, + "params": { + "n": 3, + "period": "Jan 2023", + "entity": "product name(s)" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t293_heldout296_base.json b/evals/webarena/results/t293_heldout296_base.json new file mode 100644 index 0000000..a760b2d --- /dev/null +++ b/evals/webarena/results/t293_heldout296_base.json @@ -0,0 +1,14 @@ +{ + "tid": 296, + "with_skills": false, + "answer": [ + "ssh://git@metis.lti.cs.cmu.edu:2222/eriklindernoren/PyTorch-GAN.git" + ], + "steps": 11, + "correct": true, + "score": 1.0, + "params": { + "repo": "the best GAN python implementation", + "retrieved_data_format_spec": "Return the URL only, without any additional details" + } +} diff --git a/evals/webarena/results/t293_heldout296_with.json b/evals/webarena/results/t293_heldout296_with.json new file mode 100644 index 0000000..f9f4a85 --- /dev/null +++ b/evals/webarena/results/t293_heldout296_with.json @@ -0,0 +1,15 @@ +{ + "tid": 296, + "with_skills": true, + "answer": [ + "ssh://git@metis.lti.cs.cmu.edu:2222/eriklindernoren/PyTorch-GAN.git" + ], + "steps": 15, + "correct": true, + "score": 1.0, + "params": { + "repo": "the best GAN python implementation", + "retrieved_data_format_spec": "Return the URL only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t293_heldout297_base.json b/evals/webarena/results/t293_heldout297_base.json new file mode 100644 index 0000000..74adf12 --- /dev/null +++ b/evals/webarena/results/t293_heldout297_base.json @@ -0,0 +1,14 @@ +{ + "tid": 297, + "with_skills": false, + "answer": [ + "ssh://git@metis.lti.cs.cmu.edu:2222/covid19india/covid19india-react.git" + ], + "steps": 17, + "correct": true, + "score": 1.0, + "params": { + "repo": "the most stared Covid related project", + "retrieved_data_format_spec": "Return the URL only, without any additional details" + } +} diff --git a/evals/webarena/results/t293_heldout297_with.json b/evals/webarena/results/t293_heldout297_with.json new file mode 100644 index 0000000..2ce4d5d --- /dev/null +++ b/evals/webarena/results/t293_heldout297_with.json @@ -0,0 +1,15 @@ +{ + "tid": 297, + "with_skills": true, + "answer": [ + "ssh://git@metis.lti.cs.cmu.edu:2222/covid19india/covid19india-react.git" + ], + "steps": 12, + "correct": true, + "score": 1.0, + "params": { + "repo": "the most stared Covid related project", + "retrieved_data_format_spec": "Return the URL only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t293_train293.json b/evals/webarena/results/t293_train293.json new file mode 100644 index 0000000..d164d2b --- /dev/null +++ b/evals/webarena/results/t293_train293.json @@ -0,0 +1,14 @@ +{ + "tid": 293, + "with_skills": false, + "answer": [ + "ssh://git@metis.lti.cs.cmu.edu:2222/convexegg/super_awesome_robot.git" + ], + "steps": 11, + "correct": true, + "score": 1.0, + "params": { + "repo": "Super_Awesome_Robot", + "retrieved_data_format_spec": "Return the URL only, without any additional details" + } +} diff --git a/evals/webarena/results/t293_train294.json b/evals/webarena/results/t293_train294.json new file mode 100644 index 0000000..e8c94ba --- /dev/null +++ b/evals/webarena/results/t293_train294.json @@ -0,0 +1,14 @@ +{ + "tid": 294, + "with_skills": false, + "answer": [ + "ssh://git@metis.lti.cs.cmu.edu:2222/convexegg/chatgpt.git" + ], + "steps": 11, + "correct": true, + "score": 1.0, + "params": { + "repo": "ChatGPT", + "retrieved_data_format_spec": "Return the URL only, without any additional details" + } +} diff --git a/evals/webarena/results/t293_train295.json b/evals/webarena/results/t293_train295.json new file mode 100644 index 0000000..b81fb0b --- /dev/null +++ b/evals/webarena/results/t293_train295.json @@ -0,0 +1,14 @@ +{ + "tid": 295, + "with_skills": false, + "answer": [ + "ssh://git@metis.lti.cs.cmu.edu:2222/root/metaseq.git" + ], + "steps": 20, + "correct": true, + "score": 1.0, + "params": { + "repo": "metaseq", + "retrieved_data_format_spec": "Return the URL only, without any additional details" + } +} diff --git a/evals/webarena/results/t293_trainwith293.json b/evals/webarena/results/t293_trainwith293.json new file mode 100644 index 0000000..d06bd0d --- /dev/null +++ b/evals/webarena/results/t293_trainwith293.json @@ -0,0 +1,15 @@ +{ + "tid": 293, + "with_skills": true, + "answer": [ + "ssh://git@metis.lti.cs.cmu.edu:2222/convexegg/super_awesome_robot.git" + ], + "steps": 20, + "correct": true, + "score": 1.0, + "params": { + "repo": "Super_Awesome_Robot", + "retrieved_data_format_spec": "Return the URL only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t293_trainwith294.json b/evals/webarena/results/t293_trainwith294.json new file mode 100644 index 0000000..180e33d --- /dev/null +++ b/evals/webarena/results/t293_trainwith294.json @@ -0,0 +1,15 @@ +{ + "tid": 294, + "with_skills": true, + "answer": [ + "ssh://git@metis.lti.cs.cmu.edu:2222/convexegg/chatgpt.git" + ], + "steps": 11, + "correct": true, + "score": 1.0, + "params": { + "repo": "ChatGPT", + "retrieved_data_format_spec": "Return the URL only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t293_trainwith295.json b/evals/webarena/results/t293_trainwith295.json new file mode 100644 index 0000000..0723eb5 --- /dev/null +++ b/evals/webarena/results/t293_trainwith295.json @@ -0,0 +1,15 @@ +{ + "tid": 295, + "with_skills": true, + "answer": [ + "ssh://git@metis.lti.cs.cmu.edu:2222/root/metaseq.git" + ], + "steps": 17, + "correct": true, + "score": 1.0, + "params": { + "repo": "metaseq", + "retrieved_data_format_spec": "Return the URL only, without any additional details" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t303_heldout305_base.json b/evals/webarena/results/t303_heldout305_base.json new file mode 100644 index 0000000..273456b --- /dev/null +++ b/evals/webarena/results/t303_heldout305_base.json @@ -0,0 +1,14 @@ +{ + "tid": 305, + "with_skills": false, + "answer": [ + "0" + ], + "steps": 33, + "correct": true, + "score": 1.0, + "params": { + "user": "Philip", + "period": "in Jan 2023" + } +} diff --git a/evals/webarena/results/t303_heldout305_with.json b/evals/webarena/results/t303_heldout305_with.json new file mode 100644 index 0000000..230b4ff --- /dev/null +++ b/evals/webarena/results/t303_heldout305_with.json @@ -0,0 +1,15 @@ +{ + "tid": 305, + "with_skills": true, + "answer": [ + 0 + ], + "steps": 10, + "correct": true, + "score": 1.0, + "params": { + "user": "Philip", + "period": "in Jan 2023" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t303_heldout306_base.json b/evals/webarena/results/t303_heldout306_base.json new file mode 100644 index 0000000..b34fbd6 --- /dev/null +++ b/evals/webarena/results/t303_heldout306_base.json @@ -0,0 +1,14 @@ +{ + "tid": 306, + "with_skills": false, + "answer": [ + 0 + ], + "steps": 19, + "correct": true, + "score": 1.0, + "params": { + "user": "Anthony", + "period": "between August 2022 through September 2022" + } +} diff --git a/evals/webarena/results/t303_heldout306_with.json b/evals/webarena/results/t303_heldout306_with.json new file mode 100644 index 0000000..b8f1df8 --- /dev/null +++ b/evals/webarena/results/t303_heldout306_with.json @@ -0,0 +1,15 @@ +{ + "tid": 306, + "with_skills": true, + "answer": [ + 0 + ], + "steps": 10, + "correct": true, + "score": 1.0, + "params": { + "user": "Anthony", + "period": "between August 2022 through September 2022" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t303_train303.json b/evals/webarena/results/t303_train303.json new file mode 100644 index 0000000..4212780 --- /dev/null +++ b/evals/webarena/results/t303_train303.json @@ -0,0 +1,14 @@ +{ + "tid": 303, + "with_skills": false, + "answer": [ + 0 + ], + "steps": 27, + "correct": false, + "score": 0.0, + "params": { + "user": "Kilian", + "period": "during 2023" + } +} diff --git a/evals/webarena/results/t303_train304.json b/evals/webarena/results/t303_train304.json new file mode 100644 index 0000000..60bd070 --- /dev/null +++ b/evals/webarena/results/t303_train304.json @@ -0,0 +1,14 @@ +{ + "tid": 304, + "with_skills": false, + "answer": [ + 14 + ], + "steps": 16, + "correct": true, + "score": 1.0, + "params": { + "user": "Eric Bailey", + "period": "between start of Feb 2023 and end of May 2023" + } +} diff --git a/evals/webarena/results/t303_train307.json b/evals/webarena/results/t303_train307.json new file mode 100644 index 0000000..6391359 --- /dev/null +++ b/evals/webarena/results/t303_train307.json @@ -0,0 +1,14 @@ +{ + "tid": 307, + "with_skills": false, + "answer": [ + "5" + ], + "steps": 15, + "correct": true, + "score": 1.0, + "params": { + "user": "Nic Chan", + "period": "on April 7th 2022" + } +} diff --git a/evals/webarena/results/t303_trainwith303.json b/evals/webarena/results/t303_trainwith303.json new file mode 100644 index 0000000..740c26d --- /dev/null +++ b/evals/webarena/results/t303_trainwith303.json @@ -0,0 +1,15 @@ +{ + "tid": 303, + "with_skills": true, + "answer": [ + 1 + ], + "steps": 9, + "correct": true, + "score": 1.0, + "params": { + "user": "Kilian", + "period": "during 2023" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t303_trainwith304.json b/evals/webarena/results/t303_trainwith304.json new file mode 100644 index 0000000..80637d2 --- /dev/null +++ b/evals/webarena/results/t303_trainwith304.json @@ -0,0 +1,15 @@ +{ + "tid": 304, + "with_skills": true, + "answer": [ + 14 + ], + "steps": 16, + "correct": true, + "score": 1.0, + "params": { + "user": "Eric Bailey", + "period": "between start of Feb 2023 and end of May 2023" + }, + "verdict": "use" +} diff --git a/evals/webarena/results/t303_trainwith307.json b/evals/webarena/results/t303_trainwith307.json new file mode 100644 index 0000000..0f18094 --- /dev/null +++ b/evals/webarena/results/t303_trainwith307.json @@ -0,0 +1,15 @@ +{ + "tid": 307, + "with_skills": true, + "answer": [ + 5 + ], + "steps": 9, + "correct": true, + "score": 1.0, + "params": { + "user": "Nic Chan", + "period": "on April 7th 2022" + }, + "verdict": "use" +} diff --git a/evals/webarena/run_all.sh b/evals/webarena/run_all.sh new file mode 100755 index 0000000..73d7f7c --- /dev/null +++ b/evals/webarena/run_all.sh @@ -0,0 +1,18 @@ +#!/usr/bin/env bash +# Run the full WebArena reproduction sequentially (100 solves — hours of wall time +# and real model cost; see README.md; parallelize per template if you can). +# +# export OPENAI_API_KEY=... # + OPENAI_ENDPOINT / OPENAI_MODEL on a gateway +# export WEBARENA_DATASET=/path/to/webarena-verified.json +# export WEBARENA_CONFIG=/path/to/your/verified_config.json +# ./run_all.sh [extra reproduce.py flags, e.g. --eval-python /path/to/python] +set -euo pipefail +cd "$(dirname "$0")" +[ -n "${WEBARENA_DATASET:-}" ] && [ -n "${WEBARENA_CONFIG:-}" ] || { + echo "usage: set WEBARENA_DATASET and WEBARENA_CONFIG first (see README.md)" >&2; exit 1; } + +python reproduce.py plan | grep '^python' | while read -r line; do + echo "== $line" + $line "$@" +done +python reproduce.py table "$@" diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md index 6c1976a..ad8a87f 100644 --- a/src/webwright/skills/README.md +++ b/src/webwright/skills/README.md @@ -40,6 +40,10 @@ skills with nothing new stay byte-identical. Growth never breaks what already wo - **Tasks that were expensive become cheap** — 33 steps → 10 on repeat task types; overall 70% vs 55% accuracy, ~2.4 fewer steps. +These numbers aren't take-our-word-for-it: [`evals/webarena/`](../../../evals/webarena/) +has the per-task records behind the table (aggregate them with one command, no setup) and +a driver that re-runs the whole experiment on your own WebArena deployment. + **Cheap to adopt, honest about costs.** Integration is purely additive — one tool plus one CLI, no agent-loop changes. Building the library is not free, but close: it recycles solves you already ran (only the gate-passed ones), plus one distillation LLM call per template @@ -321,7 +325,9 @@ uses the same backend as the running agent. No gateway or key is hardcoded. ## Results (summary) -Validated with this module (full data + analysis live in the companion research repo, not here): +Validated with this module. Reproducible: [`evals/webarena/`](../../../evals/webarena/) ships +the per-task records these summaries aggregate from (`reproduce.py table --results results`) +plus the driver to re-run everything; CI locks the records to the numbers below. - **WebArena — 10 templates × 3 domains (shopping_admin / gitlab / map), gold gate.** Per template 3 train solves build the library, 2 held-out instances measure reuse (WITH library vs from diff --git a/src/webwright/skills/examples/README.md b/src/webwright/skills/examples/README.md index 42816de..44f83e3 100644 --- a/src/webwright/skills/examples/README.md +++ b/src/webwright/skills/examples/README.md @@ -67,7 +67,8 @@ scratch. Same model, same budget. | all reviews with <=3 stars (shopping) | **wrong**, 31 steps| **correct**, 13 steps | Aggregate over 10 templates x 3 sites (20 held-out instances): **70% vs 55% accuracy, 14.7 vs -17.1 steps**. Two kinds of wins: tasks that get *cheaper* (33 -> 10) and tasks that get +17.1 steps** (per-task records + rerun driver: [`evals/webarena/`](../../../../evals/webarena/)). +Two kinds of wins: tasks that get *cheaper* (33 -> 10) and tasks that get *solvable* (wrong -> correct). Honest counterpoint: on tasks the agent already solves in a few steps, querying the library costs more than it saves — reuse pays on expensive-to-explore tasks. diff --git a/tests/skills/test_eval_snapshot.py b/tests/skills/test_eval_snapshot.py new file mode 100644 index 0000000..be8cb25 --- /dev/null +++ b/tests/skills/test_eval_snapshot.py @@ -0,0 +1,43 @@ +"""Lock the WebArena results snapshot (evals/webarena/results/) to the published numbers. + +The module README's table must stay derivable from the checked-in per-task records — +if either side drifts, this fails. Also enforces the snapshot's sanitization. +""" +import json +from pathlib import Path + +EVAL = Path(__file__).resolve().parents[2] / "evals" / "webarena" + + +def run(): + recs = {p.stem: json.loads(p.read_text()) for p in (EVAL / "results").glob("*.json")} + assert len(recs) == 100, f"expected 100 per-task records, found {len(recs)}" + + def agg(sel): + rs = [v for k, v in recs.items() if sel(k)] + return len(rs), sum(1 for r in rs if r["correct"]), sum(r["steps"] for r in rs) / len(rs) + + n, ok, steps = agg(lambda k: "heldout" in k and k.endswith("_with")) + assert (n, ok, round(steps, 1)) == (20, 14, 14.7), (n, ok, steps) # 70% + n, ok, steps = agg(lambda k: "heldout" in k and k.endswith("_base")) + assert (n, ok, round(steps, 1)) == (20, 11, 17.1), (n, ok, steps) # 55% + n, ok, steps = agg(lambda k: "trainwith" in k) + assert (n, ok, round(steps, 1)) == (30, 26, 13.7), (n, ok, steps) # 86.7% + n, ok, steps = agg(lambda k: "_train" in k and "trainwith" not in k) + assert (n, ok, round(steps, 1)) == (30, 23, 15.9), (n, ok, steps) # 76.7% + + # sanitization lock: no local paths, no internal hosts, no keys in the snapshot + for p in (EVAL / "results").glob("*.json"): + txt = p.read_text() + for bad in ("/home/", "ec2-", "amazonaws", "api_key", "Bearer "): + assert bad not in txt, (p.name, bad) + assert "dir" not in json.loads(txt), f"{p.name}: local run dir must stay out" + + # the driver that regenerates all of this must at least compile + compile((EVAL / "reproduce.py").read_text(), "reproduce.py", "exec") + + print("test_eval_snapshot OK") + + +if __name__ == "__main__": + run() From 6513d87db387438c0a18deba111a9653e28090e6 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Thu, 9 Jul 2026 22:21:41 +0000 Subject: [PATCH 22/54] skills: address Copilot review findings MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - prompt.py: shell-quote task and library in the skill_use hint (shlex.quote) — $VAR / $(...) / backticks expanded in bash even inside the old double quotes; regression test added - llm.py: llm_json now scans for the FIRST valid JSON object (raw_decode) as documented, instead of a greedy first-{ to last-} regex that could span unrelated braces - update.py: drop the misleading llm(..., timeout=400) kwarg (silently swallowed; the model's request_timeout_seconds already governs); read JSON files via read_text instead of unclosed open() - library.py / learn.py: explicit UTF-8 on every skill/meta/ledger read+write (locale-independent on Windows) - tests: pytest-discoverable test_all() entry points in all 7 files (CI keeps running them as scripts too) --- src/webwright/skills/learn.py | 12 ++++++------ src/webwright/skills/library.py | 9 +++++---- src/webwright/skills/llm.py | 26 ++++++++++++-------------- src/webwright/skills/prompt.py | 9 ++++++--- src/webwright/skills/update.py | 6 +++--- tests/skills/test_eval_snapshot.py | 5 +++++ tests/skills/test_evolve.py | 5 +++++ tests/skills/test_gate.py | 5 +++++ tests/skills/test_learn.py | 6 ++++++ tests/skills/test_learned_example.py | 5 +++++ tests/skills/test_library.py | 5 +++++ tests/skills/test_retrieve_decide.py | 15 +++++++++++++-- 12 files changed, 76 insertions(+), 32 deletions(-) diff --git a/src/webwright/skills/learn.py b/src/webwright/skills/learn.py index e230758..8dce16c 100644 --- a/src/webwright/skills/learn.py +++ b/src/webwright/skills/learn.py @@ -51,8 +51,8 @@ def collect_runs(runs_dir: Path, ledger: dict): print(f" skip {d.name}: no agent_response.json") continue try: - t = json.loads(tj.read_text()) - answer = json.loads(ar.read_text()).get("retrieved_data") + t = json.loads(tj.read_text(encoding="utf-8")) + answer = json.loads(ar.read_text(encoding="utf-8")).get("retrieved_data") except Exception as e: print(f" skip {d.name}: unreadable ({e})") continue @@ -100,7 +100,7 @@ def group_chunk(runs, existing_templates): def learn(runs_dir, library_root, golds=None, chunk=25, dry_run=False): lib = Library(library_root) ledger_path = Path(library_root) / ".learned.json" - ledger = json.loads(ledger_path.read_text()) if ledger_path.exists() else {"runs": {}} + ledger = json.loads(ledger_path.read_text(encoding="utf-8")) if ledger_path.exists() else {"runs": {}} golds = golds or {} runs = collect_runs(Path(runs_dir), ledger) @@ -145,7 +145,7 @@ def learn(runs_dir, library_root, golds=None, chunk=25, dry_run=False): code_p = Path(r["dir"]) / "final_script.py" traces.append(Trace( template=tmpl, - code=code_p.read_text() if code_p.exists() else "", + code=code_p.read_text(encoding="utf-8") if code_p.exists() else "", answer=r["answer"], correct=True, # existing template -> mark adapt so evolve REFINES instead of ignoring verdict="adapt" if tmpl in existing else "skip", @@ -161,7 +161,7 @@ def learn(runs_dir, library_root, golds=None, chunk=25, dry_run=False): if 0 <= m.get("i", -1) < len(batch): ledger["runs"][batch[m["i"]]["dir"]] = {"template": tmpl} # audit trail + idempotence, saved per chunk - ledger_path.write_text(json.dumps(ledger, indent=2)) + ledger_path.write_text(json.dumps(ledger, indent=2), encoding="utf-8") if not dry_run: print(f"\nlibrary now has {len(lib.list())} skill(s); ledger -> {ledger_path}") @@ -176,7 +176,7 @@ def main(argv=None) -> int: p.add_argument("--chunk", type=int, default=25, help="Runs per LLM grouping call.") p.add_argument("--dry-run", action="store_true", help="Show the grouping plan, change nothing.") a = p.parse_args(argv) - golds = json.loads(Path(a.golds).read_text()) if a.golds else {} + golds = json.loads(Path(a.golds).read_text(encoding="utf-8")) if a.golds else {} learn(a.runs_dir, a.library, golds=golds, chunk=a.chunk, dry_run=a.dry_run) return 0 diff --git a/src/webwright/skills/library.py b/src/webwright/skills/library.py index 9bb6345..1a4286c 100644 --- a/src/webwright/skills/library.py +++ b/src/webwright/skills/library.py @@ -45,15 +45,16 @@ def get(self, skill_id: str) -> Skill | None: d = self._dir(skill_id) if not (d / "meta.json").exists(): return None - meta = json.loads((d / "meta.json").read_text()) - code = (d / "skill.py").read_text() if (d / "skill.py").exists() else "" + meta = json.loads((d / "meta.json").read_text(encoding="utf-8")) + code = (d / "skill.py").read_text(encoding="utf-8") if (d / "skill.py").exists() else "" return Skill(skill_id=skill_id, code=code, meta=meta) def add(self, skill: Skill) -> None: d = self._dir(skill.skill_id) d.mkdir(parents=True, exist_ok=True) - (d / "skill.py").write_text(skill.code) - (d / "meta.json").write_text(json.dumps(skill.meta, ensure_ascii=False, indent=2)) + (d / "skill.py").write_text(skill.code, encoding="utf-8") + (d / "meta.json").write_text(json.dumps(skill.meta, ensure_ascii=False, indent=2), + encoding="utf-8") def path(self, skill_id: str) -> Path: return self._dir(skill_id) / "skill.py" diff --git a/src/webwright/skills/llm.py b/src/webwright/skills/llm.py index b79b98d..57a8076 100644 --- a/src/webwright/skills/llm.py +++ b/src/webwright/skills/llm.py @@ -6,7 +6,6 @@ from __future__ import annotations import json -import re from typing import Any, Optional from webwright.models import get_model @@ -57,18 +56,17 @@ def llm(system: str, user: str, *, model: Any = None, max_tokens: int | None = N def llm_json(system: str, user: str, **kw: Any) -> dict: - """Call + parse the first {...} JSON object out of the reply.""" + """Call + parse the first valid {...} JSON object out of the reply (prose/fences around + it are fine; brace snippets that aren't valid JSON are skipped over).""" txt = llm(system, user, **kw) - match = re.search(r"\{.*\}", txt, re.S) - if not match: - return {} - try: - return json.loads(match.group(0)) - except Exception: - s = match.group(0) - for end in range(len(s), 0, -1): - try: - return json.loads(s[:end]) - except Exception: - continue + dec = json.JSONDecoder() + for i, ch in enumerate(txt): + if ch != "{": + continue + try: + obj, _ = dec.raw_decode(txt, i) + except ValueError: + continue + if isinstance(obj, dict): + return obj return {} diff --git a/src/webwright/skills/prompt.py b/src/webwright/skills/prompt.py index 8f5a287..768a326 100644 --- a/src/webwright/skills/prompt.py +++ b/src/webwright/skills/prompt.py @@ -5,13 +5,14 @@ """ from __future__ import annotations +import shlex from pathlib import Path _HINT = """## Skill library (reuse before solving from scratch) A library of previously-built executable code skills may contain one that helps this task. BEFORE planning from scratch, query it ONCE from bash: - python -m webwright.tools.skill_use --task "{task}" --library "{library}" + python -m webwright.tools.skill_use --task {task_q} --library {library_q} It returns JSON {{verdict, skill_id, source_path, how_to_reuse}}: - "use" : read source_path, copy it into your final_script, fill THIS task's params. @@ -28,6 +29,8 @@ def with_skill_hint(task_prompt: str, *, task: str, library: str) -> str: `library` is resolved to an ABSOLUTE path: the hint's command runs in the agent's own workspace, so a relative path would silently point at a nonexistent (empty) - library there and every lookup would come back "skip".""" + library there and every lookup would come back "skip". Both values are shell-quoted: + the agent executes this line in bash, where $VAR / `...` / $(...) would otherwise + expand even inside double quotes.""" library = str(Path(library).resolve()) - return _HINT.format(task=task.replace('"', "'"), library=library) + task_prompt + return _HINT.format(task_q=shlex.quote(task), library_q=shlex.quote(library)) + task_prompt diff --git a/src/webwright/skills/update.py b/src/webwright/skills/update.py index 7b39362..c2a3cb5 100644 --- a/src/webwright/skills/update.py +++ b/src/webwright/skills/update.py @@ -98,7 +98,7 @@ def _refine(traces: list[Trace], library: Library) -> list[str]: f"answer={json.dumps(tr.answer, ensure_ascii=False)[:120]})\n```python\n{tr.code}\n```" ) sys_prompt = _REFINE_SYS + (_REFINE_INCREMENTAL if existing else "") - code = _extract_code(llm(sys_prompt, "\n\n".join(blocks), max_tokens=16000, timeout=400)) + code = _extract_code(llm(sys_prompt, "\n\n".join(blocks), max_tokens=16000)) n_prev = (existing.meta.get("n_solves", 0) if existing else 0) meta = { "template": template, @@ -174,7 +174,7 @@ def traces_from_manifest(manifest: dict) -> list["Trace"]: answer = r.get("answer") if answer is None and (d / "agent_response.json").exists(): try: - answer = json.load(open(d / "agent_response.json")).get("retrieved_data") + answer = json.loads((d / "agent_response.json").read_text(encoding="utf-8")).get("retrieved_data") except Exception: pass out.append(Trace(template=template, code=code, answer=answer, @@ -195,7 +195,7 @@ def main(argv=None) -> int: p.add_argument("--manifest", required=True, help="JSON: {template, runs:[{dir,admit,params,...}]}") p.add_argument("--library", required=True, help="Path to the skill library directory.") a = p.parse_args(argv) - manifest = json.load(open(a.manifest, encoding="utf-8")) + manifest = json.loads(Path(a.manifest).read_text(encoding="utf-8")) traces = traces_from_manifest(manifest) changelog = evolve(traces, Library(a.library)) print(json.dumps(changelog, ensure_ascii=False, indent=2)) diff --git a/tests/skills/test_eval_snapshot.py b/tests/skills/test_eval_snapshot.py index be8cb25..d90994a 100644 --- a/tests/skills/test_eval_snapshot.py +++ b/tests/skills/test_eval_snapshot.py @@ -39,5 +39,10 @@ def agg(sel): print("test_eval_snapshot OK") +# pytest entry point (CI also runs this file as a script) +def test_all(): + run() + + if __name__ == "__main__": run() diff --git a/tests/skills/test_evolve.py b/tests/skills/test_evolve.py index 4f95221..8bec0fb 100644 --- a/tests/skills/test_evolve.py +++ b/tests/skills/test_evolve.py @@ -66,5 +66,10 @@ def fake_refine(group, library): print("test_evolve OK") +# pytest entry point (CI also runs this file as a script) +def test_all(): + run() + + if __name__ == "__main__": run() diff --git a/tests/skills/test_gate.py b/tests/skills/test_gate.py index 6240b3b..dd9c730 100644 --- a/tests/skills/test_gate.py +++ b/tests/skills/test_gate.py @@ -31,5 +31,10 @@ def run(): print("test_gate OK") +# pytest entry point (CI also runs this file as a script) +def test_all(): + run() + + if __name__ == "__main__": run() diff --git a/tests/skills/test_learn.py b/tests/skills/test_learn.py index c2e0df4..a663a9a 100644 --- a/tests/skills/test_learn.py +++ b/tests/skills/test_learn.py @@ -50,6 +50,12 @@ def run_regressions(): print("test_learn regressions OK") +# pytest entry point (CI also runs this file as a script) +def test_all(): + run() + run_regressions() + + if __name__ == "__main__": run() run_regressions() diff --git a/tests/skills/test_learned_example.py b/tests/skills/test_learned_example.py index d0dfd1c..3f00dde 100644 --- a/tests/skills/test_learned_example.py +++ b/tests/skills/test_learned_example.py @@ -23,5 +23,10 @@ def run(): print("test_learned_example OK") +# pytest entry point (CI also runs this file as a script) +def test_all(): + run() + + if __name__ == "__main__": run() diff --git a/tests/skills/test_library.py b/tests/skills/test_library.py index 174c6c3..bd0adb7 100644 --- a/tests/skills/test_library.py +++ b/tests/skills/test_library.py @@ -29,5 +29,10 @@ def run(): print("test_library OK") +# pytest entry point (CI also runs this file as a script) +def test_all(): + run() + + if __name__ == "__main__": run() diff --git a/tests/skills/test_retrieve_decide.py b/tests/skills/test_retrieve_decide.py index c92f792..6552cd4 100644 --- a/tests/skills/test_retrieve_decide.py +++ b/tests/skills/test_retrieve_decide.py @@ -53,8 +53,14 @@ def run(): # the agent's workspace, where a relative path would point at nothing) from webwright.skills.prompt import with_skill_hint hinted = with_skill_hint("solve it", task="t", library="./some_rel_lib") - import os - assert f'--library "{os.path.abspath("./some_rel_lib")}"' in hinted, hinted[:300] + import os, shlex + assert f'--library {os.path.abspath("./some_rel_lib")}' in hinted, hinted[:300] + + # ...and shell-quote the task: $VAR / $(...) / backticks expand in bash even inside + # double quotes, so a task containing them must land single-quoted in the hint + evil = 'count $(whoami) commits in `pwd` for $USER' + hinted2 = with_skill_hint("solve it", task=evil, library="./some_rel_lib") + assert f"--task {shlex.quote(evil)}" in hinted2, hinted2[:300] # recommend on a MISSING or EMPTY library -> loud skip with a warning (and no mkdir side effect) import webwright.tools.skill_use as T2 @@ -83,5 +89,10 @@ def run(): print("test_retrieve_decide OK") +# pytest entry point (CI also runs this file as a script) +def test_all(): + run() + + if __name__ == "__main__": run() From 8ea59bef96e7b0a450bf9a90fd47a626ffffa2d4 Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Fri, 10 Jul 2026 00:07:07 +0000 Subject: [PATCH 23/54] =?UTF-8?q?skills:=20address=20review=20=E2=80=94=20?= =?UTF-8?q?complete=20Quickstart,=20drop=20skill=5Fmode.yaml,=20clarify=20?= =?UTF-8?q?examples/?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - README Quickstart is now the complete loop on a runnable example (public GitHub): solve 3 instances -> learn -> an UNSEEN instance reuses the skill, plus using the library without the agent (skill_use query, running the learned skill standalone, verified: pandas-dev/pandas -> v3.0.4 with a params-only taskspec) - remove config/skill_mode.yaml: nothing referenced it (the skill hint is prompt-level via with_skill_hint; the step_limit bump was never needed by any documented path) - examples/README: state the directory's purpose up front, and document learned_library's provenance (the Quickstart loop's checked-in result) with the unseen-repo runs and the CI test that locks it --- src/webwright/config/skill_mode.yaml | 13 ------- src/webwright/skills/README.md | 45 ++++++++++++++++++++----- src/webwright/skills/examples/README.md | 36 ++++++++++++++++++-- 3 files changed, 70 insertions(+), 24 deletions(-) delete mode 100644 src/webwright/config/skill_mode.yaml diff --git a/src/webwright/config/skill_mode.yaml b/src/webwright/config/skill_mode.yaml deleted file mode 100644 index b8d6158..0000000 --- a/src/webwright/config/skill_mode.yaml +++ /dev/null @@ -1,13 +0,0 @@ -# Skill-library mode (optional overlay): raise the step budget headroom for skill reuse runs. -# Enable by stacking: webwright run ... -c base.yaml -c model_openai.yaml -c skill_mode.yaml -# -# How the agent is told to reuse skills: the SKILL-LIBRARY block is prepended to the TASK prompt -# by the caller (see webwright.skills.prompt.with_skill_hint), NOT injected via system_template — -# webwright merges system_template by replacement, so a prompt-level hint is the clean, non-invasive -# way and keeps webwright's default behavior unchanged when this mode is off. -# -# Requires env: -# SKILL_LIBRARY_ROOT path to the skill library (read by the skill_use tool) -# SKILL_MODEL_NAME / SKILL_MODEL_ENDPOINT (optional) backend for skill_use; defaults to OPENAI_* -agent: - step_limit: 100 diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md index ad8a87f..7527596 100644 --- a/src/webwright/skills/README.md +++ b/src/webwright/skills/README.md @@ -74,28 +74,57 @@ Two touch points, **no change to the agent loop or default config**: > (runnable standalone, no LLM), measured step-saving numbers, and filled-in example inputs for > every file this guide asks you to write. See [`examples/README.md`](examples/README.md). -## Quickstart — the two-command version +## Quickstart — the complete loop on a real example + +Three steps: solve a few instances of a task type, `learn` them into a skill, then watch +the next solve reuse it. Copy-pasteable as is (public GitHub, read-only): ```bash export OPENAI_API_KEY=... -# custom / OpenAI-compatible gateway? BOTH steps need these too, or reuse is silently off: +# custom / OpenAI-compatible gateway? ALL steps need these too, or reuse is silently off: export OPENAI_ENDPOINT=https://your-gateway/... OPENAI_MODEL=your-model +cd src/webwright/skills # commands below run from the module directory -# 1. solve tasks with the library in the loop (wrapper = hint + answer-output instruction) -examples/solve_with_library.sh "How many commits did Jane make in Jan 2023?" \ - http://gitlab.example.com /abs/path/library -o outputs -c base.yaml -c model_openai.yaml +# 1. SOLVE a few instances of the same task type (library is empty — these run from scratch) +for repo in psf/requests pallets/flask tiangolo/fastapi; do + examples/solve_with_library.sh \ + "What is the latest release version of $repo on GitHub?" \ + "https://github.com/$repo" "$PWD/library" -o outputs -c base.yaml -c model_openai.yaml +done -# 2. turn everything you've solved into skills — no manifest, no fields to learn +# 2. LEARN: distill everything you've solved into skills — no manifest, no fields to fill python -m webwright.skills learn outputs/ --library ./library +# -> groups the 3 runs into ONE template, lifts owner/repo into parameters, writes +# library/what_is_the_latest_release_version_of_ow_.../{skill.py, meta.json} + +# 3. USE the library: same wrapper, an UNSEEN instance — the agent finds and reuses the skill +examples/solve_with_library.sh \ + "What is the latest release version of numpy/numpy on GitHub?" \ + https://github.com/numpy/numpy "$PWD/library" -o outputs -c base.yaml -c model_openai.yaml +# outputs//skill_decision.json -> {"verdict": "use", "skill_id": "what_is_the_latest_..."} +``` + +The library is also usable **without the agent**: + +```bash +# ask it whether it can help a task (the same call the agent makes — one LLM round trip) +python -m webwright.tools.skill_use \ + --task "What is the latest release version of pandas-dev/pandas on GitHub?" \ + --library ./library + +# or run the learned skill directly — no model in the loop at all +echo '{"params": {"owner": "pandas-dev", "repo": "pandas"}}' > taskspec.json +python library/what_is_the_latest_release_version_of_ow_*/skill.py taskspec.json ``` +Exactly this loop, already run and checked in: `examples/learned_library/` (with its +run/learn transcript in `examples/README.md`). + `learn` scans the run folders, gates each solve (gold if you pass `--golds golds.json`, else a shape check), auto-groups tasks into templates with one LLM call per ~25 runs, extracts the parameters, and grows the library. It is **idempotent** — re-run it whenever; already-learned runs are skipped (`library/.learned.json`), and big folders are chunked automatically. `--dry-run` shows the grouping plan without changing anything. - -That's the whole loop: solve → learn → the next solve reuses the library automatically. Everything below is **manual mode** — explicit manifests, benchmark-grade gold gates, fine control over every field. You don't need it to get started. diff --git a/src/webwright/skills/examples/README.md b/src/webwright/skills/examples/README.md index 44f83e3..014e653 100644 --- a/src/webwright/skills/examples/README.md +++ b/src/webwright/skills/examples/README.md @@ -1,7 +1,9 @@ # Examples — what a skill library looks like, and what reuse buys you -Everything here is real output of the pipeline (produced by `update.evolve` during a WebArena -evaluation), lightly curated. Nothing is hand-written. +This directory is what the module README points at whenever it says "see examples": two +real, checked-in skill libraries you can read and run (nothing is hand-written — both are +verbatim pipeline output), the wrapper script the Quickstart uses, and filled-in copies of +every input file the manual pipeline asks you to write. ``` examples/ @@ -17,9 +19,37 @@ examples/ └── batch.example.json # a filled manifest (README step 2) ``` +## The learned library — the Quickstart loop, already run and checked in + +`learned_library/` is the exact artifact the README Quickstart produces. Provenance: three +real solves of "What is the latest release version of X on GitHub?" (psf/requests, +pallets/flask, tiangolo/fastapi) were fed to `python -m webwright.skills learn`, which +grouped them into one template and lifted what varied into parameters: + +```json +{ + "template": "What is the latest release version of {{owner}}/{{repo}} on GitHub?", + "signature": { "params": ["owner", "repo"], "call": "python skill.py taskspec.json" }, + "n_solves": 3 +} +``` + +It generalizes to repos none of the three solves ever saw, with no model in the loop: + +```bash +cd learned_library/what_is_the_latest_release_version_of_ow_c29dab8 +echo '{"params": {"owner": "numpy", "repo": "numpy"}}' > taskspec.json +python skill.py taskspec.json # -> {"retrieved_data": ["v2.5.1"]} +# pandas-dev/pandas -> ["v3.0.4"] — any public repo works +``` + +`tests/skills/test_learned_example.py` locks these properties (n_solves ≥ 3, parameters +actually lifted, code compiles) in CI. + ## What a skill looks like -One template = one skill = one self-contained directory. The `meta.json` is the catalog card +`example_library/` is real output too — produced by `update.evolve` during the WebArena +evaluation, lightly curated. One template = one skill = one self-contained directory. The `meta.json` is the catalog card (what `retrieve` sees); the `skill.py` is what the agent reads and reuses: ```json From 7acbdd4319d0b84e8ffbb17a1c78bdf7c45a096f Mon Sep 17 00:00:00 2001 From: Demi Wang <86202027+DEM1TASSE@users.noreply.github.com> Date: Sat, 11 Jul 2026 00:08:15 +0000 Subject: [PATCH 24/54] =?UTF-8?q?skills:=20Quickstart=20on=20Google=20Flig?= =?UTF-8?q?hts=20=E2=80=94=20Webwright's=20own=20example=20task?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit The GitHub release example couldn't show what a skill carries: a frontier model already knows GitHub cold, and the answer is one search away. Google Flights is the task family Webwright's main README uses, and it has the two properties the Quickstart needs: the answer is live (unrecallable), and the UI is fiddly enough that from-scratch solves cost 13-26 steps. - Quickstart rewritten around the flights loop: 3 routes solved from scratch -> learn lifts FIVE parameters (origin/destination city+code, date) -> an unseen route reuses the skill (verdict=use) -> the same skill runs standalone in ~30 s with no model (the cron fare-watcher payoff) - honest verification note for live data: no gold exists, gate=self_verify (the run-time warning says so); what is verified is consistency — the standalone skill and a fresh agent solve of an unseen route returned the same answer minutes apart - examples/learned_library gains the flights skill next to the GitHub release skill (two task families, two sites, one library); provenance documented in examples/README; test_learned_example locks every skill in the directory --- src/webwright/skills/README.md | 61 ++- src/webwright/skills/examples/README.md | 47 +- .../final_script_log.txt | 17 + .../meta.json | 24 ++ .../screenshots/01_home.png | Bin 0 -> 679730 bytes .../screenshots/02_one_way.png | Bin 0 -> 678899 bytes .../screenshots/03_route.png | Bin 0 -> 679443 bytes .../screenshots/04_date.png | Bin 0 -> 662344 bytes .../screenshots/05_results.png | Bin 0 -> 358978 bytes .../screenshots/06_cheapest_tab.png | Bin 0 -> 331820 bytes .../skill.py | 403 ++++++++++++++++++ 11 files changed, 518 insertions(+), 34 deletions(-) create mode 100644 src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/final_script_log.txt create mode 100644 src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/meta.json create mode 100644 src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/screenshots/01_home.png create mode 100644 src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/screenshots/02_one_way.png create mode 100644 src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/screenshots/03_route.png create mode 100644 src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/screenshots/04_date.png create mode 100644 src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/screenshots/05_results.png create mode 100644 src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/screenshots/06_cheapest_tab.png create mode 100644 src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/skill.py diff --git a/src/webwright/skills/README.md b/src/webwright/skills/README.md index 7527596..c8e41d0 100644 --- a/src/webwright/skills/README.md +++ b/src/webwright/skills/README.md @@ -74,10 +74,12 @@ Two touch points, **no change to the agent loop or default config**: > (runnable standalone, no LLM), measured step-saving numbers, and filled-in example inputs for > every file this guide asks you to write. See [`examples/README.md`](examples/README.md). -## Quickstart — the complete loop on a real example +## Quickstart — the complete loop on Webwright's own example task Three steps: solve a few instances of a task type, `learn` them into a skill, then watch -the next solve reuse it. Copy-pasteable as is (public GitHub, read-only): +the next solve reuse it. The task family is the one from Webwright's main README — +Google Flights — where the answer is live (no model can recall it) and the UI is genuinely +fiddly, so a learned skill has something real to carry: ```bash export OPENAI_API_KEY=... @@ -86,39 +88,60 @@ export OPENAI_ENDPOINT=https://your-gateway/... OPENAI_MODEL=your-model cd src/webwright/skills # commands below run from the module directory # 1. SOLVE a few instances of the same task type (library is empty — these run from scratch) -for repo in psf/requests pallets/flask tiangolo/fastapi; do +while IFS='|' read -r FROM TO; do examples/solve_with_library.sh \ - "What is the latest release version of $repo on GitHub?" \ - "https://github.com/$repo" "$PWD/library" -o outputs -c base.yaml -c model_openai.yaml -done + "What is the cheapest flight from $FROM to $TO on 2026-08-15 (one-way)? Return the answer as a list: [airline, price]." \ + https://www.google.com/flights "$PWD/library" -o outputs -c base.yaml -c model_openai.yaml +done <<'ROUTES' +Seattle (SEA)|New York (JFK) +San Francisco (SFO)|Boston (BOS) +Los Angeles (LAX)|Chicago (ORD) +ROUTES # 2. LEARN: distill everything you've solved into skills — no manifest, no fields to fill python -m webwright.skills learn outputs/ --library ./library -# -> groups the 3 runs into ONE template, lifts owner/repo into parameters, writes -# library/what_is_the_latest_release_version_of_ow_.../{skill.py, meta.json} +# -> groups the 3 runs into ONE template and lifts FIVE parameters: +# origin city/code, destination city/code, date +# library/what_is_the_cheapest_flight_from_origin_.../{skill.py, meta.json} -# 3. USE the library: same wrapper, an UNSEEN instance — the agent finds and reuses the skill +# 3. USE the library: same wrapper, an UNSEEN route — the agent finds and reuses the skill examples/solve_with_library.sh \ - "What is the latest release version of numpy/numpy on GitHub?" \ - https://github.com/numpy/numpy "$PWD/library" -o outputs -c base.yaml -c model_openai.yaml -# outputs//skill_decision.json -> {"verdict": "use", "skill_id": "what_is_the_latest_..."} + "What is the cheapest flight from Seattle (SEA) to Denver (DEN) on 2026-08-15 (one-way)? Return the answer as a list: [airline, price]." \ + https://www.google.com/flights "$PWD/library" -o outputs -c base.yaml -c model_openai.yaml +# outputs//skill_decision.json -> {"verdict": "use", "skill_id": "what_is_the_cheapest_..."} ``` -The library is also usable **without the agent**: +The library is also usable **without the agent** — this is the whole point of code skills: ```bash # ask it whether it can help a task (the same call the agent makes — one LLM round trip) python -m webwright.tools.skill_use \ - --task "What is the latest release version of pandas-dev/pandas on GitHub?" \ + --task "cheapest flight from Portland (PDX) to Austin (AUS) on 2026-09-01" \ --library ./library -# or run the learned skill directly — no model in the loop at all -echo '{"params": {"owner": "pandas-dev", "repo": "pandas"}}' > taskspec.json -python library/what_is_the_latest_release_version_of_ow_*/skill.py taskspec.json +# or run the learned skill directly — no model in the loop, ~30 seconds +cat > taskspec.json <<'EOF' +{"params": {"origin_city": "Seattle", "origin_code": "SEA", "destination_city": "Denver", + "destination_code": "DEN", "date": "2026-08-15"}, + "output_schema": {"type": "array", "items": {"type": "string"}}} +EOF +python library/what_is_the_cheapest_flight_*/skill.py taskspec.json +# -> {"retrieved_data": ["Frontier", "$68"]} (live price — yours will differ) ``` -Exactly this loop, already run and checked in: `examples/learned_library/` (with its -run/learn transcript in `examples/README.md`). +That last command is a fare watcher: put it in cron and the query that cost the agent +13-26 exploration steps to figure out re-runs forever at zero tokens. + +**Verification on live data, honestly:** flight prices have no fixed gold answer, so the +gate here is `self_verify` (shape only — the run-time warning tells you so). What we CAN +verify: right after learning, the standalone skill and a fresh agent solve of an unseen +route returned the **same answer** within minutes of each other — the skill deterministically +reproduces what the agent finds. When your task family does have golds, pass `--golds` and +admission becomes real verification. + +Exactly this loop, already run and checked in: `examples/learned_library/` holds both this +flights skill and a GitHub release-version skill learned the same way (provenance and +transcripts in `examples/README.md`). `learn` scans the run folders, gates each solve (gold if you pass `--golds golds.json`, else a shape check), auto-groups tasks into templates with one LLM call per ~25 runs, diff --git a/src/webwright/skills/examples/README.md b/src/webwright/skills/examples/README.md index 014e653..6a60032 100644 --- a/src/webwright/skills/examples/README.md +++ b/src/webwright/skills/examples/README.md @@ -7,8 +7,11 @@ every input file the manual pipeline asks you to write. ``` examples/ -├── learned_library/ # produced by `skills learn` from 3 REAL solves (n_solves=3): -│ └── what_is_the_latest_release…/ # owner/repo lifted to parameters, two strategies as fallbacks; +├── learned_library/ # produced by `skills learn`, 3 real solves each (n_solves=3): +│ ├── what_is_the_cheapest_flight…/ # the Quickstart artifact — Google Flights, FIVE params +│ │ # (origin/destination city+code, date); unseen route +│ │ # SEA->DEN standalone in ~30 s, matched the agent's answer +│ └── what_is_the_latest_release…/ # GitHub release version — owner/repo lifted to parameters; │ # tested on an unseen repo (numpy/numpy -> v2.5.1, no model) ├── example_library/ # a library with one skill, exactly as evolve wrote it │ └── how_many_commits_did_user_make_period_in_the_cur/ @@ -21,30 +24,44 @@ examples/ ## The learned library — the Quickstart loop, already run and checked in -`learned_library/` is the exact artifact the README Quickstart produces. Provenance: three -real solves of "What is the latest release version of X on GitHub?" (psf/requests, -pallets/flask, tiangolo/fastapi) were fed to `python -m webwright.skills learn`, which -grouped them into one template and lifted what varied into parameters: +`learned_library/` holds two skills produced by `python -m webwright.skills learn`, each +aggregated from three real solves. + +**The flights skill (the Quickstart's artifact).** Three from-scratch solves of "cheapest +one-way flight" on Google Flights (SEA→JFK, SFO→BOS, LAX→ORD; 13, 26 and 18 agent steps — +airport comboboxes and date pickers are genuinely fiddly) were grouped into one template +with **five** lifted parameters: ```json { - "template": "What is the latest release version of {{owner}}/{{repo}} on GitHub?", - "signature": { "params": ["owner", "repo"], "call": "python skill.py taskspec.json" }, + "template": "What is the cheapest flight from {{origin_city}} ({{origin_code}}) to {{destination_city}} ({{destination_code}}) on {{date}} (one-way)? ...", + "signature": { "params": ["origin_city", "origin_code", "destination_city", "destination_code", "date"], + "call": "python skill.py taskspec.json" }, "n_solves": 3 } ``` -It generalizes to repos none of the three solves ever saw, with no model in the loop: +On an unseen route it runs standalone — real playwright driving the live site, no model: ```bash -cd learned_library/what_is_the_latest_release_version_of_ow_c29dab8 -echo '{"params": {"owner": "numpy", "repo": "numpy"}}' > taskspec.json -python skill.py taskspec.json # -> {"retrieved_data": ["v2.5.1"]} -# pandas-dev/pandas -> ["v3.0.4"] — any public repo works +cd learned_library/what_is_the_cheapest_flight_from_origin__7725080 +cat > taskspec.json <<'EOF' +{"params": {"origin_city": "Seattle", "origin_code": "SEA", "destination_city": "Denver", + "destination_code": "DEN", "date": "2026-08-15"}, + "output_schema": {"type": "array", "items": {"type": "string"}}} +EOF +python skill.py taskspec.json # ~30 s -> {"retrieved_data": ["Frontier", "$68"]} (live price) ``` -`tests/skills/test_learned_example.py` locks these properties (n_solves ≥ 3, parameters -actually lifted, code compiles) in CI. +Consistency check on record: minutes apart, this standalone run and a fresh agent solve of +the same unseen route (verdict `use`, 15 steps) returned the identical answer. + +**The GitHub release skill** (same recipe, gold-checkable site): owner/repo lifted from +three solves; generalizes with no model — `numpy/numpy -> ["v2.5.1"]`, +`pandas-dev/pandas -> ["v3.0.4"]`. + +`tests/skills/test_learned_example.py` locks these properties for every skill in the +directory (n_solves ≥ 3, parameters actually lifted, code compiles) in CI. ## What a skill looks like diff --git a/src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/final_script_log.txt b/src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/final_script_log.txt new file mode 100644 index 0000000..0a609f0 --- /dev/null +++ b/src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/final_script_log.txt @@ -0,0 +1,17 @@ +opened: https://www.google.com/flights +screenshot: 01_home.png +set trip type to one-way +screenshot: 02_one_way.png +selected Where from?: Seattle (SEA) +selected Where to?: Denver (DEN) +screenshot: 03_route.png +set departure date: 2026-08-15 +screenshot: 04_date.png +submitted search +results appear loaded +screenshot: 05_results.png +opened Cheapest tab +screenshot: 06_cheapest_tab.png +candidate_1: airline='Frontier', price='$68', raw='Skip to main content Accessibility feedback Explore Flights Hotels Vacation rentals Change appearance Sign in Loading results Flight search One way 1 Economy (include Basic) Seattle SEA Denver DEN Filters All filters Stops Airlines Bags Price Times Emissions Connecting airports Duration Search resul' +chosen answer: ["Frontier", "$68"] +wrote agent_response.json: {"retrieved_data": ["Frontier", "$68"]} diff --git a/src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/meta.json b/src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/meta.json new file mode 100644 index 0000000..8682f18 --- /dev/null +++ b/src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/meta.json @@ -0,0 +1,24 @@ +{ + "template": "What is the cheapest flight from {{origin_city}} ({{origin_code}}) to {{destination_city}} ({{destination_code}}) on {{date}} (one-way)? Return the answer as a list: [airline, price].", + "provenance": "update-refined", + "site": "www.google.com", + "summary": "Refined from 3 gate-passed solves; parameterized + primitives.", + "signature": { + "params": [ + "origin_city", + "origin_code", + "destination_city", + "destination_code", + "date" + ], + "call": "python skill.py taskspec.json" + }, + "output_schema": { + "type": "array", + "items": { + "type": "string" + } + }, + "n_solves": 3, + "revisions": 1 +} \ No newline at end of file diff --git a/src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/screenshots/01_home.png b/src/webwright/skills/examples/learned_library/what_is_the_cheapest_flight_from_origin__7725080/screenshots/01_home.png new file mode 100644 index 0000000000000000000000000000000000000000..812b98b3816ec783c910dd5518e1ae5de43c53dc GIT binary patch literal 679730 zcmc%wWl)^U^9Bs#8a%iq!Gi~P2u_d$cM0wgTo(xtoCFURJh(f%3&Gu87I$~ueKzNR 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