Add webwright.skills: a memory/skill-library module (reuse + accumulate solved tasks)

[DEM1TASSE]

What

Adds webwright.skills — a memory / skill-library module (MVP): turn solved tasks into reusable,
executable code skills, retrieve + judge them at solve time, gate what enters the library, and grow
the library incrementally. A self-evolving loop on top of Webwright's code-as-action solves:

store -> retrieve -> decide(use/adapt/skip) -> gate -> evolve

This is the reuse + accumulation layer on top of crafted_cli: Webwright already turns one task
into a parameterized script; this 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.

Modular composition (~633 lines of core code; +898 total incl. tests/README)

Eight small, single-responsibility modules — each with a stable interface and a swappable
implementation:

module	LOC	role
skills/library.py	59	store — Skill + Library, skills on disk (skill.py + meta.json)
skills/retrieve.py	77	retrieve — task -> most relevant candidates (relevance)
skills/decide.py	50	decide — candidates -> use / adapt / skip (utility)
skills/gate.py	68	admission gate — gold / self_verify / none (keeps wrong solves out)
skills/update.py	192	evolve — incremental growth on the existing library; refine parameterizes + decomposes into primitives
skills/llm.py	67	backend-agnostic LLM via Webwright's Model (no endpoint/key hardcoded)
skills/prompt.py	26	non-invasive task-prompt hint (with_skill_hint)
tools/skill_use.py	72	solve-time tool the agent calls from bash (retrieve + decide)

How it plugs in (no change to the agent loop or default config)

1. Reuse at solve time — the skill_use tool, invoked from bash like self_reflection / image_qa:

   python -m webwright.tools.skill_use --task "<the task>" --library "$SKILL_LIBRARY_ROOT"

   returns {verdict: use|adapt|skip, skill_id, source_path, how_to_reuse}.
2. Growth after solving — the update CLI distills a batch of gate-passed solves into a
   parameterized, primitive-decomposed skill:

   python -m webwright.skills.update --manifest batch.json --library ./library

Validation

WebArena (gold gate): retrieval + reuse works across same-template held-out tasks; the gate keeps
a wrong solve out of the library; a second batch improves the existing skill in place (keeps the
working functions, adds robustness) instead of rewriting it. Step savings on held-out tasks: ~2–4
steps (larger when the task needs more exploration).

Real website (public GitHub, read-only): the full loop runs end-to-end — solve two repos from
scratch -> update builds a parameterized skill -> a held-out repo is solved by reusing it (the
agent calls skill_use, verdict use, answer correct). No step savings yet on this task: it is
a simple single-page lookup, and querying the library + adapting costs steps of its own; when the
task itself is short (a simple search/lookup that's quick to write from scratch), reuse adds little.
This is the main thing to optimize next — reuse pays off on harder, multi-step tasks where the saved
exploration outweighs the lookup/adapt overhead.

5 unit tests under tests/skills/ (library / gate / evolve / retrieve+decide).

Status: a deliberately simplistic MVP

Most steps are a single LLM call (retrieve = one catalog prompt, decide = one prompt, refine =
one batched prompt) — chosen for clarity, not yet for scale/accuracy. The point is the modular
shape: each stage has a stable interface, so swapping in something stronger (embedding retrieval,
a learned ranker, WebJudge / cross-source consistency for the real-website gate) is a localized
change that does not touch the others or the agent loop.

Scope

Purely additive (+898 lines total), confined to src/webwright/skills/,
src/webwright/tools/skill_use.py, src/webwright/config/skill_mode.yaml, tests/skills/, and a
README section. Of the +898, the actual implementation is ~511 lines of logic (non-blank,
non-comment, across the skills module + the skill_use tool); the remainder is tests (~157),
README/config (~89), and comments/docstrings/blank lines. No edits to the agent loop, models, or
existing configs. Module README: src/webwright/skills/README.md.

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