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 Webwright's code-as-action solves: it consumes
the final_script.py every 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.

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: 10 templates × 3 domains — reuse lifts accuracy +15pp and saves steps on held-out tasks

10 retrieve-type task templates across shopping_admin / gitlab / map. Per template: 3 train
tasks build the library (solved from scratch; only gold-verified solves are admitted), 2 held-out
tasks (unseen instances of the template — different parameter values) measure reuse. Every task is
solved both WITH the library and from scratch (BASE) — 80 solves total.

set	WITH library	BASE (scratch)	delta
held-out (20 unseen instances)	70% · 14.7 steps	55% · 17.1 steps	+15pp accuracy, −2.4 steps
train (30 seen instances)	86% · 13.7 steps	76% · 15.9 steps	+10pp, −2.2 steps

Highlights:

• Reuse rescues failures: 4 held-out tasks that BASE could not solve at all are solved with the
  library; net reuse-wins 7 vs 1 regression across the 20 held-out tasks.
• Large savings where exploration is expensive: e.g. a gitlab commit-counting task drops from
  33 steps (scratch) to 10 (reuse); a map routing task from 29 to 16.
• The gate works: 7 of 30 train solves were wrong and were kept out — the library only ever
  contains verified-correct skills.
• Parameterization generalizes: update.refine lifts per-instance differences into parameters
  and bakes the aggregation logic (top-n ranking, commit counting, route-time extraction) into
  primitives, so unseen instances of the template solve by a direct use of the skill.
• Retrieval stays reliable as the library grows: all 20 held-out solves picked the correct
  skill from the shared library (grown to 10 skills over the run), including telling apart two
  near-duplicate gitlab commit-counting skills (by-date vs by-period).
• Mixed-template batches evolve safely: traces from 4 templates fed mixed across 3 sequential
  evolve batches produce 4 independent skills — new templates get added, existing skills are
  refined in place (working functions kept), skills with no new traces stay byte-identical, and
  zero cross-contamination between skills; held-out reuse against the mixed-built library matches
  the per-template-built one.

Real website (public GitHub, read-only): the full loop 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). Reuse pays off most on
multi-step tasks where saved exploration outweighs the lookup overhead (see the WebArena numbers);
on short single-page lookups it is roughly break-even.

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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