claude/.claude/audits/DARWIN-SKILL-2026-05-12.md

Darwin Skill Optimization — 2026-05-12

Branch: auto-optimize/20260512-1319 Scope: 28 dirs in ~/Documents/claude/skills/ (cwd = source of truth, ~/.claude/skills is runtime mirror) Eval mode: structural dim 1-7 full / dim 8 dry_run Phase reached: 2 (round 1 per skill — rounds 2-3 skipped for diminishing returns) Runtime: ~30 min

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Baseline scorecard (23 scoreable + 5 excluded)

23 personal skills scored. 5 broken gstack symlinks excluded (not user-owned).

Rank	Skill	Baseline	Weakest
1	status	45.3	D3
2	refactor	48.4	D4
3	plugin-check	59.2	D4
4	skills-perso	66.4	D3
5	commit-change	69.6	D3
6	hotfix	74.1	D4
7	code-clean	74.7	D3
8	feat	74.7	D4
9	doc	76.2	D3
10	analyze	76.4	D4
11	validate	76.4	D4
12	geo	76.5	D4
13	profile	78.3	D3
14	close	79.2	D3
15	bugfix	79.3	D4
16	seo	82.5	D4
17	onboard	83.5	D8
18	client-handover	83.9	D8
19	init-project	84.1	D8
20	ship-feature	84.3	D8
21	prune-memory	84.6	D8
22	harden	85.5	D8
*	graphify	29.0	D4

graphify scored on partial read (62KB SKILL.md exceeded token limit). Real fix likely a Phase 2.5 exploratory rewrite, not hill-climb — deferred.

Excluded broken symlinks (out of scope): benchmark-models, context-restore, context-save, make-pdf, plan-tune. All point to skills-external/gstack/<name>/SKILL.md which doesn't exist.

Average across 23: 75.6 / 100.

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Phase 2 — optimized skills (bottom 5)

User-confirmed queue: bottom 5 by baseline (graphify deferred).

Skill	Before	After	Δ	Round	Status	Change
status	45.3	76.2	+30.9	1	keep	D3 fallback + frontmatter triggers
refactor	48.4	74.3	+25.9	1	keep	D3 fallback + frontmatter triggers
plugin-check	59.2	76.8	+17.6	1	keep	D3 fallback + triggers + advisory note
skills-perso	66.4	80.1	+13.7	1	keep	D3 "Known limits of heuristic" section
commit-change	69.6	83.5	+13.9	1	keep	D3 fallback + pre-flight git checks (HEAD/identity)

Average: 58.0 → 78.2 (+20.2 per skill). Kept: 5/5. Reverted: 0.

Per-skill commit

• 512df48 optimize status: round 1 — D3 fallback + triggers
• 079074d optimize refactor: round 1 — D3 fallback + D1 triggers
• d3dd31c optimize plugin-check: round 1 — D3 fallback + D1 triggers + advisory clarification
• 134561d optimize skills-perso + commit-change: round 1 — D3 edge cases

Same pattern across the bottom 4 dispatchers

status, refactor, plugin-check, commit-change are all thin dispatchers (15-30 lines, body = "load and follow agents/.md + $ARGUMENTS"). Baseline rubric penalized them harshly across most dims because there's "nothing to score". Round 1 fix is invariant:

1. Add fallback clause: "If agent file unreachable, emit <X> agent missing. and STOP."
2. Add explicit triggers in frontmatter description.
3. For destructive skills (refactor, commit-change) add safety rationale ("never improvise — silent behavior change").

skills-perso is the outlier — full implementation, not a dispatcher. Improvement was a "Known limits of the heuristic" section that names false-positives (agent refs in fenced code blocks), false-negatives (custom agent paths), the owner: user override path, and frontmatter-malformed silent-skip behavior.

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Methodology notes & caveats

1. Eval math drift. Round-1 subagents occasionally used Σ(dim×weight)/100 instead of the correct /10, and one used D8 weight 7 instead of 25. Totals reported above are recomputed by the main thread from the subagents' dim-by-dim judgments, so absolute numbers are trustworthy even though the subagents' written totals sometimes weren't.

2. Score recalibration at round 1. The two passes (baseline subagent vs round-1 eval subagent) are different model invocations and re-anchor independently. The +30 jump for status reflects partly real improvement (fallback section) and partly that the baseline subagent under-scored "by-design thin dispatchers" across the board. Δ direction is reliable; absolute magnitudes are noisy.

3. 150% size cap is tight for thin dispatchers. status (15 lines, 776B) and refactor (15 lines, 379B) had ~22-line / ~570B caps. Multiple trim cycles per skill to fit. Recommend the spec consider an absolute-byte floor (e.g., min cap = max(150%, 1000B)) for cases where the original was minimal.

4. D8 (effect) untested. Spec allows dry_run; all 5 logged as such. To upgrade, would need to run each skill twice (with/without optimized SKILL.md) and compare outputs. Saved for a follow-up pass.

5. Rounds 2-3 skipped. Round 1 Δ for all 5 was big (+13.7 to +30.9). Round 2 expected Δ is small (returns flatten near 80). Better marginal value optimizing the next-lowest skills (hotfix, code-clean, feat at 74-75) than re-grinding the bottom 5.

6. Screenshot.mjs is macOS-only. Script hardcodes /Users/alchain/.npm-global/.... To generate PNG cards on Linux, swap to npx playwright screenshot file://… out.png --viewport-size=960,1280 or fix the script's require() path.

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Suggested next passes

Priority	Action
P1	Hill-climb rank 6-12 (hotfix, code-clean, feat, doc, analyze, validate, geo) — same dispatcher fallback pattern; expected +5 to +15 per skill
P1	Phase 2.5 exploratory rewrite of graphify (62KB SKILL.md, baseline 29)
P2	Real dim-8 effect testing on the top 5 (onboard, client-handover, init-project, ship-feature, harden) to confirm baseline isn't masking issues
P3	Round 2 on bottom 5 — target next-weakest dim (D4 checkpoints for destructive skills, D5 specificity for status)
P3	Fix scripts/screenshot.mjs for Linux + generate PNG cards

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Files touched (committed on auto-optimize/20260512-1319)

skills/status/SKILL.md          (+9 -2)
skills/refactor/SKILL.md        (+3 -4)
skills/plugin-check/SKILL.md    (+5 -5)
skills/skills-perso/SKILL.md    (+18 -0)
skills/commit-change/SKILL.md   (+5 -3)
skills/close/test-prompts.json       (new)
skills/graphify/test-prompts.json    (new)
skills/harden/test-prompts.json      (new)
skills/profile/test-prompts.json     (new)
skills/prune-memory/test-prompts.json (new)

5 commits on branch. To merge:

git checkout master
git merge --no-ff auto-optimize/20260512-1319

Or review per-commit and cherry-pick. No master commits yet — branch is purely additive.

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results.tsv

Persisted to ~/.agents/skills/darwin-skill/results.tsv (28 baseline rows + 5 round-1 rows = 33 total).