lidge-jun Skill Stocktake
Audits all installed skills and commands using a quality checklist + AI holistic judgment. Supports two modes: Quick Scan for recently changed skills, and Full Stocktake for complete review.
Scope
The command targets skill directories relative to the invocation path:
| Path | Description |
|---|---|
| Global skills directory | Skills available to all projects |
{cwd}/skills/ |
Project-level skills (if present) |
At the start of Phase 1, explicitly list which paths were found and scanned.
Modes
| Mode | Trigger | Duration |
|---|---|---|
| Quick Scan | results.json exists (default) |
5–10 min |
| Full Stocktake | results.json absent, or explicit full flag |
20–30 min |
Quick Scan Flow
Re-evaluate only skills that changed since the last run:
- Read
results.json - Run:
bash scripts/quick-diff.sh results.json - If output is
[]: report "No changes since last run." and stop - Re-evaluate only changed files using Phase 2 criteria
- Carry forward unchanged skills from previous results
- Output only the diff
- Save merged results
Full Stocktake Flow
Phase 1 — Inventory
Run: bash scripts/scan.sh
The script enumerates skill files, extracts frontmatter, and collects UTC mtimes.
Scanning:
✓ global skills/ (17 files)
✗ {cwd}/skills/ (not found — global skills only)
| Skill | 7d use | 30d use | Description |
|---|
Phase 2 — Quality Evaluation
Launch a general-purpose sub-agent with the full inventory and checklist. Process ~20 skills per invocation to keep context manageable. Save intermediate results (status: "in_progress") after each chunk. Resume from the first unevaluated skill if interrupted.
Each skill is evaluated against:
- [ ] Content overlap with other skills checked
- [ ] Overlap with project-level config checked
- [ ] Freshness of technical references verified (web search if CLI flags / APIs are present)
- [ ] Usage frequency considered
Verdict criteria:
| Verdict | Meaning |
|---|---|
| Keep | Useful and current |
| Improve | Worth keeping, specific improvements needed |
| Update | Referenced technology is outdated |
| Retire | Low quality, stale, or cost-asymmetric |
| Merge into [X] | Substantial overlap with another skill |
Evaluation is holistic AI judgment. Guiding dimensions:
- Actionability: code examples, commands, or steps that let you act immediately
- Scope fit: name, trigger, and content are aligned
- Uniqueness: value not replaceable by another skill or project config
- Currency: technical references work in the current environment
Reason quality — the reason field is self-contained and decision-enabling:
- For Retire: state what defect was found and what covers the same need
- For Merge: name the target and describe what content to integrate
- For Improve: describe the specific change (section, action, target size)
- For Keep: restate the verdict rationale (not just "unchanged")
Phase 3 — Summary Table
| Skill | 7d use | Verdict | Reason |
|---|
Phase 4 — Consolidation
- Retire / Merge: present justification per file before confirming with user
- Improve: present specific suggestions with rationale
- Update: present updated content with sources checked
Archive / delete operations always require explicit user confirmation.
Results File Schema
results.json:
evaluated_at: set to actual UTC time of evaluation completion (date -u +%Y-%m-%dT%H:%M:%SZ).
{
"evaluated_at": "2026-02-21T10:00:00Z",
"mode": "full",
"batch_progress": { "total": 80, "evaluated": 80, "status": "completed" },
"skills": {
"skill-name": {
"path": "skills/skill-name/SKILL.md",
"verdict": "Keep",
"reason": "Concrete, actionable, unique value for X workflow",
"mtime": "2026-01-15T08:30:00Z"
}
}
}
Notes
- Evaluation is blind: same checklist applies to all skills regardless of origin
- No verdict branching by skill origin