| name | skill-stocktake |
| description | Use when auditing Claude skills and commands for quality. Supports Quick Scan (changed skills only) and Full Stocktake modes with sequential subagent batch evaluation. |
| metadata | {"origin":"ECC"} |
skill-stocktake
Slash command (/skill-stocktake) that audits all Claude skills and commands using a quality checklist + AI holistic judgment. Supports two modes: Quick Scan for recently changed skills, and Full Stocktake for a complete review.
Scope
The command targets the following paths relative to the directory where it is invoked:
| Path | Description |
|---|
~/.claude/skills/ | Global skills (all projects) |
{cwd}/.claude/skills/ | Project-level skills (if the directory exists) |
At the start of Phase 1, the command explicitly lists which paths were found and scanned.
Targeting a specific project
To include project-level skills, run from that project's root directory:
cd ~/path/to/my-project
/skill-stocktake
If the project has no .claude/skills/ directory, only global skills and commands are evaluated.
Modes
| Mode | Trigger | Duration |
|---|
| Quick Scan | results.json exists (default) | 5–10 min |
| Full Stocktake | results.json absent, or /skill-stocktake full | 20–30 min |
Results cache: ~/.claude/skills/skill-stocktake/results.json
Quick Scan Flow
Re-evaluate only skills that have changed since the last run (5–10 min).
- Read
~/.claude/skills/skill-stocktake/results.json
- Run:
bash ~/.claude/skills/skill-stocktake/scripts/quick-diff.sh \ ~/.claude/skills/skill-stocktake/results.json
(Project dir is auto-detected from $PWD/.claude/skills; pass it explicitly only if needed)
- If output is
[]: report "No changes since last run." and stop
- Re-evaluate only those changed files using the same Phase 2 criteria
- Carry forward unchanged skills from previous results
- Output only the diff
- Run:
bash ~/.claude/skills/skill-stocktake/scripts/save-results.sh \ ~/.claude/skills/skill-stocktake/results.json <<< "$EVAL_RESULTS"
Full Stocktake Flow
Phase 1 — Inventory
Run: bash ~/.claude/skills/skill-stocktake/scripts/scan.sh
The script enumerates skill files, extracts frontmatter, and collects UTC mtimes.
Project dir is auto-detected from $PWD/.claude/skills; pass it explicitly only if needed.
Present the scan summary and inventory table from the script output:
Scanning:
✓ ~/.claude/skills/ (17 files)
✗ {cwd}/.claude/skills/ (not found — global skills only)
| Skill | 7d use | 30d use | Description |
|---|
Phase 2 — Quality Evaluation
Launch an Agent tool subagent (general-purpose agent) with the full inventory and checklist:
Agent(
subagent_type="general-purpose",
prompt="
Evaluate the following skill inventory against the checklist.
[INVENTORY]
[CHECKLIST]
Return JSON for each skill:
{ \"verdict\": \"Keep\"|\"Improve\"|\"Update\"|\"Retire\"|\"Merge into [X]\", \"reason\": \"...\" }
"
)
The subagent reads each skill, applies the checklist, and returns per-skill JSON:
{ "verdict": "Keep"|"Improve"|"Update"|"Retire"|"Merge into [X]", "reason": "..." }
Chunk guidance: Process ~20 skills per subagent invocation to keep context manageable. Save intermediate results to results.json (status: "in_progress") after each chunk.
After all skills are evaluated: set status: "completed", proceed to Phase 3.
Resume detection: If status: "in_progress" is found on startup, resume from the first unevaluated skill.
Each skill is evaluated against this checklist:
- [ ] Content overlap with other skills checked
- [ ] Overlap with MEMORY.md / CLAUDE.md checked
- [ ] Freshness of technical references verified (use WebSearch if tool names / CLI flags / APIs are present)
- [ ] Usage frequency considered
Verdict criteria:
| Verdict | Meaning |
|---|
| Keep | Useful and current |
| Improve | Worth keeping, but specific improvements needed |
| Update | Referenced technology is outdated (verify with WebSearch) |
| Retire | Low quality, stale, or cost-asymmetric |
| Merge into [X] | Substantial overlap with another skill; name the merge target |
Evaluation is holistic AI judgment — not a numeric rubric. Guiding dimensions:
- Actionability: code examples, commands, or steps that let you act immediately
- Scope fit: name, trigger, and content are aligned; not too broad or narrow
- Uniqueness: value not replaceable by MEMORY.md / CLAUDE.md / another skill
- Currency: technical references work in the current environment
Reason quality requirements — the reason field must be self-contained and decision-enabling:
- Do NOT write "unchanged" alone — always restate the core evidence
- For Retire: state (1) what specific defect was found, (2) what covers the same need instead
- Bad:
"Superseded"
- Good:
"disable-model-invocation: true already set; superseded by continuous-learning-v2 which covers all the same patterns plus confidence scoring. No unique content remains."
- For Merge: name the target and describe what content to integrate
- Bad:
"Overlaps with X"
- Good:
"42-line thin content; Step 4 of chatlog-to-article already covers the same workflow. Integrate the 'article angle' tip as a note in that skill."
- For Improve: describe the specific change needed (what section, what action, target size if relevant)
- Bad:
"Too long"
- Good:
"276 lines; Section 'Framework Comparison' (L80–140) duplicates ai-era-architecture-principles; delete it to reach ~150 lines."
- For Keep (mtime-only change in Quick Scan): restate the original verdict rationale, do not write "unchanged"
- Bad:
"Unchanged"
- Good:
"mtime updated but content unchanged. Unique Python reference explicitly imported by rules/python/; no overlap found."
Phase 3 — Summary Table
Phase 4 — Consolidation
- Retire / Merge: present detailed justification per file before confirming with user:
- What specific problem was found (overlap, staleness, broken references, etc.)
- What alternative covers the same functionality (for Retire: which existing skill/rule; for Merge: the target file and what content to integrate)
- Impact of removal (any dependent skills, MEMORY.md references, or workflows affected)
- Improve: present specific improvement suggestions with rationale:
- What to change and why (e.g., "trim 430→200 lines because sections X/Y duplicate python-patterns")
- User decides whether to act
- Update: present updated content with sources checked
- Check MEMORY.md line count; propose compression if >100 lines
Results File Schema
~/.claude/skills/skill-stocktake/results.json:
evaluated_at: Must be set to the actual UTC time of evaluation completion.
Obtain via Bash: date -u +%Y-%m-%dT%H:%M:%SZ. Never use a date-only approximation like T00:00:00Z.
{
"evaluated_at": "2026-02-21T10:00:00Z",
"mode": "full",
"batch_progress": {
"total": 80,
"evaluated": 80,
"status": "completed"
},
"skills": {
"skill-name": {
"path": "~/.claude/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: the same checklist applies to all skills regardless of origin (ECC, self-authored, auto-extracted)
- Archive / delete operations always require explicit user confirmation
- No verdict branching by skill origin
