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).

1. Read ~/.claude/skills/skill-stocktake/results.json
2. Run: bash ~/.claude/skills/skill-stocktake/scripts/quick-diff.sh \ ~/.claude/skills/skill-stocktake/results.json (Project dir is auto-detected from $PWD/.claude/skills; pass it explicitly only if needed)
3. If output is []: report "No changes since last run." and stop
4. Re-evaluate only those changed files using the same Phase 2 criteria
5. Carry forward unchanged skills from previous results
6. Output only the diff
7. Run: bash ~/.claude/skills/skill-stocktake/scripts/save-results.sh \ ~/.claude/skills/skill-stocktake/results.json <<< "$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 a Task tool subagent (Explore agent, model: opus) with the full inventory and checklist. 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

Skill	7d use	Verdict	Reason

Phase 4 — Consolidation

1. 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)
2. 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
3. Update: present updated content with sources checked
4. 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"
    }
  }
}

Worked Example — Stocktake with 3 Findings

Scenario: Full Stocktake run on a project with 3 skills worth highlighting.

Phase 1 — Inventory output:

Scanning:
  ✓ ~/.claude/skills/         (42 files)
  ✓ ~/projects/my-api/.claude/skills/   (3 files)

Phase 2 — Quality Evaluation results (excerpt):

Skill	7d use	Verdict	Reason
api-pagination-filtering	8	Keep	Concrete cursor handler (TypeScript), combined request example, and when-to-use decision table. Unique — not covered by api-design. No overlap found.
chatlog-to-article	0	Merge into content-creation	38-line thin wrapper; no code examples. Step 4 of content-creation already covers the same "extract article angle" workflow. Integrate the single unique tip ("open with a concrete failure, not a lesson") as a callout note in content-creation.
legacy-http-client	0	Retire	References node-fetch v2 patterns superseded by the native fetch in Node 18+. The http-client-patterns skill covers the same use cases with current API. No unique content remains after removing the deprecated examples.

Phase 3 — Consolidation actions proposed:

Retire:  ~/.claude/skills/legacy-http-client/SKILL.md
  → Reason: node-fetch v2 is EOL; covered by http-client-patterns
  → Impact: no dependent skills or MEMORY.md references found
  Confirm? [y/N]

Merge:   ~/.claude/skills/chatlog-to-article/SKILL.md → content-creation
  → Action: copy "open with concrete failure" tip to content-creation as a callout
  → Then delete chatlog-to-article/
  Confirm? [y/N]

Result after user confirms both:

• legacy-http-client/ — deleted
• content-creation/SKILL.md — updated with merged tip
• chatlog-to-article/ — deleted
• api-pagination-filtering/ — unchanged (Keep)

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Notes

• Evaluation is blind: the same checklist applies to all skills regardless of origin (clarc, self-authored, auto-extracted)
• Archive / delete operations always require explicit user confirmation
• No verdict branching by skill origin