Use when auditing Claude skills and commands for quality. Supports Quick Scan (changed skills only) and Full Stocktake modes with sequential subagent batch evaluation.

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: 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: Skill 7d use 30d use Description Phase 2 — Quality Evaluation Launch an Agent tool subagent ( general purpose agent ) 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: 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 . 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

skill-stocktake