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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." 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).

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 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: `"Supe