skill-stocktake Skill | Agent Skills

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.

Maestro Integration

Lifecycle: review Activates when: maestro:new-track detects relevant tech in tech-stack.md, or maestro:implement encounters matching task types.

Phase Guidance

In maestro:review: Run skill quality audit during review phase. Use Quick Scan for changed skills, Full Stocktake for comprehensive assessment.

Related Skills

maestro:eval-harness
maestro:plankton-code-quality
maestro:verification-loop

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 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

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

Relationship to Maestro Workflow

/maestro:new-track -- Detects this skill during Step 9.5 (skill matching)
/maestro:implement -- Loads this skill's guidance during task execution
/maestro:review -- Uses checklists as review criteria

Agent Skills: skill-stocktake

Install this agent skill to your local

Skill Files