skill-stocktake

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

8 stars

Best use case

skill-stocktake is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

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

Teams using skill-stocktake should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/skill-stocktake/SKILL.md --create-dirs "https://raw.githubusercontent.com/marvinrichter/clarc/main/skills/skill-stocktake/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/skill-stocktake/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How skill-stocktake Compares

Feature / Agentskill-stocktakeStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

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

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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:

```bash
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`.

```json
{
  "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)

---

## 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

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