Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
affaan-m avatar

Skill Stocktake

  • 1.4k installs
  • 238k repo stars
  • Updated August 5, 2026
  • affaan-m/ecc

This is a copy of skill-stocktake by affaan-m - installs and ranking accrue to the original listing.

skill-stocktake is an ECC slash-command skill that systematically audits and improves custom Claude skills and commands using a quality checklist and AI holistic judgment for developers maintaining agent skill libraries.

About

skill-stocktake is an ECC slash command (/skill-stocktake) that audits Claude skills and commands for quality using a checklist plus AI holistic judgment. It offers Quick Scan for recently changed skills and Full Stocktake for a complete sequential subagent batch evaluation across global and project paths such as ~/.claude/skills/ and the current working directory. Developers reach for skill-stocktake when custom skills drift in quality, duplicate triggers, or lack clear activation criteria after rapid iteration. The command scopes paths relative to the invocation directory and targets both global and project-local skill trees. Results help teams retire weak skills, tighten descriptions, and align slash commands before wider agent rollout.

  • Audits both global ~/.claude/skills/ and project-level .claude/skills/ directories
  • Two modes: Quick Scan for changed skills only and Full Stocktake for complete review
  • Combines structured quality checklist with AI holistic judgment via sequential subagent batch evaluation
  • Caches results to ~/.claude/skills/skill-stocktake/results.json for fast incremental scans
  • Explicitly lists scanned paths at the start of every run

Skill Stocktake by the numbers

  • 1,396 all-time installs (skills.sh)
  • +82 installs in the week ending Aug 5, 2026 (Skillselion tracking)
  • Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/affaan-m/ecc --skill skill-stocktake

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs1.4k
repo stars238k
Last updatedAugust 5, 2026
Repositoryaffaan-m/ecc

How do you audit Claude skills for quality issues?

Systematically audit and improve the quality of their custom Claude skills and slash commands.

Who is it for?

Developers maintaining growing libraries of Claude Code skills and slash commands who need periodic quality audits across global and project paths.

Skip if: Teams with no custom Claude skills or developers who only need one-off code review unrelated to skill metadata and triggers.

When should I use this skill?

Custom Claude skills or slash commands need a quality audit, especially after recent changes or before publishing a skill pack.

What you get

Skill quality audit report, checklist scores, and prioritized improvement recommendations per skill or slash command.

  • Skill audit report
  • Quality checklist results
  • Improvement recommendations

By the numbers

  • Provides 2 audit modes: Quick Scan and Full Stocktake
  • Targets global ~/.claude/skills/ and project-local skill paths

Files

SKILL.mdMarkdownGitHub ↗

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:

PathDescription
~/.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

ModeTriggerDuration
Quick Scanresults.json exists (default)5–10 min
Full Stocktakeresults.json absent, or /skill-stocktake full20–30 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)
Skill7d use30d useDescription

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:

VerdictMeaning
KeepUseful and current
ImproveWorth keeping, but specific improvements needed
UpdateReferenced technology is outdated (verify with WebSearch)
RetireLow 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

Skill7d useVerdictReason

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"
    }
  }
}

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

Related skills

How it compares

Pick skill-stocktake over manual skill README review when a whole skill library needs checklist-driven batch evaluation with subagent scoring.

FAQ

What modes does skill-stocktake support?

skill-stocktake supports Quick Scan for recently changed skills and Full Stocktake for a complete review using sequential subagent batch evaluation against a quality checklist plus holistic AI judgment.

Which directories does skill-stocktake audit?

skill-stocktake audits paths relative to the invocation directory, including global ~/.claude/skills/ and project-local skill folders in the current working tree.

AI & Agent Buildingagentsautomation

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.