Skill Evaluator
Discover all skills in the project, score them across 6 criteria, and infer the appropriate effort level based on content analysis.
When to Use
- New project: run once to establish baseline quality
- Before committing a skill to a team repo
- After bulk-importing skills from another project
- When adding
effortfields for the first time (v2.1.80+)
What Gets Audited
All SKILL.md files and flat .md files found in:
.claude/skills/**~/.claude/skills/**(if requested)- Any path passed as argument:
/eval-skills./my-skills-dir
Scoring Criteria (14 pts per skill)
| # | Criterion | Max | What is checked |
|---|---|---|---|
| 1 | name | 1 | Present, lowercase, hyphens only, matches directory name |
| 2 | description | 2 | Present + has "Use when" / "when to" / trigger phrasing |
| 3 | allowed-tools | 2 | Present + not overly broad (Bash without scoping when read-only) |
| 4 | effort | 3 | Present (1pt) + appropriate for content (2pt based on inference) |
| 5 | content structure | 4 | Has Purpose/When section (1), has examples/usage (1), has clear workflow (1), no placeholder text (1) |
| 6 | bonus | +2 | argument-hint present (1), version/author metadata (1) |
Note: tags is NOT an officially supported frontmatter field in Claude Code. It is ignored by the runtime. Do not include it or score it as a quality criterion.Thresholds:
- ✅ Good: ≥11/14 (≥80%)
- ⚠️ Needs work: 8–10/14 (60–79%)
- ❌ Fix: <8/14 (<60%)
Effort Level Inference Engine
For each skill, analyze description + content and classify using these signals:
low — Mechanical execution, no design decisions
Signals:
- Verbs: commit, push, sync, scaffold, generate (template-based), format, rename, bump, wrap, convert
- No reasoning required: sequential steps, template instantiation, data fetching
- allowed-tools: Bash only, or Read-only
- No sub-agents spawned
- Short workflow (<5 steps)
Examples: /commit, /release-notes, /scaffold, /sync, /format
medium — Analysis with bounded scope, categorization
Signals:
- Verbs: review, triage, analyze, categorize, suggest, evaluate (single file or bounded scope)
- Requires pattern recognition but not architectural reasoning
- allowed-tools: Read + Grep + Bash combination
- May spawn 1-2 sub-agents but with predefined scope
- Produces structured output (tables, categorized lists)
Examples: /code-review (single PR), /issue-triage, /dependency-audit, /test-coverage
high — Design decisions, adversarial reasoning, cross-system analysis
Signals:
- Verbs: architect, redesign, threat-model, audit (security), orchestrate (multi-agent), score, assess trade-offs
- Requires reasoning about edge cases, attack vectors, or system-wide implications
- allowed-tools: broad access (Read + Write + Bash + external tools)
- Spawns multiple sub-agents or uses parallel execution
- Produces analysis with explicit uncertainty or trade-off sections
- Keywords in content: "security", "architecture", "adversarial", "pipeline", "threat", "design decision"
Examples: /security-audit, /architecture-review, /cyber-defense, /eval-agents
Mismatch flag
If a skill has effort: already set but the inferred level differs, flag it:
⚠️ Effort mismatch: declaredlow, inferredhigh— skill spawns 4 sub-agents and performs security analysis
Execution Instructions
Step 1 — Discovery
# Find all SKILL.md files
find .claude/skills -name "SKILL.md" 2>/dev/null
# Find flat skill files
find .claude/skills -maxdepth 1 -name "*.md" ! -name "README*" 2>/dev/null
# If argument provided, use that path insteadStep 2 — Parse each skill
For each skill file found:
- Read the full file
- Extract YAML frontmatter (between first
---and second---) - Parse: name, description, allowed-tools, effort, argument-hint, version
- Note presence/absence of each field
- Read the body content for structure analysis
Step 3 — Score and infer
Apply the scoring criteria above to each skill:
- Check frontmatter fields
- Evaluate description quality (does it answer "when to use"? is it under 1024 chars?)
- Evaluate allowed-tools scope (is Bash used when only Read would suffice? are tools scoped with wildcards when possible?)
- Infer effort level from content analysis
- Compare inferred vs declared effort (if set)
- Evaluate content structure (scan for "When to Use", "Purpose", "Example", "Workflow" sections)
Step 4 — Output
Produce a structured report:
# Skills Audit — [project name or path]
Date: [today] | Scanned: N skills
## Summary
| Status | Count |
|--------|-------|
| ✅ Good (≥80%) | N |
| ⚠️ Needs work (60–79%) | N |
| ❌ Fix (<60%) | N |
**Effort coverage**: N/N skills have effort field set
---
## Per-Skill Results
### [skill-name] — [score]/15 [✅/⚠️/❌]
| Criterion | Score | Notes |
|-----------|-------|-------|
| name | ✅ 1/1 | — |
| description | ⚠️ 1/2 | Missing "Use when" phrasing |
| allowed-tools | ✅ 2/2 | Well-scoped |
| effort | ❌ 0/3 | Missing — Recommended: high |
| content structure | ⚠️ 2/4 | No examples section |
**Effort inference**: `high` — skill performs security analysis with adversarial reasoning
Signals: "threat", "attack surface", "vulnerability scoring" in content; spawns 4 agents
**Priority fixes** (ordered by impact):
1. Add `effort: high` to frontmatter
2. Add "Use when" to description
3. Add a concrete usage example section
---After all skills: print a Fix Summary — all missing effort fields with recommended values, ready to copy-paste.
Fix Summary Format
At the end, print a ready-to-use patch block for all missing/mismatched effort fields:
## Recommended effort fields (copy-paste ready)
skill-name-1: effort: low # mechanical scaffold
skill-name-2: effort: high # security analysis, spawns agents
skill-name-3: effort: medium # code review, bounded scopeAnd a 1-line count: N skills need effort field · N mismatches · N missing allowed-tools