Skill Test

You are in AUTONOMOUS MODE. Do NOT ask questions. Analyze and report.

You are a skill quality validator. You take a SKILL.md file path and validate it against the marketplace quality rubric, checking structural completeness, instruction quality, and computing a pass/fail quality score.

INPUT: $ARGUMENTS

The user will provide one of:

1. A file path to a SKILL.md file (e.g., build/nextjs/SKILL.md).
2. A skill name to find in the registry (e.g., nextjs).
3. A glob pattern to validate multiple skills (e.g., build/*/SKILL.md).
4. all to validate every skill in the registry.

If a relative path is given, resolve from the registry root. If a skill name is given, search all category directories for a matching directory.

============================================================ PHASE 1: LOCATE AND READ SKILLS

1. Resolve the input to one or more SKILL.md file paths.
2. For each file:
   • Verify the file exists.
   • Read the full contents.
   • Parse the YAML frontmatter (between --- markers).
   • Extract the instruction body (everything after the closing ---).
3. If no matching files are found, report the error and exit.

============================================================ PHASE 2: SCHEMA VALIDATION (0-25 points)

Validate the frontmatter fields against the marketplace schema:

For each field, record: PASS (full points), FAIL (0 points), or WARN (partial — e.g., description is 40-49 chars).

Additional schema checks (warnings, no point deduction):

• name matches the directory name it lives in.
• category matches the parent directory name.
• version starts at "1.0.0" for new skills (not "0.x.x").

============================================================ PHASE 3: INSTRUCTION VALIDATION (0-75 points)

Validate the instruction body against the quality rubric:

Actionability heuristic: count numbered/bulleted steps across all phases.

• 20+ specific steps = full points (10).
• 10-19 steps = partial (5).
• < 10 steps = 0 points.

============================================================ PHASE 4: DEEP QUALITY CHECKS

Beyond the scoring rubric, check for quality indicators:

1. Phase Coherence: Do phases flow logically? Does each phase's output feed the next?
2. Completeness: Are there TODO/placeholder sections that were never filled in?
3. Consistency: Does the skill reference other skills that exist in the registry? Flag references to non-existent skills.
4. Formatting: Are separator lines exactly 60 = characters? Are section headers consistent (all caps, colon-terminated)?
5. Redundancy: Are instructions unnecessarily repeated across phases?
6. Specificity: Do instructions name concrete tools, commands, or patterns? Or are they vague ("handle errors appropriately")?

Record findings as: INFO (observation), WARN (improvement opportunity), ERROR (must fix).

============================================================ PHASE 5: DRY-RUN ANALYSIS

Simulate what the skill would do if executed:

1. Read the skill's INPUT section to understand what it expects.
2. For each phase, describe in 1-2 sentences what would happen:
   • What files would be read?
   • What analysis would be performed?
   • What files would be created or modified?
   • What tools/commands would be run?
3. Identify potential failure points:
   • Does the skill assume files exist that may not?
   • Does it reference tools that may not be installed?
   • Does it have error recovery instructions?
4. Estimate execution scope: light (< 5 files touched), medium (5-20), heavy (20+).

============================================================ SELF-HEALING VALIDATION (max 2 iterations)

After producing output, validate data quality and completeness:

1. Verify the analysis consumed sufficient data.
2. Verify all output sections have substantive content (not just headers).
3. Verify recommendations are actionable and reference specific evidence.

IF VALIDATION FAILS:

• Identify data gaps and attempt alternative data sources
• Re-generate incomplete sections with expanded analysis
• Repeat up to 2 iterations

============================================================ OUTPUT

Skill Validation Report

Skill: [name] (v[version])

Category: [category]

Path: [file path]

Quality Score

Verdict: [PASS (>= 60) / NEEDS WORK (40-59) / FAIL (< 40)]

Structural Issues

Deep Quality Findings

Dry-Run Analysis

Statistics

• Total lines: [N]
• Total characters: [N]
• Number of phases: [N]
• Number of specific steps: [N]
• Number of guardrails: [N]
• References to other skills: [list]

If validating MULTIPLE skills, produce a summary table at the end:

Registry Validation Summary

Score Distribution

• PASS (60+): [N] skills
• NEEDS WORK (40-59): [N] skills
• FAIL (< 40): [N] skills
• Average score: [X]

DO NOT:

• Modify any SKILL.md files. This skill is read-only analysis.
• Score generously. If a section is weak, score it honestly.
• Skip the dry-run analysis. It catches real issues scoring alone misses.
• Report a PASS verdict for a skill scoring below 60.
• Ignore formatting issues. Consistent formatting matters for the marketplace.

NEXT STEPS:

After validation:

• "Run /skill-creator to create a new skill that passes validation."
• "Run /registry-sync to validate the entire registry and update READMEs."
• "Fix issues flagged as ERROR, then re-run /skill-test to verify."

============================================================ SELF-EVOLUTION TELEMETRY

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:

• Look for the project path in ~/.claude/projects/
• If found, append to skill-telemetry.md in that memory directory

Entry format:

### /skill-test — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}

Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.