skill-quality-analyzer

name	skill-quality-analyzer
description	Analyze the quality of a OpenCode skill across discovery, structure, safety, examples, and maintainability.

Skill Quality Analyzer

Static analysis system for OpenCode skills. Identifies quality issues, scores the skill across six dimensions, and produces actionable recommendations before distribution.

Workflow

1. Gather hard facts.
   • Does SKILL.md exist?
   • Are name and description present?
   • Are referenced references/, scripts/, and assets/ files real?
   • Are behavior flags intentional?
   • If the skill declares or implies an upstream baseline, does the local file clearly differentiate upstream guidance from local adaptations?
2. Read the target skill semantically.
   • Is the promise clear?
   • Is the scope differentiated?
   • Is the workflow executable?
   • If a vendored upstream reference exists, does the local skill still track it where it should?
3. Synthesize a severity-ranked report.

If analyzer.py exists in this skill's directory, run it as the hard-metrics step:

python3 skills/skill-quality-analyzer/analyzer.py --skill-path /path/to/skill

Output Formats

Score Card

{
  "overall_score": 85,
  "dimensions": {
    "discovery_surface": 90,
    "structure": 85,
    "examples_evals": 80,
    "execution_safety": 80,
    "best_practices": 85,
    "maintainability": 90
  }
}

Report

Include:

• executive summary
• dimension breakdown
• severity-ranked issues
• prioritized recommendations

Six Quality Dimensions

1. Discovery Surface (20%)

What it measures:

• distinct name
• clear description
• low overlap with neighboring skills

2. Structure (20%)

What it measures:

• valid frontmatter
• coherent section layout
• clean markdown structure

3. Examples / Evals (15%)

What it measures:

• positive examples
• negative or redirect examples
• borderline examples
• realistic prompt coverage

4. Execution Safety (15%)

What it measures:

• disable-model-invocation is correct
• user-invocable is correct
• allowed-tools is justified
• model/agent/context choices make sense

5. Best Practices (15%)

What it measures:

• OpenCode naming conventions
• proper package layout
• no misleading platform branding
• OMC-only metadata clearly treated as project-specific
• upstream-derived skills clearly identify what is baseline versus local adaptation

6. Maintainability (15%)

What it measures:

• modular skill package
• low clutter
• references split before the skill becomes hard to scan
• sustainable file organization

How to Use

Basic

Analyze the quality of my skill-creator skill
Run quality analysis on skills/skill-debugger/

Detailed

Generate a detailed quality report for skill-tester
Compare skill-creator and skill-debugger against current best practices

Batch

Analyze all skills in skills/ and rank them by quality

Recommended Pipeline

1. skill-creator
2. skill-quality-analyzer
3. skill-debugger
4. skill-tester

Best Practices

1. Analyze before distribution
2. Target 80+ for production skills
3. Fix critical issues first
4. Compare against current official examples, not only local tradition
5. For upstream-derived skills, compare against the vendored upstream baseline before scoring local adaptations
6. Re-run after meaningful edits

Limitations

• Static analysis cannot prove routing behavior
• Prompt-level behavior still needs skill-tester
• Human judgment is still required for overlap and scope

When NOT to Use This Skill

• For root-cause analysis of why a skill is not selected, use skill-debugger
• For prompt-level verification, use skill-tester
• For first-draft authoring or upgrades, use skill-creator