Validate, test, and score the quality of skills within the claude-skills ecosystem. Comprehensive meta-skill: structure validation, Python script testing (syntax + imports + runtime + output format), multi-dimensional quality scoring with letter grades and tier classification (BASIC/STANDARD/POWERFUL). Use when authoring a new skill, auditing existing skills for tier promotion, setting up pre-commit hooks for skill quality, or integrating skill QA into CI.
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add alirezarezvani/claude-skills --skill "skill-tester" -g -a claude-code -yOr manually — clone and copy the skill directory (SKILL.md + companion files):
git clone --depth 1 https://github.com/alirezarezvani/claude-skills /tmp/claude-skills && cp -r /tmp/claude-skills/engineering/skills/skill-tester ~/.claude/skills/skill-tester-alirezarezvani-2This skill contains nested skills — each is browsable and installable on its own.
This skill is a directory: SKILL.md is the entry point; the files below ship with it.
---
name: "skill-tester"
description: "Validate, test, and score the quality of skills within the claude-skills ecosystem. Comprehensive meta-skill: structure validation, Python script testing (syntax + imports + runtime + output format), multi-dimensional quality scoring with letter grades and tier classification (BASIC/STANDARD/POWERFUL). Use when authoring a new skill, auditing existing skills for tier promotion, setting up pre-commit hooks for skill quality, or integrating skill QA into CI."
---
# Skill Tester
**Tier**: POWERFUL · **Category**: Engineering Quality Assurance · **Dependencies**: None (Python stdlib only)
Meta-skill that validates, tests, and scores skills in this repository. Four tools, run from the **repo root** with full paths:
1. **`scripts/skill_validator.py`** — structure + documentation compliance
2. **`scripts/script_tester.py`** — Python script syntax/imports/runtime/output testing
3. **`scripts/quality_scorer.py`** — multi-dimensional scoring with letter grade
4. **`scripts/security_scorer.py`** — security posture scoring (also available via `quality_scorer.py --include-security`)
> **Scope note:** this skill's tier line-count minimums measure *legacy* skills. For authoring *new* skills, `engineering/write-a-skill` (SKILL.md under ~100 lines, Matt Pocock doctrine) is the binding standard — do not pad a new skill to satisfy a tier minimum here.
## Quick Start (exact, runnable from repo root)
```bash
# 1. Validate structure (exit non-zero on failure — usable as a gate)
python3 engineering/skills/skill-tester/scripts/skill_validator.py engineering/skills/self-eval --json
# 2. Test the skill's Python scripts (30s default timeout per script)
python3 engineering/skills/skill-tester/scripts/script_tester.py engineering/skills/self-eval --json
# 3. Score quality (fail CI below threshold with --minimum-score)
python3 engineering/skills/skill-tester/scripts/quality_scorer.py engineering/skills/self-eval --json --detailed --minimum-score 75
```
Consume the JSON: validator emits `overall_score`, `compliance_level`, per-check `checks{}`; scorer emits `overall_score`, `letter_grade`, `tier_recommendation`, `dimensions`, and an `improvement_roadmap` — work the roadmap top-down, then re-run until the target score is met.
For repo-wide auditing prefer `scripts/audit_skills.py` at the repo root (wraps the write-a-skill checklist runner across all skills).
## What Each Tool Checks
### skill_validator.py
- SKILL.md frontmatter parsing, required sections, minimum line counts per tier (`--tier BASIC|STANDARD|POWERFUL`)
- Required structure: SKILL.md, README.md, scripts/, references/, assets/, expected_outputs/
- Python scripts: argparse present, stdlib-only imports
### script_tester.py
- AST-based syntax validation; import analysis (flags external dependencies)
- Controlled execution with timeout protection (`--timeout`, default 30s)
- `--help` functionality verification; sample-data runs compared against expected_outputs/
### quality_scorer.py
Four dimensions, 25% each: **Documentation** (depth, examples, references), **Code Quality** (complexity, error handling, output consistency), **Completeness** (required dirs, sample data, expected outputs), **Usability** (help text, example clarity). Outputs 0-100 + A-F grade + tier recommendation.
## Tier Classification
| Tier | SKILL.md | Scripts | CLI surface |
|---|---|---|---|
| BASIC | ≥ 100 lines | 1 (100-300 LOC) | basic argparse |
| STANDARD | ≥ 200 lines | 1-2 (300-500 LOC) | subcommands, JSON + text output |
| POWERFUL | ≥ 300 lines | 2-3 (500-800 LOC) | multiple modes, CI integration |
(Advisory for legacy skills; new skills follow write-a-skill — see scope note above.)
## CI Integration
```yaml
# GitHub Actions: gate changed skills
- name: "validate-changed-skills"
run: |
for skill in $changed_skills; do
python3 engineering/skills/skill-tester/scripts/skill_validator.py "$skill" --json
python3 engineering/skills/skill-tester/scripts/script_tester.py "$skill"
python3 engineering/skills/skill-tester/scripts/quality_scorer.py "$skill" --minimum-score 75
done
```
Pre-commit hook: run the validator on the staged skill directory and block the commit on non-zero exit.
## Verification Loop
A skill "passes" when, in one run from repo root:
1. `skill_validator.py <skill> --json` exits 0,
2. `script_tester.py <skill>` reports all scripts passing, and
3. `quality_scorer.py <skill> --minimum-score <target>` exits 0.
If any step fails, apply the top `improvement_roadmap` item and re-run all three — never report a partial pass.
## Troubleshooting
- **Timeout errors** → raise `--timeout` or optimize the script under test
- **Import failures** → external deps detected; stdlib-only is the repo policy
- **Tier misclassification** → check line counts/LOC against the tier table; remember the write-a-skill exception for new skills
References: `references/` holds the structure specification, tier requirements matrix, and scoring rubric the tools implement.
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Use when completing tasks, implementing major features, or before merging to verify work meets requirements
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes