القائمة
Skill Tester
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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| name | skill-tester |
| description | Skill Tester |
Name: skill-tester Tier: POWERFUL Category: Engineering Quality Assurance Dependencies: None (Python Standard Library Only) Author: Claude Skills Engineering Team Version: 1.0.0 Last Updated: 2026-02-16
The Skill Tester is a comprehensive meta-skill designed to validate, test, and score the quality of skills within the claude-skills ecosystem. This powerful quality assurance tool ensures that all skills meet the rigorous standards required for BASIC, STANDARD, and POWERFUL tier classifications through automated validation, testing, and scoring mechanisms.
As the gatekeeping system for skill quality, this meta-skill provides three core capabilities:
This skill is essential for maintaining ecosystem consistency, enabling automated CI/CD integration, and supporting both manual and automated quality assurance workflows. It serves as the foundation for pre-commit hooks, pull request validation, and continuous integration processes that maintain the high-quality standards of the claude-skills repository.
Automatically classifies skills based on complexity and functionality:
The skill-tester follows a modular architecture where each component serves a specific validation purpose:
All validation is performed against well-defined standards documented in the references/ directory:
Designed for seamless integration into existing development workflows:
# Primary validation workflow
validate_skill_structure() -> ValidationReport
check_skill_md_compliance() -> DocumentationReport
validate_python_scripts() -> ScriptReport
generate_compliance_score() -> float
Key validation checks include:
# Core testing functions
syntax_validation() -> SyntaxReport
import_validation() -> ImportReport
runtime_testing() -> RuntimeReport
output_format_validation() -> OutputReport
Testing capabilities encompass:
# Multi-dimensional scoring
score_documentation() -> float # 25% weight
score_code_quality() -> float # 25% weight
score_completeness() -> float # 25% weight
score_usability() -> float # 25% weight
calculate_overall_grade() -> str # A-F grade
Scoring dimensions include:
# Pre-commit hook validation
skill_validator.py path/to/skill --tier POWERFUL --json
# Comprehensive skill testing
script_tester.py path/to/skill --timeout 30 --sample-data
# Quality assessment and scoring
quality_scorer.py path/to/skill --detailed --recommendations
# GitHub Actions workflow example
- name: "validate-skill-quality"
run: |
python skill_validator.py engineering/${{ matrix.skill }} --json | tee validation.json
python script_tester.py engineering/${{ matrix.skill }} | tee testing.json
python quality_scorer.py engineering/${{ matrix.skill }} --json | tee scoring.json
# Validate all skills in repository
find engineering/ -type d -maxdepth 1 | xargs -I {} skill_validator.py {}
# Generate repository quality report
quality_scorer.py engineering/ --batch --output-format json > repo_quality.json
All tools provide both human-readable and machine-parseable output:
=== SKILL VALIDATION REPORT ===
Skill: engineering/example-skill
Tier: STANDARD
Overall Score: 85/100 (B)
Structure Validation: ✓ PASS
├─ SKILL.md: ✓ EXISTS (247 lines)
├─ README.md: ✓ EXISTS
├─ scripts/: ✓ EXISTS (2 files)
└─ references/: ⚠ MISSING (recommended)
Documentation Quality: 22/25 (88%)
Code Quality: 20/25 (80%)
Completeness: 18/25 (72%)
Usability: 21/25 (84%)
Recommendations:
• Add references/ directory with documentation
• Improve error handling in main.py
• Include more comprehensive examples
{
"skill_path": "engineering/example-skill",
"timestamp": "2026-02-16T16:41:00Z",
"validation_results": {
"structure_compliance": {
"score": 0.95,
"checks": {
"skill_md_exists": true,
"readme_exists": true,
"scripts_directory": true,
"references_directory": false
}
},
"overall_score": 85,
"letter_grade": "B",
"tier_recommendation": "STANDARD",
"improvement_suggestions": [
"Add references/ directory",
"Improve error handling",
"Include comprehensive examples"
]
}
}
#!/bin/bash
# .git/hooks/pre-commit
echo "Running skill validation..."
python engineering/skill-tester/scripts/skill_validator.py engineering/new-skill --tier STANDARD
if [ $? -ne 0 ]; then
echo "Skill validation failed. Commit blocked."
exit 1
fi
echo "Validation passed. Proceeding with commit."
name: "skill-quality-gate"
on:
pull_request:
paths: ['engineering/**']
jobs:
validate-skills:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: "setup-python"
uses: actions/setup-python@v4
with:
python-version: '3.11'
- name: "validate-changed-skills"
run: |
changed_skills=$(git diff --name-only ${{ github.event.before }} | grep -E '^engineering/[^/]+/' | cut -d'/' -f1-2 | sort -u)
for skill in $changed_skills; do
echo "Validating $skill..."
python engineering/skill-tester/scripts/skill_validator.py $skill --json
python engineering/skill-tester/scripts/script_tester.py $skill
python engineering/skill-tester/scripts/quality_scorer.py $skill --minimum-score 75
done
#!/bin/bash
# Daily quality report generation
echo "Generating daily skill quality report..."
timestamp=$(date +"%Y-%m-%d")
python engineering/skill-tester/scripts/quality_scorer.py engineering/ \
--batch --json > "reports/quality_report_${timestamp}.json"
echo "Quality trends analysis..."
python engineering/skill-tester/scripts/trend_analyzer.py reports/ \
--days 30 > "reports/quality_trends_${timestamp}.md"
The Skill Tester represents a critical infrastructure component for maintaining the high-quality standards of the claude-skills ecosystem. By providing comprehensive validation, testing, and scoring capabilities, it ensures that all skills meet or exceed the rigorous requirements for their respective tiers.
This meta-skill not only serves as a quality gate but also as a development tool that guides skill authors toward best practices and helps maintain consistency across the entire repository. Through its integration capabilities and comprehensive reporting, it enables both manual and automated quality assurance workflows that scale with the growing claude-skills ecosystem.
The combination of structural validation, runtime testing, and multi-dimensional quality scoring provides unparalleled visibility into skill quality while maintaining the flexibility needed for diverse skill types and complexity levels. As the claude-skills repository continues to grow, the Skill Tester will remain the cornerstone of quality assurance and ecosystem integrity.