{
  "schemaVersion": "1.0",
  "item": {
    "slug": "skill-tester",
    "name": "skill-tester",
    "source": "tencent",
    "type": "skill",
    "category": "开发工具",
    "sourceUrl": "https://clawhub.ai/alirezarezvani/skill-tester",
    "canonicalUrl": "https://clawhub.ai/alirezarezvani/skill-tester",
    "targetPlatform": "OpenClaw"
  },
  "install": {
    "downloadMode": "redirect",
    "downloadUrl": "/downloads/skill-tester",
    "sourceDownloadUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=skill-tester",
    "sourcePlatform": "tencent",
    "targetPlatform": "OpenClaw",
    "installMethod": "Manual import",
    "extraction": "Extract archive",
    "prerequisites": [
      "OpenClaw"
    ],
    "packageFormat": "ZIP package",
    "includedAssets": [
      "README.md",
      "SKILL.md",
      "assets/sample-skill/README.md",
      "assets/sample-skill/SKILL.md",
      "assets/sample-skill/assets/sample_text.txt",
      "assets/sample-skill/assets/test_data.csv"
    ],
    "primaryDoc": "SKILL.md",
    "quickSetup": [
      "Download the package from Yavira.",
      "Extract the archive and review SKILL.md first.",
      "Import or place the package into your OpenClaw setup."
    ],
    "agentAssist": {
      "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
      "steps": [
        "Download the package from Yavira.",
        "Extract it into a folder your agent can access.",
        "Paste one of the prompts below and point your agent at the extracted folder."
      ],
      "prompts": [
        {
          "label": "New install",
          "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete."
        },
        {
          "label": "Upgrade existing",
          "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run."
        }
      ]
    },
    "sourceHealth": {
      "source": "tencent",
      "status": "healthy",
      "reason": "direct_download_ok",
      "recommendedAction": "download",
      "checkedAt": "2026-05-07T17:22:31.273Z",
      "expiresAt": "2026-05-14T17:22:31.273Z",
      "httpStatus": 200,
      "finalUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report",
      "contentType": "application/zip",
      "probeMethod": "head",
      "details": {
        "probeUrl": "https://wry-manatee-359.convex.site/api/v1/download?slug=afrexai-annual-report",
        "contentDisposition": "attachment; filename=\"afrexai-annual-report-1.0.0.zip\"",
        "redirectLocation": null,
        "bodySnippet": null
      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/skill-tester"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    },
    "downloadPageUrl": "https://openagent3.xyz/downloads/skill-tester",
    "agentPageUrl": "https://openagent3.xyz/skills/skill-tester/agent",
    "manifestUrl": "https://openagent3.xyz/skills/skill-tester/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/skill-tester/agent.md"
  },
  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
        "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run."
      }
    ]
  },
  "documentation": {
    "source": "clawhub",
    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Skill Tester",
        "body": "Name: skill-tester\nTier: POWERFUL\nCategory: Engineering Quality Assurance\nDependencies: None (Python Standard Library Only)\nAuthor: Claude Skills Engineering Team\nVersion: 1.0.0\nLast Updated: 2026-02-16"
      },
      {
        "title": "Description",
        "body": "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.\n\nAs the gatekeeping system for skill quality, this meta-skill provides three core capabilities:\n\nStructure Validation - Ensures skills conform to required directory structures, file formats, and documentation standards\nScript Testing - Validates Python scripts for syntax, imports, functionality, and output format compliance\nQuality Scoring - Provides comprehensive quality assessment across multiple dimensions with letter grades and improvement recommendations\n\nThis 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."
      },
      {
        "title": "Comprehensive Skill Validation",
        "body": "Structure Compliance: Validates directory structure, required files (SKILL.md, README.md, scripts/, references/, assets/, expected_outputs/)\nDocumentation Standards: Checks SKILL.md frontmatter, section completeness, minimum line counts per tier\nFile Format Validation: Ensures proper Markdown formatting, YAML frontmatter syntax, and file naming conventions"
      },
      {
        "title": "Advanced Script Testing",
        "body": "Syntax Validation: Compiles Python scripts to detect syntax errors before execution\nImport Analysis: Enforces standard library only policy, identifies external dependencies\nRuntime Testing: Executes scripts with sample data, validates argparse implementation, tests --help functionality\nOutput Format Compliance: Verifies dual output support (JSON + human-readable), proper error handling"
      },
      {
        "title": "Multi-Dimensional Quality Scoring",
        "body": "Documentation Quality (25%): SKILL.md depth and completeness, README clarity, reference documentation quality\nCode Quality (25%): Script complexity, error handling robustness, output format consistency, maintainability\nCompleteness (25%): Required directory presence, sample data adequacy, expected output verification\nUsability (25%): Example clarity, argparse help text quality, installation simplicity, user experience"
      },
      {
        "title": "Tier Classification System",
        "body": "Automatically classifies skills based on complexity and functionality:\n\nBASIC Tier Requirements\n\nMinimum 100 lines in SKILL.md\nAt least 1 Python script (100-300 LOC)\nBasic argparse implementation\nSimple input/output handling\nEssential documentation coverage\n\nSTANDARD Tier Requirements\n\nMinimum 200 lines in SKILL.md\n1-2 Python scripts (300-500 LOC each)\nAdvanced argparse with subcommands\nJSON + text output formats\nComprehensive examples and references\nError handling and edge case management\n\nPOWERFUL Tier Requirements\n\nMinimum 300 lines in SKILL.md\n2-3 Python scripts (500-800 LOC each)\nComplex argparse with multiple modes\nSophisticated output formatting and validation\nExtensive documentation and reference materials\nAdvanced error handling and recovery mechanisms\nCI/CD integration capabilities"
      },
      {
        "title": "Modular Design Philosophy",
        "body": "The skill-tester follows a modular architecture where each component serves a specific validation purpose:\n\nskill_validator.py: Core structural and documentation validation engine\nscript_tester.py: Runtime testing and execution validation framework\nquality_scorer.py: Multi-dimensional quality assessment and scoring system"
      },
      {
        "title": "Standards Enforcement",
        "body": "All validation is performed against well-defined standards documented in the references/ directory:\n\nSkill Structure Specification: Defines mandatory and optional components\nTier Requirements Matrix: Detailed requirements for each skill tier\nQuality Scoring Rubric: Comprehensive scoring methodology and weightings"
      },
      {
        "title": "Integration Capabilities",
        "body": "Designed for seamless integration into existing development workflows:\n\nPre-commit Hooks: Prevents substandard skills from being committed\nCI/CD Pipelines: Automated quality gates in pull request workflows\nManual Validation: Interactive command-line tools for development-time validation\nBatch Processing: Bulk validation and scoring of existing skill repositories"
      },
      {
        "title": "skill_validator.py Core Functions",
        "body": "# Primary validation workflow\nvalidate_skill_structure() -> ValidationReport\ncheck_skill_md_compliance() -> DocumentationReport  \nvalidate_python_scripts() -> ScriptReport\ngenerate_compliance_score() -> float\n\nKey validation checks include:\n\nSKILL.md frontmatter parsing and validation\nRequired section presence (Description, Features, Usage, etc.)\nMinimum line count enforcement per tier\nPython script argparse implementation verification\nStandard library import enforcement\nDirectory structure compliance\nREADME.md quality assessment"
      },
      {
        "title": "script_tester.py Testing Framework",
        "body": "# Core testing functions\nsyntax_validation() -> SyntaxReport\nimport_validation() -> ImportReport\nruntime_testing() -> RuntimeReport\noutput_format_validation() -> OutputReport\n\nTesting capabilities encompass:\n\nPython AST-based syntax validation\nImport statement analysis and external dependency detection\nControlled script execution with timeout protection\nArgparse --help functionality verification\nSample data processing and output validation\nExpected output comparison and difference reporting"
      },
      {
        "title": "quality_scorer.py Scoring System",
        "body": "# Multi-dimensional scoring\nscore_documentation() -> float  # 25% weight\nscore_code_quality() -> float   # 25% weight\nscore_completeness() -> float   # 25% weight\nscore_usability() -> float      # 25% weight\ncalculate_overall_grade() -> str # A-F grade\n\nScoring dimensions include:\n\nDocumentation: Completeness, clarity, examples, reference quality\nCode Quality: Complexity, maintainability, error handling, output consistency\nCompleteness: Required files, sample data, expected outputs, test coverage\nUsability: Help text quality, example clarity, installation simplicity"
      },
      {
        "title": "Development Workflow Integration",
        "body": "# Pre-commit hook validation\nskill_validator.py path/to/skill --tier POWERFUL --json\n\n# Comprehensive skill testing\nscript_tester.py path/to/skill --timeout 30 --sample-data\n\n# Quality assessment and scoring\nquality_scorer.py path/to/skill --detailed --recommendations"
      },
      {
        "title": "CI/CD Pipeline Integration",
        "body": "# GitHub Actions workflow example\n- name: \"validate-skill-quality\"\n  run: |\n    python skill_validator.py engineering/${{ matrix.skill }} --json | tee validation.json\n    python script_tester.py engineering/${{ matrix.skill }} | tee testing.json\n    python quality_scorer.py engineering/${{ matrix.skill }} --json | tee scoring.json"
      },
      {
        "title": "Batch Repository Analysis",
        "body": "# Validate all skills in repository\nfind engineering/ -type d -maxdepth 1 | xargs -I {} skill_validator.py {}\n\n# Generate repository quality report\nquality_scorer.py engineering/ --batch --output-format json > repo_quality.json"
      },
      {
        "title": "Dual Output Support",
        "body": "All tools provide both human-readable and machine-parseable output:\n\nHuman-Readable Format\n\n=== SKILL VALIDATION REPORT ===\nSkill: engineering/example-skill\nTier: STANDARD\nOverall Score: 85/100 (B)\n\nStructure Validation: ✓ PASS\n├─ SKILL.md: ✓ EXISTS (247 lines)\n├─ README.md: ✓ EXISTS  \n├─ scripts/: ✓ EXISTS (2 files)\n└─ references/: ⚠ MISSING (recommended)\n\nDocumentation Quality: 22/25 (88%)\nCode Quality: 20/25 (80%)\nCompleteness: 18/25 (72%)\nUsability: 21/25 (84%)\n\nRecommendations:\n• Add references/ directory with documentation\n• Improve error handling in main.py\n• Include more comprehensive examples\n\nJSON Format\n\n{\n  \"skill_path\": \"engineering/example-skill\",\n  \"timestamp\": \"2026-02-16T16:41:00Z\",\n  \"validation_results\": {\n    \"structure_compliance\": {\n      \"score\": 0.95,\n      \"checks\": {\n        \"skill_md_exists\": true,\n        \"readme_exists\": true,\n        \"scripts_directory\": true,\n        \"references_directory\": false\n      }\n    },\n    \"overall_score\": 85,\n    \"letter_grade\": \"B\",\n    \"tier_recommendation\": \"STANDARD\",\n    \"improvement_suggestions\": [\n      \"Add references/ directory\",\n      \"Improve error handling\",\n      \"Include comprehensive examples\"\n    ]\n  }\n}"
      },
      {
        "title": "Code Quality Requirements",
        "body": "Standard Library Only: No external dependencies (pip packages)\nError Handling: Comprehensive exception handling with meaningful error messages\nOutput Consistency: Standardized JSON schema and human-readable formatting\nPerformance: Efficient validation algorithms with reasonable execution time\nMaintainability: Clear code structure, comprehensive docstrings, type hints where appropriate"
      },
      {
        "title": "Testing Standards",
        "body": "Self-Testing: The skill-tester validates itself (meta-validation)\nSample Data Coverage: Comprehensive test cases covering edge cases and error conditions\nExpected Output Verification: All sample runs produce verifiable, reproducible outputs\nTimeout Protection: Safe execution of potentially problematic scripts with timeout limits"
      },
      {
        "title": "Documentation Standards",
        "body": "Comprehensive Coverage: All functions, classes, and modules documented\nUsage Examples: Clear, practical examples for all use cases\nIntegration Guides: Step-by-step CI/CD and workflow integration instructions\nReference Materials: Complete specification documents for standards and requirements"
      },
      {
        "title": "Pre-Commit Hook Setup",
        "body": "#!/bin/bash\n# .git/hooks/pre-commit\necho \"Running skill validation...\"\npython engineering/skill-tester/scripts/skill_validator.py engineering/new-skill --tier STANDARD\nif [ $? -ne 0 ]; then\n    echo \"Skill validation failed. Commit blocked.\"\n    exit 1\nfi\necho \"Validation passed. Proceeding with commit.\""
      },
      {
        "title": "GitHub Actions Workflow",
        "body": "name: \"skill-quality-gate\"\non:\n  pull_request:\n    paths: ['engineering/**']\n\njobs:\n  validate-skills:\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions/checkout@v3\n      - name: \"setup-python\"\n        uses: actions/setup-python@v4\n        with:\n          python-version: '3.11'\n      - name: \"validate-changed-skills\"\n        run: |\n          changed_skills=$(git diff --name-only ${{ github.event.before }} | grep -E '^engineering/[^/]+/' | cut -d'/' -f1-2 | sort -u)\n          for skill in $changed_skills; do\n            echo \"Validating $skill...\"\n            python engineering/skill-tester/scripts/skill_validator.py $skill --json\n            python engineering/skill-tester/scripts/script_tester.py $skill\n            python engineering/skill-tester/scripts/quality_scorer.py $skill --minimum-score 75\n          done"
      },
      {
        "title": "Continuous Quality Monitoring",
        "body": "#!/bin/bash\n# Daily quality report generation\necho \"Generating daily skill quality report...\"\ntimestamp=$(date +\"%Y-%m-%d\")\npython engineering/skill-tester/scripts/quality_scorer.py engineering/ \\\n  --batch --json > \"reports/quality_report_${timestamp}.json\"\n\necho \"Quality trends analysis...\"\npython engineering/skill-tester/scripts/trend_analyzer.py reports/ \\\n  --days 30 > \"reports/quality_trends_${timestamp}.md\""
      },
      {
        "title": "Execution Performance",
        "body": "Fast Validation: Structure validation completes in <1 second per skill\nEfficient Testing: Script testing with timeout protection (configurable, default 30s)\nBatch Processing: Optimized for repository-wide analysis with parallel processing support\nMemory Efficiency: Minimal memory footprint for large-scale repository analysis"
      },
      {
        "title": "Scalability Considerations",
        "body": "Repository Size: Designed to handle repositories with 100+ skills\nConcurrent Execution: Thread-safe implementation supports parallel validation\nResource Management: Automatic cleanup of temporary files and subprocess resources\nConfiguration Flexibility: Configurable timeouts, memory limits, and validation strictness"
      },
      {
        "title": "Safe Execution Environment",
        "body": "Sandboxed Testing: Scripts execute in controlled environment with timeout protection\nResource Limits: Memory and CPU usage monitoring to prevent resource exhaustion\nInput Validation: All inputs sanitized and validated before processing\nNo Network Access: Offline operation ensures no external dependencies or network calls"
      },
      {
        "title": "Security Best Practices",
        "body": "No Code Injection: Static analysis only, no dynamic code generation\nPath Traversal Protection: Secure file system access with path validation\nMinimal Privileges: Operates with minimal required file system permissions\nAudit Logging: Comprehensive logging for security monitoring and troubleshooting"
      },
      {
        "title": "Common Issues & Solutions",
        "body": "Validation Failures\n\nMissing Files: Check directory structure against tier requirements\nImport Errors: Ensure only standard library imports are used\nDocumentation Issues: Verify SKILL.md frontmatter and section completeness\n\nScript Testing Problems\n\nTimeout Errors: Increase timeout limit or optimize script performance\nExecution Failures: Check script syntax and import statement validity\nOutput Format Issues: Ensure proper JSON formatting and dual output support\n\nQuality Scoring Discrepancies\n\nLow Scores: Review scoring rubric and improvement recommendations\nTier Misclassification: Verify skill complexity against tier requirements\nInconsistent Results: Check for recent changes in quality standards or scoring weights"
      },
      {
        "title": "Debugging Support",
        "body": "Verbose Mode: Detailed logging and execution tracing available\nDry Run Mode: Validation without execution for debugging purposes\nDebug Output: Comprehensive error reporting with file locations and suggestions"
      },
      {
        "title": "Planned Features",
        "body": "Machine Learning Quality Prediction: AI-powered quality assessment using historical data\nPerformance Benchmarking: Execution time and resource usage tracking across skills\nDependency Analysis: Automated detection and validation of skill interdependencies\nQuality Trend Analysis: Historical quality tracking and regression detection"
      },
      {
        "title": "Integration Roadmap",
        "body": "IDE Plugins: Real-time validation in popular development environments\nWeb Dashboard: Centralized quality monitoring and reporting interface\nAPI Endpoints: RESTful API for external integration and automation\nNotification Systems: Automated alerts for quality degradation or validation failures"
      },
      {
        "title": "Conclusion",
        "body": "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.\n\nThis 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.\n\nThe 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."
      }
    ],
    "body": "Skill Tester\n\nName: 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\n\nDescription\n\nThe 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.\n\nAs the gatekeeping system for skill quality, this meta-skill provides three core capabilities:\n\nStructure Validation - Ensures skills conform to required directory structures, file formats, and documentation standards\nScript Testing - Validates Python scripts for syntax, imports, functionality, and output format compliance\nQuality Scoring - Provides comprehensive quality assessment across multiple dimensions with letter grades and improvement recommendations\n\nThis 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.\n\nCore Features\nComprehensive Skill Validation\nStructure Compliance: Validates directory structure, required files (SKILL.md, README.md, scripts/, references/, assets/, expected_outputs/)\nDocumentation Standards: Checks SKILL.md frontmatter, section completeness, minimum line counts per tier\nFile Format Validation: Ensures proper Markdown formatting, YAML frontmatter syntax, and file naming conventions\nAdvanced Script Testing\nSyntax Validation: Compiles Python scripts to detect syntax errors before execution\nImport Analysis: Enforces standard library only policy, identifies external dependencies\nRuntime Testing: Executes scripts with sample data, validates argparse implementation, tests --help functionality\nOutput Format Compliance: Verifies dual output support (JSON + human-readable), proper error handling\nMulti-Dimensional Quality Scoring\nDocumentation Quality (25%): SKILL.md depth and completeness, README clarity, reference documentation quality\nCode Quality (25%): Script complexity, error handling robustness, output format consistency, maintainability\nCompleteness (25%): Required directory presence, sample data adequacy, expected output verification\nUsability (25%): Example clarity, argparse help text quality, installation simplicity, user experience\nTier Classification System\n\nAutomatically classifies skills based on complexity and functionality:\n\nBASIC Tier Requirements\nMinimum 100 lines in SKILL.md\nAt least 1 Python script (100-300 LOC)\nBasic argparse implementation\nSimple input/output handling\nEssential documentation coverage\nSTANDARD Tier Requirements\nMinimum 200 lines in SKILL.md\n1-2 Python scripts (300-500 LOC each)\nAdvanced argparse with subcommands\nJSON + text output formats\nComprehensive examples and references\nError handling and edge case management\nPOWERFUL Tier Requirements\nMinimum 300 lines in SKILL.md\n2-3 Python scripts (500-800 LOC each)\nComplex argparse with multiple modes\nSophisticated output formatting and validation\nExtensive documentation and reference materials\nAdvanced error handling and recovery mechanisms\nCI/CD integration capabilities\nArchitecture & Design\nModular Design Philosophy\n\nThe skill-tester follows a modular architecture where each component serves a specific validation purpose:\n\nskill_validator.py: Core structural and documentation validation engine\nscript_tester.py: Runtime testing and execution validation framework\nquality_scorer.py: Multi-dimensional quality assessment and scoring system\nStandards Enforcement\n\nAll validation is performed against well-defined standards documented in the references/ directory:\n\nSkill Structure Specification: Defines mandatory and optional components\nTier Requirements Matrix: Detailed requirements for each skill tier\nQuality Scoring Rubric: Comprehensive scoring methodology and weightings\nIntegration Capabilities\n\nDesigned for seamless integration into existing development workflows:\n\nPre-commit Hooks: Prevents substandard skills from being committed\nCI/CD Pipelines: Automated quality gates in pull request workflows\nManual Validation: Interactive command-line tools for development-time validation\nBatch Processing: Bulk validation and scoring of existing skill repositories\nImplementation Details\nskill_validator.py Core Functions\n# Primary validation workflow\nvalidate_skill_structure() -> ValidationReport\ncheck_skill_md_compliance() -> DocumentationReport  \nvalidate_python_scripts() -> ScriptReport\ngenerate_compliance_score() -> float\n\n\nKey validation checks include:\n\nSKILL.md frontmatter parsing and validation\nRequired section presence (Description, Features, Usage, etc.)\nMinimum line count enforcement per tier\nPython script argparse implementation verification\nStandard library import enforcement\nDirectory structure compliance\nREADME.md quality assessment\nscript_tester.py Testing Framework\n# Core testing functions\nsyntax_validation() -> SyntaxReport\nimport_validation() -> ImportReport\nruntime_testing() -> RuntimeReport\noutput_format_validation() -> OutputReport\n\n\nTesting capabilities encompass:\n\nPython AST-based syntax validation\nImport statement analysis and external dependency detection\nControlled script execution with timeout protection\nArgparse --help functionality verification\nSample data processing and output validation\nExpected output comparison and difference reporting\nquality_scorer.py Scoring System\n# Multi-dimensional scoring\nscore_documentation() -> float  # 25% weight\nscore_code_quality() -> float   # 25% weight\nscore_completeness() -> float   # 25% weight\nscore_usability() -> float      # 25% weight\ncalculate_overall_grade() -> str # A-F grade\n\n\nScoring dimensions include:\n\nDocumentation: Completeness, clarity, examples, reference quality\nCode Quality: Complexity, maintainability, error handling, output consistency\nCompleteness: Required files, sample data, expected outputs, test coverage\nUsability: Help text quality, example clarity, installation simplicity\nUsage Scenarios\nDevelopment Workflow Integration\n# Pre-commit hook validation\nskill_validator.py path/to/skill --tier POWERFUL --json\n\n# Comprehensive skill testing\nscript_tester.py path/to/skill --timeout 30 --sample-data\n\n# Quality assessment and scoring\nquality_scorer.py path/to/skill --detailed --recommendations\n\nCI/CD Pipeline Integration\n# GitHub Actions workflow example\n- name: \"validate-skill-quality\"\n  run: |\n    python skill_validator.py engineering/${{ matrix.skill }} --json | tee validation.json\n    python script_tester.py engineering/${{ matrix.skill }} | tee testing.json\n    python quality_scorer.py engineering/${{ matrix.skill }} --json | tee scoring.json\n\nBatch Repository Analysis\n# Validate all skills in repository\nfind engineering/ -type d -maxdepth 1 | xargs -I {} skill_validator.py {}\n\n# Generate repository quality report\nquality_scorer.py engineering/ --batch --output-format json > repo_quality.json\n\nOutput Formats & Reporting\nDual Output Support\n\nAll tools provide both human-readable and machine-parseable output:\n\nHuman-Readable Format\n=== SKILL VALIDATION REPORT ===\nSkill: engineering/example-skill\nTier: STANDARD\nOverall Score: 85/100 (B)\n\nStructure Validation: ✓ PASS\n├─ SKILL.md: ✓ EXISTS (247 lines)\n├─ README.md: ✓ EXISTS  \n├─ scripts/: ✓ EXISTS (2 files)\n└─ references/: ⚠ MISSING (recommended)\n\nDocumentation Quality: 22/25 (88%)\nCode Quality: 20/25 (80%)\nCompleteness: 18/25 (72%)\nUsability: 21/25 (84%)\n\nRecommendations:\n• Add references/ directory with documentation\n• Improve error handling in main.py\n• Include more comprehensive examples\n\nJSON Format\n{\n  \"skill_path\": \"engineering/example-skill\",\n  \"timestamp\": \"2026-02-16T16:41:00Z\",\n  \"validation_results\": {\n    \"structure_compliance\": {\n      \"score\": 0.95,\n      \"checks\": {\n        \"skill_md_exists\": true,\n        \"readme_exists\": true,\n        \"scripts_directory\": true,\n        \"references_directory\": false\n      }\n    },\n    \"overall_score\": 85,\n    \"letter_grade\": \"B\",\n    \"tier_recommendation\": \"STANDARD\",\n    \"improvement_suggestions\": [\n      \"Add references/ directory\",\n      \"Improve error handling\",\n      \"Include comprehensive examples\"\n    ]\n  }\n}\n\nQuality Assurance Standards\nCode Quality Requirements\nStandard Library Only: No external dependencies (pip packages)\nError Handling: Comprehensive exception handling with meaningful error messages\nOutput Consistency: Standardized JSON schema and human-readable formatting\nPerformance: Efficient validation algorithms with reasonable execution time\nMaintainability: Clear code structure, comprehensive docstrings, type hints where appropriate\nTesting Standards\nSelf-Testing: The skill-tester validates itself (meta-validation)\nSample Data Coverage: Comprehensive test cases covering edge cases and error conditions\nExpected Output Verification: All sample runs produce verifiable, reproducible outputs\nTimeout Protection: Safe execution of potentially problematic scripts with timeout limits\nDocumentation Standards\nComprehensive Coverage: All functions, classes, and modules documented\nUsage Examples: Clear, practical examples for all use cases\nIntegration Guides: Step-by-step CI/CD and workflow integration instructions\nReference Materials: Complete specification documents for standards and requirements\nIntegration Examples\nPre-Commit Hook Setup\n#!/bin/bash\n# .git/hooks/pre-commit\necho \"Running skill validation...\"\npython engineering/skill-tester/scripts/skill_validator.py engineering/new-skill --tier STANDARD\nif [ $? -ne 0 ]; then\n    echo \"Skill validation failed. Commit blocked.\"\n    exit 1\nfi\necho \"Validation passed. Proceeding with commit.\"\n\nGitHub Actions Workflow\nname: \"skill-quality-gate\"\non:\n  pull_request:\n    paths: ['engineering/**']\n\njobs:\n  validate-skills:\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions/checkout@v3\n      - name: \"setup-python\"\n        uses: actions/setup-python@v4\n        with:\n          python-version: '3.11'\n      - name: \"validate-changed-skills\"\n        run: |\n          changed_skills=$(git diff --name-only ${{ github.event.before }} | grep -E '^engineering/[^/]+/' | cut -d'/' -f1-2 | sort -u)\n          for skill in $changed_skills; do\n            echo \"Validating $skill...\"\n            python engineering/skill-tester/scripts/skill_validator.py $skill --json\n            python engineering/skill-tester/scripts/script_tester.py $skill\n            python engineering/skill-tester/scripts/quality_scorer.py $skill --minimum-score 75\n          done\n\nContinuous Quality Monitoring\n#!/bin/bash\n# Daily quality report generation\necho \"Generating daily skill quality report...\"\ntimestamp=$(date +\"%Y-%m-%d\")\npython engineering/skill-tester/scripts/quality_scorer.py engineering/ \\\n  --batch --json > \"reports/quality_report_${timestamp}.json\"\n\necho \"Quality trends analysis...\"\npython engineering/skill-tester/scripts/trend_analyzer.py reports/ \\\n  --days 30 > \"reports/quality_trends_${timestamp}.md\"\n\nPerformance & Scalability\nExecution Performance\nFast Validation: Structure validation completes in <1 second per skill\nEfficient Testing: Script testing with timeout protection (configurable, default 30s)\nBatch Processing: Optimized for repository-wide analysis with parallel processing support\nMemory Efficiency: Minimal memory footprint for large-scale repository analysis\nScalability Considerations\nRepository Size: Designed to handle repositories with 100+ skills\nConcurrent Execution: Thread-safe implementation supports parallel validation\nResource Management: Automatic cleanup of temporary files and subprocess resources\nConfiguration Flexibility: Configurable timeouts, memory limits, and validation strictness\nSecurity & Safety\nSafe Execution Environment\nSandboxed Testing: Scripts execute in controlled environment with timeout protection\nResource Limits: Memory and CPU usage monitoring to prevent resource exhaustion\nInput Validation: All inputs sanitized and validated before processing\nNo Network Access: Offline operation ensures no external dependencies or network calls\nSecurity Best Practices\nNo Code Injection: Static analysis only, no dynamic code generation\nPath Traversal Protection: Secure file system access with path validation\nMinimal Privileges: Operates with minimal required file system permissions\nAudit Logging: Comprehensive logging for security monitoring and troubleshooting\nTroubleshooting & Support\nCommon Issues & Solutions\nValidation Failures\nMissing Files: Check directory structure against tier requirements\nImport Errors: Ensure only standard library imports are used\nDocumentation Issues: Verify SKILL.md frontmatter and section completeness\nScript Testing Problems\nTimeout Errors: Increase timeout limit or optimize script performance\nExecution Failures: Check script syntax and import statement validity\nOutput Format Issues: Ensure proper JSON formatting and dual output support\nQuality Scoring Discrepancies\nLow Scores: Review scoring rubric and improvement recommendations\nTier Misclassification: Verify skill complexity against tier requirements\nInconsistent Results: Check for recent changes in quality standards or scoring weights\nDebugging Support\nVerbose Mode: Detailed logging and execution tracing available\nDry Run Mode: Validation without execution for debugging purposes\nDebug Output: Comprehensive error reporting with file locations and suggestions\nFuture Enhancements\nPlanned Features\nMachine Learning Quality Prediction: AI-powered quality assessment using historical data\nPerformance Benchmarking: Execution time and resource usage tracking across skills\nDependency Analysis: Automated detection and validation of skill interdependencies\nQuality Trend Analysis: Historical quality tracking and regression detection\nIntegration Roadmap\nIDE Plugins: Real-time validation in popular development environments\nWeb Dashboard: Centralized quality monitoring and reporting interface\nAPI Endpoints: RESTful API for external integration and automation\nNotification Systems: Automated alerts for quality degradation or validation failures\nConclusion\n\nThe 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.\n\nThis 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.\n\nThe 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."
  },
  "trust": {
    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/alirezarezvani/skill-tester",
    "publisherUrl": "https://clawhub.ai/alirezarezvani/skill-tester",
    "owner": "alirezarezvani",
    "version": "2.1.1",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/skill-tester",
    "downloadUrl": "https://openagent3.xyz/downloads/skill-tester",
    "agentUrl": "https://openagent3.xyz/skills/skill-tester/agent",
    "manifestUrl": "https://openagent3.xyz/skills/skill-tester/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/skill-tester/agent.md"
  }
}