skill-tester

Automating AI skill validation involves integrating this tool into CI/CD pipelines and pre-commit hooks to automatically block pull requests that introduce skills failing minimum quality gates. It executes script tests and checks documentation adherence.

Quality scoring for AI skills evaluates documentation, code, completeness, and usability dimensions. It enforces tier-specific requirements across BASIC, STANDARD, and POWERFUL levels to provide detailed feedback for structural improvements.

Validating Python scripts for syntax and runtime errors requires executing the scripts to check for import issues and execution failures. This script testing ensures reliability before skills are approved for deployment.

Pre-commit hooks can enforce documentation standards and directory layout validation. Structure verification checks file presence and adherence to tier-specific requirements before code is committed.

Verifying directory layout for code quality assurance is done through automated structure validation. This process checks file presence, directory structure, and documentation standards to ensure consistency across the skill library.

CI/CD validation blocks pull requests introducing new skills to maintain the integrity of the entire skill library. It enforces minimum quality gates by scoring structure, script execution, and documentation adherence before merging.