quality-standards_skill

Overview

This skill defines practical code quality benchmarks, language-specific standards, and enforceable best practices to keep codebases healthy and maintainable. It gives clear quality score thresholds and a weighted breakdown of components that determine overall quality. The guidance applies across Python, JavaScript/TypeScript, and general project workflows. Use it to set measurable targets and automated checks for teams and CI pipelines.

How this skill works

The skill inspects linting and formatting results, test outcomes, documentation coverage, pattern adherence, and basic code metrics to compute a composite quality score. It maps findings to predefined thresholds (Excellent, Good, Acceptable, Needs Improvement) and highlights which component weights drove the score. Language-specific rules (PEP 8, ESLint/Prettier, TypeScript strict mode) and conventions are enforced where applicable, producing actionable remediation items.

When to use it

• Setting quality gates for CI/CD pipelines
• Defining team or project coding standards
• Evaluating repositories before releases or merges
• Onboarding new contributors with clear benchmarks
• Auditing legacy code to prioritize refactoring

Best practices

• Adopt automated linters and formatters in pre-commit hooks and CI
• Use type hints and strict TypeScript to reduce runtime errors
• Write and maintain docstrings for public APIs using a consistent style
• Treat failing tests as the highest-priority fix (tests = 30% weight)
• Monitor complexity and duplication; refactor to keep code metrics within thresholds

Example use cases

• Block pull requests that drop the quality score below a configured gate
• Run scheduled audits to track quality score trends across services
• Enforce Python PEP 8 and Black line-length standards in CI
• Require TypeScript strict mode and ESLint recommended rules for frontend repos
• Prioritize remediation tasks based on component weights (tests, standards, docs)