Skill UI

An autonomous system designed to continuously improve code quality by scanning codebase metrics. It functions as an agent, proposing mechanical code changes, executing them, and committing only if defined metrics improve. It supports multiple modes (Scan, Scaffold, Run) for systematic, data-driven codebase refactoring.

A powerful post-deployment monitoring tool designed for continuous quality assurance in CI/CD pipelines. It captures a performance baseline before deployment and then continuously monitors live applications after deployment. It alerts on critical failures (e.g., 5xx errors), performance regressions, and unexpected changes, ensuring that new releases maintain production stability.

This tool provides a unified interface to run test suites for various programming stacks (Python, Node.js, Rust) within a CI/CD pipeline. It automatically detects the project's environment (by checking lock files or config) and executes the appropriate testing framework (e.g., pytest, vitest, cargo test), ensuring consistent code quality checks regardless of the underlying language. Ideal for pre-commit or CI/CD integration.

This skill facilitates the computation of Electronic Clinical Quality Measures (eCQMs) by leveraging CQL (Clinical Quality Language) logic against the QDM (Quality Data Model). It is designed to extract crucial facts—such as counseling provided, symptoms, or documented exclusions—from unstructured clinical notes. By lifting these facts, it supplements structured EHR data, ensuring that numerators and valid exclusions are accurately captured for quality measure evaluation. Note that this tool augments data capture and does not replace certified measure engines.

This tool audits all existing skills within the current project. It performs a comprehensive quality check, validating frontmatter completeness, tool scoping, and content quality against established standards. It produces a detailed, scored report, including effort-level recommendations for each skill. Ideal for new project onboarding, pre-release quality assurance, or diagnosing skill triggering issues.

A comprehensive multi-step pipeline tool that executes a fully validated development plan. It handles worktree isolation, TDD scaffolding, parallel task execution across architectural layers, quality gating (linting, testing, smoke tests), documentation reconciliation, PR creation, and final merge, ensuring the feature is fully documented and integrated.

This utility analyzes code changes within a branch compared to the base branch. It calculates a complexity score, detects scope coherence issues (warning if changes are too large or unrelated), and generates a fully structured Pull Request (PR) draft. It automates the creation of titles, comprehensive descriptions (including checklists and technical details), and even suggests splitting the PR into smaller, manageable units, significantly improving code review quality and efficiency.

This skill provides a comprehensive, 4-phase workflow for managing and reviewing a pull request backlog. It automatically audits open PRs, classifying them by size, staleness, overlap, and CI status. Users can then initiate deep code reviews, generate comment drafts, and set up local worktrees for systematic maintenance and quality assurance.

A comprehensive, multi-step framework for conducting systematic QA testing on web applications. This guide covers scope detection (diff-aware), tiered testing depth (Quick, Standard, Exhaustive), detailed issue taxonomy (Functional, Visual, UX, etc.), structured bug documentation, and the critical fix-and-verify loop, ensuring high code quality throughout the development cycle.

A comprehensive assistant designed to analyze existing codebases for adherence to SOLID principles, common code smells, and complexity metrics. It identifies anti-patterns like tight coupling, excessive responsibility, and duplication, providing prioritized, actionable refactoring suggestions to improve code maintainability and quality.

This skill guides a thorough, adversarial review of development plans across four key dimensions: Architecture, Code Quality, Testing, and Performance. It forces explicit consideration of trade-offs, risks, and user approval before any code is written, ensuring robust and well-engineered solutions for complex systems.

A sophisticated, multi-agent system designed to perform deep, comprehensive code reviews on pull requests. It covers core elements like code quality, functionality, security vulnerabilities, and testing coverage. Advanced features include detecting scope drift, enforcing SOLID principles, identifying DRY violations, and providing anti-hallucination safeguards for maintain high software integrity.