Skill UI

This skill outlines a systematic workflow for addressing review comments and issue feedback on GitHub Pull Requests (PRs) using the GitHub CLI (`gh`). It guides users through inspecting comments, planning fixes, applying code changes, and finally responding to the threads as resolved, ensuring a streamlined code review process.

This skill is designed to execute complex, structured workflows and actions based on a defined overview. It facilitates systematic process completion and adherence to scope. Users must ensure the task precisely matches the skill's scope, and remember that the output is not a substitute for manual validation or expert review.

This skill defines structured behavioral frameworks (e.g., Brainstorm, Implement, Debug, Review, Teach, Ship) to guide the AI's output and approach based on the specific task phase. It optimizes performance by ensuring the AI adopts the correct mindset and output format—from initial ideation to final deployment—guaranteeing systematic, high-quality results across the entire development lifecycle.

This skill provides comprehensive guidance and established patterns for conducting rigorous scientific research and complex data analysis. It helps users structure their thinking process, ensuring a systematic and methodical approach to scholarly inquiry. Ideal for academic projects, literature reviews, and complex data interpretation.

This comprehensive checklist provides a systematic framework for conducting thorough code reviews. It covers critical areas including functionality, security vulnerabilities, performance optimization, and overall code maintainability. Use it when reviewing pull requests, conducting code audits, or standardizing team development practices.

This skill helps transform code reviews from simple gatekeeping into a powerful knowledge-sharing mechanism. It guides users through systematic analysis of code changes, focusing on correctness, security, performance, and maintainability. Use it when reviewing pull requests, establishing best practices, or mentoring developers to ensure high-quality, robust codebase development.

A powerful AI tool designed for complex, coordinated programming tasks. It enables developers to generate, refactor, and scaffold entire features across multiple files (e.g., schema, API routes, service layers, tests) while maintaining consistency with existing codebase patterns. It supports agent mode for autonomous, multi-step changes and allows systematic migration and review of all affected files via detailed diffs.

This tool integrates advanced AI capabilities into the entire Git lifecycle within the IDE. It assists developers by automatically generating standardized commit messages from staged diffs, providing deep code review using the @Git context (checking for vulnerabilities, missing handling), and offering systematic guidance for resolving complex merge conflicts. It streamlines version control and elevates code quality.

This meta-skill coordinates the entire design lifecycle, ensuring that ideas are systematically refined through mandatory stages: brainstorming, risk assessment, and multi-agent review. It acts as a process gatekeeper, enforcing adherence to structured steps and mitigating risks before allowing implementation, thus preventing premature or flawed execution.

Performs a structured, multi-phase audit on local branch changes (diffs) to identify security vulnerabilities, bugs, and code quality issues. It systematically checks for common risks like Injection (SQL, XSS), Broken Authentication, IDOR, CSRF, and business logic flaws, providing detailed severity, evidence, and suggested fixes. Ideal for pre-merge code reviews and security audits.

This skill orchestrates a comprehensive, multi-stage Git development workflow, simulating the process from code commit to PR preparation. It leverages specialized AI agents (code reviewers, test automators, prompt engineers) to systematically enforce best practices. Key functions include code quality assessment, breaking change analysis, running unit/integration/E2E tests, and generating Conventional Commit messages, ensuring code readiness and high quality before merging.

This sophisticated skill automates the entire Pull Request (PR) quality assurance lifecycle. It systematically fetches and categorizes CI check failures and detailed review feedback using the LOGAF priority scale. The workflow guides users through root cause analysis, proposing minimal, targeted fixes, replying to reviewers, and committing updates iteratively until all checks pass, ensuring high code quality before merging.