Skill UI

This skill provides a systematic methodology for iteratively improving complex skill documentation. By utilizing a reviewer agent, it identifies and categorizes issues (Critical, Major, Minor) in a structured loop. The process ensures that the skill meets high standards of quality, reliability, and functional completeness before deployment, moving beyond simple manual edits.

This tool guides the process of reviewing security findings from DAST and SAST scanners. It uses the OWASP risk rating methodology to classify vulnerabilities (true positive, false positive, needs review) and calculate a comprehensive risk score based on likelihood and impact, ensuring development teams prioritize the most critical issues.

This skill provides comprehensive automation for managing GitHub workflows across the entire development lifecycle. It covers detailed issue triage, PR review status checks, CI/CD failure debugging, official release management, and security monitoring using the GitHub CLI. Ideal for maintaining open-source project health and streamlining team development processes.

Guides AI agents through analyzing working code, spotting overly complex structures, then refactoring for clarity while preserving exact behavior and project conventions, especially after reviews or high-pressure edits.

Streamlines the process of providing code review feedback by enforcing a highly structured, ultra-compressed format. It guides users to cut through noise and deliver actionable, single-line comments (Location: Problem. Fix.) while maintaining critical context like severity and technical rationale. Ideal for team settings prioritizing efficiency and high code quality.

This skill demonstrates how to use Playwright MCP to programmatically automate the process of inputting data into a web form. It covers various input types, including text fields, date selection, time setting, and file uploads. The process simulates realistic user interaction for data entry or form submission testing, emphasizing the ability to fill data without final submission for review purposes.

This advanced skill automates the enforcement of Python code quality using Ruff. It implements a controlled, iterative workflow that supports safe and unsafe automatic autofixes, formats the code, and reviews diffs after each pass. The process recursively repeats until all findings are resolved or require explicit user decision, ensuring high code consistency and quality gates.

This tool automates the entire workflow of creating a GitHub Pull Request (PR) directly from a structured specification file. It analyzes the template, extracts requirements, checks for existing PRs, creates the draft, updates the body and title with specification details, and finally transitions the PR state to 'Ready for Review'. This significantly streamlines the feature request submission process, ensuring all necessary documentation and metadata are included.

A comprehensive, five-stage methodology for reviewing code quality. This process systematically checks for requirements compliance, logical correctness, code maintainability, test coverage, and critical security/performance vulnerabilities. It provides actionable feedback categorized by severity (Blocker, Major, Minor, Nit), ensuring high standards for all merged code.

This skill guides the systematic execution of a detailed, written development plan. It mandates a critical review phase to identify dependencies, vague requirements, and environment assumptions before starting. The process involves tackling tasks sequentially, performing rigorous verification (testing/checking), committing changes after each milestone, and systematically handling common exceptions like test failures or dependency gaps. It ensures a disciplined, traceable, and reportable development cycle.

This guide details the best practices for utilizing a code review sub-agent. It should be used after completing tasks, implementing major features, or before merging code into main branches. The process ensures that work results are validated against requirements, allowing reviewers to focus purely on the output rather than the development history, thus catching issues early.

This tool automates the complete specification generation process across four core phases: Initialization, Requirements, Design, and Tasks. It supports both interactive (step-by-step approval) and automatic execution modes. After generating the full spec, it performs a final sanity review to ensure coherence and completeness, providing a ready-to-implement blueprint.