Skill UI

This skill automatically generates comprehensive and highly structured Pull Request (PR) descriptions directly from Git diffs. It goes beyond simple summaries by automatically adding detailed change categories (e.g., source, test, config), security risk assessments, and required review checklists based on the modified file types. Use this when submitting complex code changes or when the diff is large and requires explicit structure for thorough review.

This skill automates the creation of comprehensive documentation for relational databases (PostgreSQL/MySQL). By introspecting live schemas, it extracts detailed metadata including table structures, column descriptions, primary/foreign key relationships, indexes, views, and stored procedures. The output provides a data dictionary, entity-relationship map, and schema statistics, crucial for knowledge transfer and database lifecycle management.

This skill acts as a PR optimization expert, designed to help users create high-quality pull requests. It generates comprehensive descriptions, automates review processes, and ensures adherence to best practices regarding clarity, documentation, and test coverage. It streamlines the entire code review lifecycle.

This skill is intended to analyze and improve the quality of codebases, ensuring adherence to best practices and coding standards. Note: This specific file is a translation placeholder, and the core functionality description should refer to the original skill file for details.

This skill helps users create a new Git branch that strictly adheres to established project naming conventions (e.g., Sentry standards). It automatically determines the appropriate branch type (such as `feat`, `fix`, `chore`, etc.) and generates a descriptive, standardized branch name by analyzing the provided work description or local code differences (diff). It guides the entire branching workflow, ensuring proper lineage and classification for code submissions.

This skill guides the process of auditing and correcting critical HTML metadata elements, ensuring optimal performance for search engines and social media sharing. It covers page titles, meta descriptions, canonical URLs, Open Graph tags, Twitter cards, favicons, JSON-LD structured data, and robots directives. Use this when implementing or refining SEO metadata, fixing broken social previews, or setting up structured data.

This tool automates the creation of production-ready End-to-End (E2E) tests using the Playwright framework. Users can provide natural language descriptions (like user stories, feature names, or component paths) to automatically generate comprehensive test suites. It follows best practices, including utilizing role-based locators, structuring tests with setup/act/assert phases, and generating supporting files like page objects and fixtures for maintainability.

This guide outlines best practices for creating high-quality, structured pull requests (PRs) using Git and GitHub CLI. It ensures that every PR description captures not only what changed, but also the 'why' and necessary context for reviewers, significantly improving the code review process and adherence to engineering standards.

A comprehensive, canonical workflow for defining, writing, and improving complex AI skills. It guides users through a structured, multi-step process, including resolving targets, synthesizing information, enforcing depth gates, authoring artifacts, optimizing descriptions, and conducting thorough evaluation and validation.

Creates a comprehensive onboarding guide for new teammates by extracting project context from the knowledge graph, layering architecture descriptions, guiding tours, and highlighting complex files so newcomers can ramp up quickly.

A comprehensive workflow for the full lifecycle management of AI skills. This tool guides users through creating skills from scratch, modifying existing ones, and rigorously benchmarking performance. It supports iterative refinement, quantitative evaluation, and optimizing skill descriptions for maximum triggering accuracy.

This workflow guides developers through generating standardized, descriptive commit messages using the Conventional Commits specification. It provides a structured XML template (including type, scope, description, body, and footer) and integrates directly into the Git workflow, ensuring project history is clean, traceable, and machine-readable. Ideal for maintaining large, collaborative codebases.