Skill UI

This tool automates complex Git workflows to save time and reduce manual overhead. It intelligently detects and groups related code changes, automatically generates descriptive branch names (e.g., fix/auth-bug), and streamlines the entire cycle: staging, committing, pushing, and creating Pull Requests (PRs). Use it to maintain clean, consistent commit histories efficiently.

This expert skill specializes in guiding comprehensive GitHub issue resolution. It covers systematic bug investigation, feature implementation, and collaborative development workflows. Expertise includes issue triage, root cause analysis, test-driven development, and professional pull request management, ensuring vague reports are transformed into production-ready code following modern CI/CD practices.

Build end-to-end tests with TestDriver.ai, an AI code reviewer that runs every pull request in a real desktop sandbox, finds bugs, and creates regression tests for web, Electron, and native apps.

This skill guides the systematic triage of project issues and external pull requests (PRs). It moves development items through a defined state machine using roles (e.g., bug, enhancement) and states (e.g., ready-for-agent, wontfix). The process involves context gathering, cross-codebase verification, deep questioning (grilling), and generating comprehensive, agent-ready briefs or marking them for human implementation.

Scan Apple's SwiftUI documentation via the Sosumi MCP to find deprecated APIs and their modern replacements, then update the SwiftUI Expert Skill reference file and open a pull request.

This skill enables developers to deploy frontend and full-stack applications on Vercel efficiently. It covers setting up preview environments for pull requests, configuring edge functions, managing environment variables across different stages, and implementing production guardrails. Includes integration with GitHub Actions for automated CI/CD pipelines and security best practices like custom domains and headers.

Vibers provides a human-in-the-loop code review workflow for AI-generated GitHub projects. Users push code, and the service reviews it against a specification, checks for security issues, and submits a pull request with suggested fixes. It supports various languages and requires adding a collaborator and configuring a GitHub Action to trigger the review process.

A five-axis code review checklist covering correctness, security, readability, performance, and test coverage to produce actionable comments instead of style nitpicks. Use for reviewing code, diffs, or pull requests.