Skill UI

This skill simulates a senior engineer's final, deep audit of an entire codebase before committing or pushing to a remote repository. It systematically checks for junk files, dead code, security vulnerabilities (secrets, injection flaws), and performance bottlenecks (N+1 queries). Use this pre-flight check before any major release or public deployment to ensure maximum code quality and production readiness.

A rigorous, security-focused code review process designed for Pull Requests (PRs), commits, and diffs. It moves beyond general reviews by focusing intensely on high-risk areas such as authentication, cryptography, external calls, and value transfer. The methodology is evidence-based, requiring detailed attack scenario modeling and generating comprehensive, auditable markdown reports to identify overlooked vulnerabilities and regressions.

This skill guides users on setting up automated code quality gates directly at the Git level. It covers best practices using frameworks like Husky, lint-staged, and pre-commit. Users can enforce linting, formatting, type-checking, and standardized commit message conventions (e.g., Conventional Commits) before code is committed or pushed, significantly improving code hygiene and development workflow consistency.

Autoresearch Agent runs an autonomous optimization loop that edits a target file, invokes a provided evaluation command, keeps metric-improving commits, drops failures, and repeats indefinitely so engineers can optimize speed, bundle size, prompts, or other measurable goals overnight.

A comprehensive solution for identifying and preventing the accidental commitment of exposed AWS credentials, API keys, and session tokens within source code, configuration files, and git history. It integrates TruffleHog and git-secrets into CI/CD pipelines and pre-commit hooks, providing critical protection against credential theft and unauthorized cloud access in a DevSecOps environment. Essential for security auditing and compliance.

This skill guides the implementation of Gitleaks for robust secret scanning across development lifecycles. It covers setting up pre-commit hooks to prevent secrets from being committed, integrating scanning into CI/CD pipelines (like GitHub Actions), and writing custom regex rules to detect and remediate hardcoded credentials (API keys, passwords, tokens) in Git history, ensuring adherence to secure SDLC practices.

Vibers provides a human code review workflow for AI-generated GitHub projects; add the collaborator, GitHub Action, and commit guidance to receive spec- and security-focused feedback plus follow-up PR fixes.

This command merges the results of the highest-ranked agent from a specified or latest session into the base branch. It automates the finalization of a development cycle by ensuring a clean and traceable history. Functionality includes merging the winner's branch, archiving all losing agents' commits using Git tags, and cleaning up temporary worktrees.

This skill generates user-friendly, public-facing release notes (changelogs) for App Stores. It analyzes the raw Git history since the last tag, intelligently filtering out internal commits (like refactors, CI updates, or build scripts) and grouping visible changes into concise, benefit-focused bullet points suitable for a "What's New" section.

A comprehensive guide to professional Git version control. It covers best practices for branching strategies (GitHub Flow, GitFlow, Trunk-Based), writing standardized commit messages using Conventional Commits, managing merges vs. rebases, and defining effective Pull Request workflows. Ideal for any development team looking to improve code collaboration and maintain a clean, traceable history.

A comprehensive guide to disciplined version control using Git. It outlines best practices for atomic commits, descriptive message formatting (feat, fix, refactor), adopting trunk-based development, and managing feature branches. Use this whenever making code changes, committing, or coordinating complex work streams to ensure code stability and traceability.

A disciplined workflow that forces you to build in thin vertical slices, test, verify, and commit each working increment before moving on, ideal for multi-file features or large refactors.