Skill UI

This skill generates commit messages strictly following Sentry's conventions and the Conventional Commit format. It mandates a structured commit message including a type, scope, subject, body (explaining what and why), and a footer for issue references. Use this whenever committing code changes to ensure a highly readable, traceable, and standardized version history.

Automated assistance for crafting commit messages aligned with DevOps basics, including guidance, patterns, and validation to keep version control workflows consistent.

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.

Generates conventional commit messages using AI by analyzing staged Git changes and recommending standardized text for features or fixes, helping teams keep commit history consistent during development.

Automatically generates conventional commit messages by analyzing staged Git changes, identifying types and breaking changes, and formatting the result so you can approve standardized commits quickly after running `/commit-smart` or `/gc`.

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 skill automates the complete software development workflow: staging all changes, creating a structured conventional commit message, and pushing the results to the remote repository. It is ideal for developers who have completed a feature or batch of changes and need to reliably share their work while adhering to standard Git best practices.

This skill integrates advanced AI assistance into Git workflows within the Windsurf IDE. It automates complex source control tasks, including generating descriptive commit messages from staged changes, suggesting optimal branch names, assisting with merge conflict resolution, and streamlining common Git operations. Ideal for developers managing version control or needing robust support for collaborative coding environments.

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.

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.

This tool generates highly standardized and ultra-compressed commit messages following the Conventional Commits specification. It ensures that commits are concise, professional, and adhere to strict formats (e.g., type(scope): summary). It intelligently generates a body only when the context (like breaking changes or complex reasoning) requires it, significantly reducing noise and maintaining a clean, clear project history.