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.

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.

Manages branching strategies, conventional commits, PR checklists, and daily Git commands so you stay disciplined when creating features, resolving conflicts, and collaborating through pull requests.

This skill provides comprehensive guidance and tools for end-to-end software release management. It automates critical tasks such as generating changelogs from conventional commits, determining semantic version bumps (Major/Minor/Patch), assessing release readiness, and planning complex deployment workflows (e.g., Git Flow). Ideal for teams adopting robust DevOps practices.

A comprehensive framework guiding feature planning and implementation through adaptive phases: Specify, Design, Tasks, and Execute. It enforces highly structured development practices by using EARS notation, atomic tasks, deterministic Conventional Commits, and mandatory code validation scripts. Features include requirement traceability, automatic verification, and a self-improving lessons layer, ensuring quality and compliance throughout the lifecycle.

This skill automates the entire software release workflow within Windsurf. It analyzes commit history based on conventional commits to determine the correct semantic version bump (major, minor, or patch). It then automatically generates comprehensive changelogs, manages Git tags, updates version files, and publishes the final release package to a registry. It is essential for teams implementing robust CI/CD practices.

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.

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.