git-commit-writer
Writes conventional commit messages by analyzing your staged git changes. Detects commit type, scope, and breaking changes automatically.
by Samuel Rose
About This Skill
Stop writing vague commit messages. This skill reads your actual staged diff and generates precise, informative commit messages following the Conventional Commits specification. It detects the commit type (feat, fix, refactor, docs, chore, etc.), identifies the scope from the changed files, flags breaking changes, and suggests splitting commits when multiple logical changes are staged. Works with any git repository.`
How to Install
unzip git-commit-writer.zip -d ~/.claude/skills/What This Skill Does
Stop writing vague commit messages. This skill reads your actual staged diff and generates precise, informative commit messages following the Conventional Commits specification.
It detects the commit type (feat, fix, refactor, docs, chore, etc.), identifies the scope from the changed files, flags breaking changes, and suggests splitting commits when multiple logical changes are staged. Works with any git repository.
Example Usage
Ask your agent: "Commit my staged changes with a good message"
Free
One-time purchase • Own forever
Security Scanned
Passed automated security review
8/8 checks passed
Tags
Frequently Asked Questions
Learn More About AI Agent Skills
Similar Skills
env-doctor
Diagnoses why your project will not start. Checks runtime versions, dependencies, environment variables, databases, ports, and build artifacts systematically.
changelog-generator
Generates user-facing changelogs from git history. Transforms developer commit messages into clear release notes that users actually understand.
migration-auditor
Catches dangerous database migrations before they hit production. Reviews schema changes for locking hazards, data loss, missing rollbacks, and index issues across PostgreSQL, MySQL, and SQLite.
evaluating-ai-harness-dimensions
Evaluates AI coding agent platforms across five structural dimensions that determine real-world performance independently of model quality, so teams select on architectural fit rather than benchmark scores.