skills/commit/SKILL.md
Expert-level commit creation and formatting following Conventional Commits. Make sure to use this skill whenever you need to create a commit message, save changes to git, structure a logical commit history, or when the user mentions 'commit', 'git commit', 'コミット', '変更をコミット', or asks you to push their code.
npx skillsauth add hrdtbs/agent-skills commitInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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This skill ensures your commits are logical, atomic, and structured perfectly. A commit is not just a save point; it is a communication tool for future developers (and AI agents) to understand the history and intent of a codebase.
git log --oneline.README.md while working on the auth system, commit the typo fix separately from the auth feature.git diff --cached or review the staged changes to ensure no debugging statements (console.log, print) or secrets are accidentally included.Before creating a commit, follow these steps:
Co-authored-by: for the AI Agent and the user at the end of the commit message body.Format: <type>[optional scope][optional !]: <description>
feat: A new featurefix: A bug fixdocs: Documentation only changesstyle: Changes that do not affect the meaning of the code (white-space, formatting, missing semi-colons, etc)refactor: A code change that neither fixes a bug nor adds a featureperf: A code change that improves performancetest: Adding missing tests or correcting existing testsbuild: Changes that affect the build system or external dependenciesci: Changes to CI configuration files and scriptschore: Other changes that don't modify src or test filesrevert: Reverts a previous commit! before the colon in the subject line (e.g., feat(api)!: remove v1 endpoints).BREAKING CHANGE: <description> at the very end of the body.Example 1: New feature Input: Added user authentication with JWT tokens Output:
feat(auth): implement JWT-based authentication
Example 2: Bug fix with context Input: Fixed the crash that happens when the user clicks the login button twice Output:
fix(login): resolve crash on double click
The login button was missing a debounce handler, allowing users to
submit multiple network requests. Added a 500ms debounce to prevent
race conditions during authentication.
Example 3: Breaking change
Input: Changed the API response for the /users endpoint to return an array of objects instead of a single object
Output:
feat(api)!: return array of objects from /users endpoint
BREAKING CHANGE: The `/users` endpoint now returns an array of user
objects instead of a single user object. Clients will need to update
their parsing logic.
Before committing, validate your message format by running the included validation script. Ensure you locate it relative to the SKILL.md path or use find if unsure.
# Example assuming you are in the repo root:
bash skills/commit/scripts/validate-commit.sh "your commit message"
testing
Evaluate Agent Skill design quality against official specifications and best practices. Use when reviewing, auditing, or improving SKILL.md files and skill packages. Provides multi-dimensional scoring and actionable improvement suggestions.
testing
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
development
Evaluate and score user-written LLM prompts on a 100-point scale across 5 axes (Clarity, Structure, Information Content, Specificity, Context), providing specific improvement suggestions and a revised prompt. Make sure to use this skill whenever the user asks to evaluate, review, score, or improve a prompt, or when they say things like 'このプロンプトどう?', 'プロンプトを評価して', 'rate my prompt', 'review this prompt', or 'is this prompt good enough?'. This skill focuses on scoring existing prompts, not writing new ones from scratch.
testing
Apply prompt engineering best practices to write, refine, and optimize system prompts, user prompts, and agent instructions. Use this skill whenever the user wants to write a prompt, optimize an existing prompt for better results, fix a prompt that is hallucinating or underperforming, or structure prompts for Large Language Models (LLMs). Even if the user just says "help me write instructions for my agent", trigger this skill.