Boost Your Git Workflow: Master AI-Powered Conventional Commits

In modern software development, standardized commit messages are essential for team collaboration and project maintainability. AI Commits technology automatically generates Conventional Commit messages by analyzing code diffs and context, freeing developers from manual formatting.

High-quality commit messages can automatically generate release logs for a Spring Boot project.

1. Basics of Git Commit Conventions

1.1 Introduction to Conventional Commits

Conventional Commits is a widely used Git commit specification that makes messages clear and machine‑readable. It originated from the Angular team and is now a de‑facto standard in the open‑source community.

Benefits include clearer history, automatic changelog generation, version bump automation, and easier code review.

Commit history becomes clearer, helping team members quickly understand changes.

Automatic generation of changelogs.

Version numbers can be inferred from commit types.

Facilitates code review by conveying intent.

1.2 Basic Format and Components

The standard format is:

<code>&lt;type&gt;(&lt;scope&gt;): &lt;description&gt;

[optional body]

[optional footer(s)]</code>

Core components:

type : required, indicates the nature of the change (e.g., feat, fix, docs, style, refactor, test, chore).

scope : optional, the affected module or area.

description : required, a concise imperative summary.

body : optional, detailed explanation.

footer : optional, notes breaking changes or issues closed.

2. AI Commits Implementation

The AI Commits workflow consists of:

Change analysis : obtain staged diff (git diff --staged) and analyze changed files.

Context collection : fetch recent commit history (default 10) and extract project commit style.

Prompt construction : build prompts from changes and project conventions.

LLM invocation : send prompts to an LLM service and parse the generated message.

Post‑processing : format the output and apply custom rules/filters.

3. Practical Guide

3.1 Tool Comparison

AI Commits offers model flexibility, prompt customization, and lower subscription cost compared to GitHub Copilot, Cursor, and 通义灵码.

3.2 IntelliJ AI Commits Plugin Setup

Install the “AI Commits” plugin from the Marketplace and restart the IDE.

Configure the LLM model (e.g., DeepSeek‑V3, Claude‑3.5‑Sonnet) in the plugin settings.

3.3 Prompt Template Configuration

<code>Write a concise, clear, and informative commit message based on the conventional commit specification.

- Format: `<type>(<scope>): <description>`
- Accurately classify the commit type (feat, fix, docs, style, refactor, test, chore) and scope.
- Use present tense, active voice, and keep lines under 72 characters.
- Include back‑ticked code, filenames, or numbers in the subject.
- Optional body: provide relevant details, why the change was made, and its impact.</code>