AI Compose Commit Guide

Overview

AI Compose Commit reads the current code changes and generates commit messages in Conventional Commits format. The point is simple: spend less time writing commits while keeping history clearer.

What is AI Compose Commit?

AI Compose Commit analyzes your changes and writes standardized commit messages for them. It trades manual commit writing for a more consistent history.

Use it when:

Large code changes requiring multiple commits
Multi-repository projects with coordinated changes
Teams requiring standardized commit formats
Developers who want to improve commit quality

Main Advantages

Feature	Traditional Manual Commits	AI Compose Commit
Commit Quality	Depends on individual experience	Based on best practices, consistent quality
Commit Speed	Requires thought and organization	One-click, instant generation
Consistency	May vary between commits	Automatically follows standards
Multi-repo Support	Manual coordination required	Automatic repo recognition and grouping
Time Savings	Can take significant time	Saves 10-60+ seconds per commit

Quick Start

Enable Feature

In the HagiCode desktop application, navigate to the project where you want to use AI Compose Commit and enable this feature.

Important: Ensure your project has been added to HagiCode first.

Trigger Feature

Locate the “AI Combined Commit” button in the Git operations section.

AI Combined Commit Button

This button is typically located prominently in the Git operations section, near other version control actions.

Important Note: Clicking this button will trigger automatic commit message generation. If you need to roll back or modify commits, use standard Git commands.

First Use Risk Prompt

When using AI Compose Commit for the first time or with unapproved changes, the system will display a confirmation dialog.

Second Confirmation Dialog

The dialog contains options including:

Confirm and start processing
Understand risks, no longer show this prompt
Checking this option makes subsequent use more convenient

Recommend carefully reading the risk prompt during first use to understand the implications of automatic commits.

View Results

After AI completes processing, you will see a success notification.

Commit messages have been generated and are ready. You can review them before finalizing if needed.

Feature Details

AI Analysis Logic

AI Compose Commit analyzes your code changes through the following process:

1. Read Workspace Changes

The system collects all modified, newly added, and deleted files in the current workspace.

2. Analyze File Content

AI deeply analyzes the content of changed files to understand:

Functional changes implemented
Bug fixes applied
Refactoring patterns used
Dependencies added or removed

3. Identify Change Associations

AI intelligently identifies which changes should be grouped together:

Frontend and backend changes for the same feature
Related configuration file modifications
Test updates alongside implementation changes

4. Generate Commit Structure

AI generates structured commit information following Conventional Commits specification:

<type>(<scope>): <subject>
  <body>
    <footer>

5. Write Commit Message

Each change is transformed into a clear, descriptive commit message.

Commit Format Specification

AI-generated commit messages follow the Conventional Commits format:

&lt;type&gt;(&lt;scope&gt;): &lt;subject&gt;
  &lt;body&gt;
    &lt;footer&gt;

type: Commit type (required)

feat: New feature
fix: Bug fix
docs: Documentation changes
style: Code style changes (formatting, no code change)
refactor: Code refactoring
test: Adding or updating tests
chore: Build process or auxiliary tool changes
perf: Performance improvements

scope: Scope of the change (optional)

Indicates which part of the project is affected
Format: module/name or category/name

subject: Short description of the change (required)

Imperative mood
Max 50 characters
Use present tense: “Add” not “Added”

body: Detailed description (optional)

What was changed and why
Can use multiple paragraphs
Max 100 characters for first line, 72 for subsequent lines

footer: References to related issues (optional)

Closes #123
BREAKING CHANGE: ...
Co-Authored-By: &commat;anthropic.com>

Example Output

feat(auth): add user login functionality
Implemented OAuth2 authentication with Google and GitHub providers.

The login component now supports:
- Google OAuth integration
- GitHub OAuth integration
- Session management with JWT tokens

Co-Authored-By: Claude Opus 4.6 &lt;noreply@anthropic.com&gt;

Comparison with Traditional Manual Commits

Manual Commits

Traditional Git commits rely on:

Developer’s experience and mood
Individual writing habits
Time constraints affecting quality
Inconsistent formatting across team

AI Compose Commit

AI Compose Commit provides:

Standardized format: Always follows Conventional Commits specification
Intelligent analysis: Understands code context and relationships
Quality consistency: Maintains professional tone and accuracy
Time efficiency: Reduces commit writing time from 5-30 minutes to seconds

When to Use Each

Use AI Compose Commit When:

Large batch changes (multiple files, complex features)
Coordinated changes across multiple repositories
Need for high-quality, consistent commit messages
Team projects with multiple contributors

Use Manual Commits When:

Small, focused changes (single file, simple bug fix)
Personal projects with full control over commit quality
When you have time to craft thoughtful, personalized messages

Multi-Repository Support

monospecs Integration

When managing monospecs-based multi-repository projects, AI Compose Commit provides enhanced capabilities:

Read monospecs.yaml

AI automatically reads the monospecs.yaml configuration to:

Identify all configured sub-repositories
Match file paths to corresponding repositories
Access display names and icons for UI rendering

Match File Changes

AI intelligently matches each changed file to the correct sub-repository:

Analyzes file path in project structure
Matches against monospecs repository configurations
Groups related changes together for logical commits

Separate Commits

Each sub-repository receives independent commits:

Frontend changes committed to repos/frontend
Backend changes committed to repos/backend
Maintains independent git history and version control

AI Compose Commit appends the currently effective Co-Authored-By trailer to every generated commit. Interactive Git commits and the OpenSpec v1 archive prompt reuse the same resolution rules, so teams only need to maintain one convention.

The precedence order is fixed:

Project-level config: local Git config in the current repository (git config --local)
Global config: user-level Git config (git config --global)
Built-in default: Hagicode <noreply@hagicode.com>

If a layer is missing name, missing email, or contains an invalid email address, HagiCode skips that layer and falls back to the next one instead of emitting a malformed trailer.

Default output:

Co-Authored-By: HagiCode &lt;noreply@hagicode.com&gt;

Repository override example:

git config --local coAuthoredBy.name "Platform Bot"
git config --local coAuthoredBy.email "platform-bot@example.com"

Global fallback example:

git config --global coAuthoredBy.name "Hagicode"
git config --global coAuthoredBy.email "noreply@hagicode.com"

With the local override above, the trailer becomes:

Co-Authored-By: Platform Bot &lt;platform-bot@example.com&gt;

Operational details:

If the commit message already contains the same resolved trailer, HagiCode does not append a duplicate.
If the message already contains a different Co-Authored-By line, HagiCode keeps that line and still appends the resolved AI trailer.
Teams should treat repository-level config as the canonical rule and keep global config as a personal fallback.

Advantages for Multi-Repo Projects

Traceability: Clear history of which repository received which changes Coordination: Changes across multiple repos can be committed simultaneously or sequentially Consistency: Uniform commit format across all repositories Efficiency: Reduced coordination overhead for changes spanning multiple repositories

Usage Guidelines

Best Practices

For optimal AI Compose Commit results:

1. Keep Changes Focused

Each commit should address a single feature or fix
Avoid bundling unrelated changes in one commit
Make incremental progress with meaningful commit messages

2. Clear Code Comments

Add comments in complex code to help AI understand intent
Use descriptive comments for non-obvious implementation details
Keep comments updated as code evolves

3. Reasonable File Naming

Use clear, descriptive file names
Follow project naming conventions
Avoid overly generic names that don’t convey purpose

4. Timely Committing

Commit frequently as meaningful changes are completed
Don’t accumulate large batches of uncommitted work
Each commit should represent a logical unit of progress

5. Leverage Multi-Repo Intelligence

Let AI group related changes across repositories
Use descriptive scopes to indicate affected modules
Review AI’s suggested groupings before committing

Usage Restrictions

Limitations

AI Compose Commit has the following usage limitations:

1. Network Dependency

Requires active connection to AI service
Processing time depends on network conditions
Large codebases may take longer to analyze

2. File Size Limits

Very large files (>10MB) may exceed processing limits
Binary files are not analyzed (only text-based content)
Split large changes into smaller commits if needed

3. Codebase Complexity

Extremely complex code may be difficult to analyze fully
Deeply nested or obfuscated code may have reduced accuracy
Results are best-effort based on visible code

4. First Use Considerations

When using AI Compose Commit for the first time:

Start with smaller, safer changes to understand AI behavior
Verify generated commits before finalizing
Provide clear code comments to improve AI understanding
Check that project is properly connected in HagiCode

Troubleshooting

Feature Not Available

If you don’t see the AI Combined Commit button:

Ensure the project is added to HagiCode
Refresh the interface
Check if this feature is enabled in your version
Verify your network connection to AI service

No Response After Clicking

If nothing happens after clicking the button:

Check for notification dialogs (may be hidden behind other windows)
Check browser console for error messages
Verify network connectivity
Try restarting the application

Incorrect Generated Messages

If AI generates incorrect commit messages:

Manually modify using git commit --amend
Add more descriptive comments to help AI understand
Check code changes are accurately represented
Re-trigger the feature after making adjustments

Processing Takes Too Long

If processing seems to be taking an unusually long time:

Check if there are very large files being analyzed
Verify network stability
Try splitting changes into smaller batches
Check if AI service is experiencing high load

If commits don’t include the expected footer:

This may indicate AI service is unavailable
Check your API token configuration in HagiCode settings
Verify network connectivity
Check whether the local coAuthoredBy.name / coAuthoredBy.email config is incomplete and therefore falling back to another layer
Consider generating commits manually if issue persists

For more information, refer to the HagiCode documentation or check the monospecs repository for configuration examples.