AI Compose 提交指南

概述

AI Compose Commit 讀取目前程式碼變更並產生傳統提交格式的提交訊息。重點很簡單：花更少的時間編寫提交，同時保持歷史記錄更清晰。

什麼是 AI Compose Commit？

AI Compose Commit 會分析您的變更並為其編寫標準化的提交訊息。它用手動提交寫入來換取更一致的歷史記錄。

在以下情況下使用它：

需要多次提交的大型程式碼更改
具有協調變更的多重儲存庫項目
需要標準化提交格式的團隊
想要提高提交品質的開發人員

主要優點

特點	傳統手動提交	AI 撰寫投稿
承諾品質	取決於個人經驗	基於最佳實踐，始終如一的質量
提交速度	需要思考和組織	一鍵即時生成
一致性	提交之間可能有所不同	自動遵循標準
多倉庫支援	需要手動協調	自動倉庫識別和分組
節省時間	可能需要大量時間	每次提交節省 10-60 秒以上

快速入門

啟用功能

在 HagiCode 桌面應用程式中，導航至要使用 AI Compose Commit 的項目並啟用此功能。

重要：首先確保您的專案已新增至 HagiCode。

觸發功能

在Git操作部分找到「AI組合提交」按鈕。

AI組合提交按鈕

此按鈕通常位於 Git 操作部分的顯著位置，靠近其他版本控制操作。

重要提示：按一下此按鈕將觸發自動提交訊息產生。如果需要回滾或修改提交，請使用標準 Git 指令。

首次使用風險提示

首次使用 AI Compose Commit 或進行未經批准的更改時，系統將顯示確認對話方塊。

第二次確認對話框

此對話方塊包含的選項包括：

確認並開始處理
了解風險，不再顯示此提示
勾選此選項，後續使用更加方便

建議首次使用時仔細閱讀風險提示，以了解自動提交的含義。

查看結果

AI完成處理後，您將看到成功通知。

提交訊息已產生並準備就緒。如果需要，您可以在最終確定之前查看它們。

功能詳情

人工智慧分析邏輯

AI Compose Commit 透過以下流程分析您的程式碼變更：

1. 讀取工作區更改

系統會收集目前工作區中所有修改、新增、刪除的檔案。

2. 分析文件內容

AI 深入分析更改的文件內容以了解：

實作功能變更
應用了錯誤修復
所使用的重構模式
新增或刪除的依賴項

3. 辨識變革關聯

人工智慧可以智慧地識別哪些變更應該分組在一起：

相同功能的前端和後端更改
相關設定檔修改
測試更新以及實施更改

4. 產生提交結構

AI 依照傳統提交規範產生結構化提交資訊：

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

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;類型&gt;（&lt;範圍&gt;）：&lt;主題&gt;
  &lt;正文&gt;
    &lt;頁尾&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)：新增使用者登入功能
與 Google 和 GitHub 提供者一起實施了 OAuth2 身份驗證。

登入組件現在支援：
- 谷歌 OAuth 集成
- GitHub OAuth 集成
- 使用 JWT 令牌進行會話管理

共同創作者：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:

共同作者：HagiCode &lt;noreply@hagicode.com&gt;

Repository override example:

git config --local coAuthoredBy.name“平台機器人”
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:

共同作者：Platform Bot <platform-bot@example.com>

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.