Guia de compromisso do AI Compose

Visão geral

AI Compose Commit lê as alterações de código atuais e gera mensagens de commit no formato Convencional Commits. A questão é simples: gaste menos tempo escrevendo commits enquanto mantém o histórico mais claro.

O que é o AI Compose Commit?

O AI Compose Commit analisa suas alterações e escreve mensagens de commit padronizadas para elas. Ele troca a escrita manual de commits por um histórico mais consistente.

Use-o quando:

Grandes alterações de código que exigem vários commits
Projetos multi-repositórios com mudanças coordenadas
Equipes que exigem formatos de commit padronizados
Desenvolvedores que desejam melhorar a qualidade do commit

Principais vantagens

Recurso	Confirmações manuais tradicionais	Compromisso de composição de IA
Comprometa a qualidade	Depende da experiência individual	Baseado nas melhores práticas, qualidade consistente
Velocidade de confirmação	Requer pensamento e organização	Geração instantânea com um clique
Consistência	Pode variar entre commits	Segue padrões automaticamente
Suporte a vários repositórios	Coordenação manual necessária	Reconhecimento e agrupamento automático de repositórios
Economia de tempo	Pode levar um tempo significativo	Economiza de 10 a 60+ segundos por commit

Início rápido

Habilitar recurso

No aplicativo de desktop HagiCode, navegue até o projeto onde deseja usar o AI Compose Commit e habilite este recurso.

Importante: Certifique-se de que seu projeto foi adicionado ao HagiCode primeiro.

Recurso de gatilho

Localize o botão “AI Combined Commit” na seção de operações do Git.

Botão de confirmação combinado de IA

Este botão normalmente está localizado em destaque na seção de operações do Git, próximo a outras ações de controle de versão.

Nota importante: Clicar neste botão acionará a geração automática de mensagens de commit. Se você precisar reverter ou modificar commits, use comandos padrão do Git.

Alerta de risco de primeiro uso

Ao usar o AI Compose Commit pela primeira vez ou com alterações não aprovadas, o sistema exibirá uma caixa de diálogo de confirmação.

Segunda caixa de diálogo de confirmação

A caixa de diálogo contém opções, incluindo:

Confirme e inicie o processamento
Entenda os riscos, não mostre mais este aviso
Marcar esta opção torna o uso subsequente mais conveniente

Recomendamos a leitura cuidadosa do prompt de risco durante o primeiro uso para compreender as implicações dos commits automáticos.

Ver resultados

Depois que o AI concluir o processamento, você verá uma notificação de sucesso.

As mensagens de commit foram geradas e estão prontas. Você pode revisá-los antes de finalizar, se necessário.

Detalhes do recurso

Lógica de análise de IA

O AI Compose Commit analisa as alterações do seu código por meio do seguinte processo:

1. Leia as alterações no espaço de trabalho

O sistema coleta todos os arquivos modificados, adicionados recentemente e excluídos no espaço de trabalho atual.

2. Analise o conteúdo do arquivo

A IA analisa profundamente o conteúdo dos arquivos alterados para compreender:

Mudanças funcionais implementadas
Correções de bugs aplicadas
Padrões de refatoração usados
Dependências adicionadas ou removidas

3. Identifique associações de mudança

A IA identifica de forma inteligente quais mudanças devem ser agrupadas:

Mudanças de front-end e back-end para o mesmo recurso
Modificações relacionadas ao arquivo de configuração
Teste atualizações junto com mudanças de implementação

4. Gerar estrutura de commit

AI gera informações de commit estruturadas seguindo a especificação de commits convencionais:

&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;tipo&gt;(&lt;escopo&gt;): &lt;assunto&gt;
  &lt;corpo&gt;
    &lt;rodapé&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): adiciona funcionalidade de login do usuário
Implementei autenticação OAuth2 com provedores Google e GitHub.

O componente de login agora suporta:
- Integração do Google OAuth
- Integração GitHub OAuth
- Gerenciamento de sessão com tokens JWT

Coautor: 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:

Coautor: HagiCode &lt;noreply@hagicode.com&gt;

Repository override example:

git config --local coAuthoredBy.name "Bot da plataforma"
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:

Coautor: 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.