bel-ai-jar 0.1.1

Git hooks for understanding AI-generated code changes

bel-ai-jar

Git hooks for understanding AI-generated code changes.

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Problem

When working with AI-generated code, developers often face the challenge of understanding the changes made by AI agents. This can lead to a lack of cognitive understanding and ownership of the code, making it difficult to maintain and debug in the future.

Solution

Bel-ai-jar is an open-source library that helps developers understand AI-generated code changes by asking questions before committing the code. This ensures that developers truly understand what has been implemented by the AI agent, maintaining cognitive ability and ownership of the code.

User Flow

Installation and Configuration Flow

flowchart TD
    A1[Install bel-ai-jar library] --> A2[Configure bel-ai-jar using terminal]
    A2 --> A3[Configure total number of question]
    A3 --> A4[Configure number of answer options on each question]
    A4 --> A5[Configure passing grade]
    A5 --> A6[Configure additional prompt to enhance question generation]

Evaluation and Git Hooks Flow

flowchart TD
    A1[User made code changes] --> A2[User runs git commit]
    A2 --> A3[bel-ai-jar pre-commit hook triggers]
    A3 --> A4{ai_coauthored_only enabled?}
    A4 --> |No - default| A5[bel-ai-jar analyzes staged changes and generates questions]
    A4 --> |Yes| A4b{AI co-author detected?}
    A4b --> |No| A9[Commit proceeds immediately]
    A4b --> |Yes| A5
    A5 --> A6[User answers each question]
    A6 --> |pass| A7[Code successfully committed]
    A6 --> |fail| A8[Commit rejected — user must review the changes]
    A8 --> A2

Architecture Flow

1. When you run git commit, the pre-commit hook triggers.
2. By default, bel-ai-jar evaluates every commit regardless of who wrote the code.
3. If ai_coauthored_only: true is set in the config, bel-ai-jar first checks whether the commit is co-authored by an AI tool (e.g. Claude, Copilot). If no AI co-author is detected, the commit proceeds immediately — no quiz needed.
4. Bel-ai-jar analyzes your staged changes and generates multiple-choice questions using an LLM.
5. You answer the questions in your terminal.
6. If you pass, the commit proceeds. If you fail, the commit is rejected and you must review the changes before trying again.

Tech Stack

• Python ≥ 3.8 / Node.js ≥ 16: Supported runtimes.
• Mistral / OpenAI / Gemini / Local Llama: Pluggable AI backends for question generation.

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Installation

Python (pip)

pip install bel-ai-jar

JavaScript (npm)

npm install -g bel-ai-jar

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Usage

Initialize Configuration

Python:

bel-ai-jar init

JavaScript:

bel-ai-jar init

This will guide you through setting up:

• Number of questions to ask (default: 3)
• Number of answer options per question (default: 4)
• AI model selection (Mistral, OpenAI GPT-3.5/4, Google Gemini, or Local Llama)
• Strict mode (default: enabled — must answer all questions correctly)
• Passing grade (if strict mode disabled)
• Additional prompt for question generation

Disable bel-ai-jar

bel-ai-jar disable

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How AI Co-author Detection Works

Bel-ai-jar only runs the evaluation quiz when a commit is co-authored by an AI tool. Pure human commits are always passed through without interruption.

Automatic detection

When you commit with git commit -m "...", git writes the message to .git/COMMIT_EDITMSG before the hook runs. Bel-ai-jar scans that file for Co-Authored-By: trailers and matches against known AI tool signatures:

Tool	Detected pattern
Claude (Anthropic)	anthropic, claude
GitHub Copilot	copilot
ChatGPT	chatgpt, openai
Gemini	gemini
Cursor	cursor
Codeium	codeium
Tabnine	tabnine

Example commit message that triggers evaluation:

feat: add user authentication

Co-Authored-By: Claude Sonnet 4.6 <[e-mail]>

Interactive commit fallback

When using git commit without -m (interactive editor), the hook runs before you type the message, so the co-author trailer isn't available yet. Use the BEL_AI_JAR_INTERACTIVE_COMMIT env var to signal that this is an AI-assisted commit:

# Single commit
BEL_AI_JAR_INTERACTIVE_COMMIT=1 git commit

# Or export for the whole shell session if you're always pairing with AI
export BEL_AI_JAR_INTERACTIVE_COMMIT=1

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Configuration

Configuration is stored in .bel-ai-jar.json in your project root. This file is automatically added to .gitignore during init. Run bel-ai-jar help at any time to see all options and their defaults in your terminal.

Option	Type	Default	Description
total_questions	int	3	Questions asked per evaluation
answer_options	int	4	Answer choices per question
strict_mode	bool	true	Require 100% score to pass
passing_grade	int	100	Passing % when strict_mode is false
ai_model	string	"mistral"	LLM backend (mistral / openai-gpt3.5 / openai-gpt4 / gemini / local-llama)
ai_coauthored_only	bool	false	When false (default), every commit triggers evaluation. Set to true to only evaluate commits that contain an AI Co-Authored-By trailer.
additional_prompt	string	null	Extra instructions appended to the question-generation prompt
max_diff_lines	int	1000	Max diff lines sent to the LLM. Larger diffs are trimmed before prompting to reduce token usage.
excluded_files	array	see below	Glob patterns of files to skip during diff analysis.
model_params	object	{}	Extra params for the local-llama backend, e.g. {"api_url": "..."}

Default excluded_files patterns (lock files, build artefacts, documentation):

*.lock, package-lock.json, yarn.lock, poetry.lock, Pipfile.lock
*.min.js, *.min.css, *.map
dist/*, build/*, *.egg-info/*, __pycache__/*, *.pyc, *.md

You can override any of these by editing .bel-ai-jar.json directly. For example, to also skip test snapshots and increase the diff budget:

{
  "max_diff_lines": 500,
  "excluded_files": [
    "*.lock", "package-lock.json", "yarn.lock",
    "dist/*", "build/*", "*.md",
    "**/__snapshots__/*"
  ]
}

Security: API Keys

API keys are never stored in the config file.

Set your API key as an environment variable before running bel-ai-jar init. Use the generic variable (works for any model) or the legacy model-specific ones:

# Generic — recommended, works for all models
export BEL_AI_JAR_LLM_API_KEY='your-api-key'

# Legacy model-specific fallbacks (still supported)
export MISTRAL_API_KEY='your-mistral-api-key'   # Mistral AI
export OPENAI_API_KEY='your-openai-api-key'     # OpenAI GPT-3.5 / GPT-4
export GOOGLE_API_KEY='your-google-api-key'     # Google Gemini

Key resolution priority: explicit argument > BEL_AI_JAR_LLM_API_KEY > model-specific legacy variable.

Add the export to your shell profile (~/.zshrc, ~/.bashrc, etc.) to persist it across sessions. bel-ai-jar will automatically read the key from the environment at runtime.

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Development

Python

cd src/python

# Install in development mode (includes test dependencies)
pip install -e ".[dev]"

# Run tests
python -m pytest

JavaScript

cd src/javascript
npm install

# Run tests
npm test

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Repository Structure

bel-ai-jar/
├── src/
│   ├── python/           # Python package (pip)
│   │   ├── bel_ai_jar/   # Source code
│   │   ├── tests/        # Pytest tests
│   │   └── pyproject.toml
│   └── javascript/       # JavaScript package (npm)
│       ├── src/          # Source code
│       ├── tests/        # Jest tests
│       └── package.json
├── documentation/
├── logo.webp
└── README.md

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Publishing

PyPI

cd src/python
pip install build twine
python -m build
twine upload dist/*

npm

cd src/javascript
npm publish

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Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub.

1. Fork the repository
2. Create your feature branch (git checkout -b feature/amazing-feature)
3. Commit your changes (guided by bel-ai-jar itself 😄)
4. Push to the branch (git push origin feature/amazing-feature)
5. Open a Pull Request