pycodemark 0.4.1

PyCodemark – a full-featured code review tool for Python.

PyCodemark

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Overview

PyCodemark is a full-featured Python code review tool that automatically detects style issues, missing docstrings, and other code quality problems.
It supports terminal, JSON, and SARIF output for CI/CD integration and is extensible via plugins.

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Features

• Detect missing docstrings, PEP8 violations, and clarity issues
• JSON and SARIF output for CI pipelines
• Plugin system for custom checks
• Pre-commit integration
• Configurable via pyproject.toml
• Works with Python 3.13+

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Installation

Clone the repository:

git clone https://github.com/roshanguptamca/pycodemark.git
cd pycodemark

Create a Python 3.13 virtual environment:

python3.13 -m venv venv 
source venv/bin/activate
poetry install

Quick Start

Quick Start with PyCodemark

PyCodemark is a full-featured Python code review tool that detects style issues, missing docstrings, and other code quality problems. It supports terminal, JSON, and SARIF output for CI integration.

Installation

Install PyCodemark via pip:

pip install pycodemark

Or with Poetry:

poetry add pycodemark 

poetry run pycodemark review src/
poetry run  pycodemark review src/ --format json
poetry run pycodemark review src/ --format sarif  

Run a basic code review:

pycodemark review src/
pycodemark review src/ --format json
pycodemark review src/ --format sarif    

Environment Variables

PyCodemark requires an OpenAI API key to perform AI-powered smart code reviews. You can also optionally specify which OpenAI model to use.

1. OpenAI API Key

Set your API key as an environment variable:

export OPENAI_API_KEY="sk-xxxxxxxxxxxxxxxxxxxxxxxx" 

This is required for the smart-review command.

The tool will use this key to authenticate with the OpenAI API.

You can obtain your key from OpenAI API Keys 2.Optional: Specify AI Model

By default, PyCodemark uses the gpt-5 model for smart reviews. You can override it with the environment variable CODEMARK_MODEL:

export CODEMARK_MODEL="gpt-5" 

If not set, gpt-5 will be used automatically. You can specify any OpenAI chat-capable model available to your account. Example models: gpt-5, gpt-5.1, gpt-4, gpt-4-32k.

Quick Example:

export OPENAI_API_KEY="sk-xxxx"
export CODEMARK_MODEL="gpt-5"

# Run AI-powered review
pycodemark smart-review src/

Any issues detected by the AI will appear in the terminal table, JSON, or SARIF output depending on the chosen --format.

If your quota is exceeded or the API fails, the tool will log the error in the report.

4. Security Tips Do not commit your API key to version control. Store keys securely in environment variables or secret managers. You can also use .env files with tools like direnv or python-dotenv.

Plugins Extend PyCodemark by adding custom checks under src/codemark/plugins/. Each plugin must implement: ```python` def run(file_path, config): """Return a list of issues detected in the file.""" return []

Pre-commit Integration

Add to your .pre-commit-config.yaml:
```yaml repos:
  - repo: local
    hooks:
      - id: codemark
        name: codemark-review
        entry: codemark review --format terminal
        language: system
        types: [python]     
```

Then run:
```bashpre-commit install
```pre-commit install

CI/CD Integration (GitHub Actions)

You can integrate PyCodemark in CI pipelines with SARIF reporting. Example workflow file: .github/workflows/codemark.yml

Runs PyCodemark on every push or pull request

Uploads SARIF results to GitHub for code scanning

Provides automated code quality checks

Configuration

Configure PyCodemark via pyproject.toml:

[tool.pycodemark]
max_line_length = 88
rules = ["PEP8", "clarity", "docstrings"]
exclude = ["tests/", "migrations/"]

Makefile Commands:

• make lint: Run code linting
• make venv # Create virtual environment
• make install # Install dependencies via Poetry
• make build # Build package
• make test # Run tests
• make lint # Run linter
• make lint-fix # Automatically fix lint issues
• make review # Run terminal review
• make review-json # Run review with JSON output
• make review-sarif # Run review with SARIF output
• make publish # Publish to PyPI

Download Documentation: PDF HTML

License

MIT License © Roshan Gupta

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If you want, I can also provide a **one-liner command** so you can generate this README automatically on your Mac without manually copying it.  

Do you want me to do that next?