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AI Command Auditor - Project Plan

Overview

This repository will host a collection of bash and python scripts for auditing and analyzing AI commands and interactions. The project will be set up with modern development practices including containerized development, proper version control, and organized code structure.

Project Setup Phases

Phase 1: Repository Initialization (3.1) ✅ COMPLETED

  • Create comprehensive README.md with project description, installation, and usage instructions
  • Setup .gitignore file with appropriate exclusions for Python, Bash, Docker, and IDE files
  • Add essential dotfiles for development consistency
  • Initialize proper Git configuration
  • Add MIT License file

Phase 2: Cursor IDE Configuration (3.2) ✅ COMPLETED

  • Create .cursor/rules folder
  • Define Cursor-specific rules for:
    • Code formatting standards
    • File naming conventions
    • Import organization
    • Documentation requirements
    • Best practices for Python and Bash development

Phase 3: Development Container Setup (3.3) ✅ COMPLETED

  • Create .devcontainer folder structure
  • Design Dockerfile with:
    • Ubuntu/Debian base image
    • Python 3.11+ installation with pip
    • Bash shell enhancements
    • Essential development tools (git, curl, wget, etc.)
    • GitHub CLI installation
    • Useful utilities for script development
  • Create docker-compose.yaml for service orchestration
  • Configure devcontainer.json with:
    • VS Code extensions for Python and Bash
    • Port forwarding configuration
    • Volume mounts for persistent development
    • Environment variables setup
  • Create requirements.txt and requirements-dev.txt with all necessary dependencies

Phase 4: Development Environment Activation (3.4) ✅ COMPLETED

  • Restart IDE to recognize devcontainer configuration
  • Build and start development container
  • Verify all tools and dependencies are properly installed
  • Test container environment functionality

Phase 5: GitHub Integration (3.5) ✅ COMPLETED

  • User authentication with GitHub CLI
  • Configure Git credentials within container
  • Test GitHub CLI functionality
  • Setup repository connection for seamless workflow

Phase 6: Project Structure Creation (3.6) ✅ COMPLETED

  • Design and create folder structure:

    /
├── docs/
│   ├── planning/
│   ├── api/
│   └── user-guides/
├── scripts/
│   ├── bash/
│   │   ├── utils/
│   │   ├── automation/
│   │   └── monitoring/
│   └── python/
│       ├── core/
│       ├── analysis/
│       ├── reporting/
│       └── tests/
├── config/
├── data/
│   ├── input/
│   ├── output/
│   └── samples/
├── templates/
└── tools/

  • Create placeholder files and basic structure

  • Add README files for each major directory

  • Setup Python package structure with __init__.py files

  • Create configuration template file

  • Add .gitkeep files for empty directories

Phase 7: Git Hooks and CI Pipeline Setup (3.7) ✅ COMPLETED

  • Setup comprehensive pre-commit hooks using Python-based pre-commit package
  • Configure automated code formatting with Black and isort
  • Implement code quality checks with Pylint and MyPy
  • Add security scanning with Bandit
  • Setup Bash script validation with ShellCheck
  • Create pre-push hooks to run full CI pipeline locally
  • Implement GitHub Actions CI pipeline with multiple jobs:
    • Python linting (Black, isort, Pylint, MyPy)
    • Bash linting (ShellCheck)
    • Python testing with multiple versions (3.9, 3.10, 3.11)
    • Integration testing
    • Security scanning (Bandit, Safety)
  • Setup automatic hook installation script (scripts/setup-hooks.sh)
  • Configure pre-commit and pre-push hooks to catch CI issues locally
  • Test and verify all hooks and CI pipeline functionality

Current Status ✨

✅ Completed Components

  1. Repository Infrastructure: Complete with README, LICENSE, .gitignore
  2. Development Environment: Full devcontainer setup with Python 3.11, GitHub CLI, Docker
  3. Code Standards: Comprehensive Cursor rules for Python and Bash development
  4. Project Structure: Complete folder hierarchy with proper Python package structure
  5. Documentation: Initial documentation structure and README files
  6. Configuration: Template configuration file with all major settings
  7. Git Hooks & CI Pipeline: Comprehensive automated quality assurance system
    • Pre-commit hooks with formatting, linting, and security checks
    • Pre-push hooks with full CI simulation
    • Multi-job GitHub Actions pipeline with testing and validation
    • Automated code quality enforcement

🎉 Next Steps

All planned phases are now complete! The repository is fully set up and ready for feature development:

  1. Development Environment: ✅ Active and verified
  2. GitHub Integration: ✅ Authenticated and working
  3. Quality Assurance: ✅ Git hooks and CI pipeline operational
  4. Project Structure: ✅ Complete and organized

Ready for feature development using the established workflow:

  • Create feature branches following GitFlow
  • Use task planning documents for complex features
  • Leverage automated quality checks via git hooks
  • Submit PRs with automated CI validation

📋 Files Created

  • README.md - Comprehensive project documentation
  • LICENSE - MIT License
  • .gitignore - Comprehensive exclusions for Python, Bash, Docker, IDEs
  • .cursor/rules/cursor_rules.md - Complete coding standards and best practices
  • .devcontainer/ - Complete development container configuration
    • devcontainer.json - VS Code dev container config with extensions
    • docker-compose.yml - Container orchestration
    • Dockerfile - Development environment image
  • requirements.txt - Production dependencies
  • requirements-dev.txt - Development dependencies
  • scripts/ - Complete folder structure with Python packages
  • docs/ - Documentation structure
  • config/config.template.yml - Configuration template

Success Criteria

  • Repository is fully containerized and reproducible
  • Development environment is consistent across different machines
  • Code organization follows best practices
  • GitHub integration is seamless
  • Documentation is comprehensive and up-to-date
  • Ready for collaborative development

Feature Development Guidelines

Starting New Features

With all setup phases complete, follow this workflow for new features:

  1. Create Task Planning Document: For complex features, create a planning document in docs/task-planning/
  2. Create Feature Branch: git checkout -b feature/your-feature-name
  3. Implement with Quality Checks: Git hooks will automatically validate code quality
  4. Test Thoroughly: Use the established test structure in scripts/python/tests/
  5. Submit PR: CI pipeline will run comprehensive validation
  6. Review and Merge: Follow the established GitFlow process

Available Tools and Scripts

  • Python Development: Core modules in scripts/python/core/
  • Bash Utilities: Shell scripts in scripts/bash/utils/
  • Configuration: Template in config/config.template.yml
  • Documentation: Structure in docs/ with planning, API, and user guides

Quality Assurance

  • Pre-commit: Automatic formatting and linting
  • Pre-push: Full CI simulation before push
  • GitHub Actions: Multi-job validation pipeline
  • Security: Automated vulnerability scanning

Notes

  • All development should happen within the devcontainer for consistency
  • Follow established coding standards and documentation practices
  • Regular commits and meaningful commit messages
  • Maintain backward compatibility when possible