A Django-based control center for engineering leaders to monitor team delivery and performance metrics.
- Backend: Django 6.0 + Django REST Framework
- Database: PostgreSQL
- UI: Django Admin panel + Metabase
- Testing: pytest
- Linting: black, isort, ruff
- Containerization: Docker + Docker Compose
- Python 3.14+
- Docker & Docker Compose
- Create and activate virtual environment:
python -m venv .venv
source .venv/bin/activate # On Windows: .venv\Scripts\activate- Install dependencies:
pip install -r requirements.txt- Run migrations:
python manage.py migrate- Run development server:
python manage.py runserverdocker-compose uppytestblack .
isort .
ruff check .Run all CI checks (formatters + linters + tests) at once:
./scripts/validate-ci.shThis is the same validation that runs in GitHub Actions CI and automatically in the AI system.
control_center/- Django project settingshealthcheck/- Health check endpointtests/- Test suitedocs/- Documentation.claude/- Claude Code AI development configuration
This project uses Claude Code with an agentic system for development.
- CLAUDE.md - Main guide for AI behavior and context
.claude/settings.json- Pre-configured hooks and agents.claude/agents/- Specialized agents (e.g., dev-feedback).claude/hooks/- Automation hooks documentationscripts/- Utility scripts (CI validation, accessible to all)
dev-feedback: When providing feedback on AI-generated code that requires systemic changes, this agent analyzes the feedback and updates relevant documentation and automation.
- Make changes (or have AI make them)
- Run
./scripts/validate-ci.shto verify all checks pass (optional - AI runs this automatically) - Commit and push
- CI runs automatically on PR
The AI system is pre-configured in .claude/settings.json to run CI validation automatically before completing responses.