Python Project CI/CD Pipeline Design

I'll help you design a comprehensive CI/CD pipeline for your Python project. Let me understand your requirements:

## Understanding Your Project

**Project Architecture:**
- What type of Python project are you building? (microservice API, distributed analytics engine, CLI tool, web application, library)
- What are your key dependencies and integration points?
- Are there specific concurrency models or architectural patterns I should know about?

**Python Runtime Requirements:**
- Which Python versions do you need to support? (e.g., 3.10, 3.11, 3.12)
- Do you have any version-specific features or compatibility requirements?
- Are you targeting specific operating systems?

**Code Quality Standards:**
- What are your expectations for code formatting? (Black, autopep8, or custom)
- Do you need import sorting? (isort standards)
- What linting rules should be enforced? (Flake8 configuration)
- Do you require type checking? (MyPy strictness level)
- What code coverage threshold is acceptable? (typically 80%+)

**Security & Compliance:**
- Are there specific compliance requirements? (OWASP, NIST 800-53, ISO 27001)
- What level of security scanning do you need? (Bandit, Safety)
- Should security scans block deployments or just warn?
- Are there internal security policies to enforce?

**Testing Requirements:**
- What types of tests do you have? (unit, integration, async/event loop)
- What testing frameworks and libraries do you use? (pytest, unittest, mocking libraries)
- Do you need specific test fixtures or conventions documented?

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Based on your answers, I'll provide:

## 1. Complete GitHub Actions Workflow

A production-ready `.github/workflows/ci.yml` file that includes:
- Multi-version Python testing matrix
- Automated code formatting checks (Black)
- Import sorting verification (isort)
- Linting with configurable rules (Flake8)
- Strict type checking (MyPy)
- Security scanning (Bandit for code, Safety for dependencies)
- Test execution with coverage reporting
- Artifact preservation for audit trails
- Scheduled nightly runs for dependency drift detection

## 2. Unified Configuration File

A complete `pyproject.toml` with:
- Build system configuration
- Tool-specific settings (Black, isort, MyPy, pytest, coverage, Bandit)
- Consistent formatting standards across all tools
- Coverage thresholds and reporting options

## 3. Development Dependencies

A `requirements-dev.txt` file with pinned versions for:
- Code formatters and linters
- Type checkers
- Security scanners
- Testing frameworks
- Coverage tools
- Pre-commit hooks

## 4. Pre-commit Configuration

Setup for local development with:
- Automatic formatting before commits
- Fast local validation
- Consistency with CI pipeline

## 5. Pipeline Optimization Features

- Dependency caching for faster builds
- Codecov integration for coverage tracking
- Multi-format coverage reports (HTML, XML, terminal)
- Configurable security scan behavior (blocking vs. warning)
- Matrix testing across Python versions
- Artifact uploads for debugging and compliance

## 6. Quality Assurance Checklist

A validation checklist to ensure:
- Pipeline validates across all declared Python versions
- Code conforms to formatting, linting, and type constraints
- Coverage thresholds are enforced
- Security scan artifacts are captured
- Builds are reproducible and deterministic
- All configurations are version-controlled

Tell me about your Python project and I'll create a complete, production-ready CI/CD pipeline tailored to your needs!