claude-code-python-stack

Allure Report integration with pytest — decorators, steps, attachments, severity levels, test categorization, CI/CD integration, and custom report plugins.

API test automation patterns — httpx/requests client wrappers, response validation with Pydantic, test data factories, retry/polling utilities, schema testing, and contract testing.

Async HTTP client patterns for Python — httpx, aiohttp, retry strategies, connection pooling, streaming, and testing with respx.

Celery patterns for distributed task queues — task definitions, retry strategies, scheduling, chains/groups, monitoring, and production configuration with Redis/RabbitMQ.

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.

Database migration best practices for schema changes, data migrations, rollbacks, and zero-downtime deployments. Covers Alembic, Django, and raw SQL.

Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies for Python applications.

Django architecture patterns, REST API design with DRF, ORM best practices, caching, signals, middleware, and production-grade Django apps.

Django security best practices, authentication, authorization, CSRF protection, SQL injection prevention, XSS prevention, and secure deployment configurations.

Django testing strategies with pytest-django, TDD methodology, factory_boy, mocking, coverage, and testing Django REST Framework APIs.

Verification loop for Django projects: migrations, linting, tests with coverage, security scans, and deployment readiness checks.

Docker and Docker Compose patterns for Python development, container security, networking, and multi-service orchestration.

FastAPI patterns for building production-grade APIs — routing, Pydantic models, dependency injection, middleware, async patterns, WebSockets, and background tasks.

PostgreSQL database patterns for query optimization, schema design, indexing, and security.

Pydantic v2 patterns — models, validators, serialization, computed fields, discriminated unions, settings management, and integration with FastAPI/SQLAlchemy.

OOP-based pytest patterns for API test automation — base classes, service layers, fixtures hierarchy, parametrization, markers, parallel execution, and test isolation.

Pythonic idioms, PEP 8 standards, type hints, and best practices for building robust, efficient, and maintainable Python applications.

Python testing strategies using pytest, TDD methodology, fixtures, mocking, parametrization, and coverage requirements.

Redis patterns for Python — caching, sessions, pub/sub, rate limiting, distributed locks, and integration with Django/FastAPI.

SQLAlchemy 2.0 patterns — ORM models, async sessions, relationships, queries, Alembic migrations, repository pattern, and performance optimization.