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Found 2,101 Skills
testcontainers-python specialist. Covers all container modules (PostgreSQL, MySQL, MongoDB, Redis, Kafka, RabbitMQ, MinIO, Elasticsearch, LocalStack), GenericContainer, wait strategies, Docker Compose, networks, pytest fixtures, and CI/CD integration. USE WHEN: user mentions "testcontainers", "docker in tests", "real database in tests", "test with real postgres/redis/kafka", asks about container fixtures or Docker-based testing. DO NOT USE FOR: Spring Boot testcontainers (Java) - use `spring-boot-integration`; Mocking HTTP - use `fastapi-testing`; Pure pytest patterns - use `pytest`
Tinybird Python SDK for defining datasources, pipes, and queries in Python. Use when working with tinybird-sdk, Python Tinybird projects, or data ingestion and queries in Python.
cuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.
Write, edit, refactor, or review Python in easy-cheese with concise stdlib-first code, Python 3.12, self-contained .pyz packaging, and repository test and validation conventions. Use for Python changes under src/, shared/scripts/, scripts/, .github/scripts/, or tests/, especially when the user asks for Pythonic, succinct, de-slopped, dataclass-based, CLI, validator, or bundled-helper code.
Write Python code in n8n Code nodes. Use when writing Python in n8n, using _input/_json/_node syntax, working with standard library, or need to understand Python limitations in n8n Code nodes.
Chunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
Master Python 3.12+ with modern features, async programming, performance optimization, and production-ready practices. Expert in the latest Python ecosystem including uv, ruff, pydantic, and FastAPI. Use PROACTIVELY for Python development, optimization, or advanced Python patterns.
Quick reference mapping global architecture concepts to Python/FastAPI/SQLAlchemy syntax. For concepts, see the global skills.
Python scripting with uv and PEP 723 inline dependencies. Use when creating standalone Python scripts with automatic dependency management.
Enforce Pythonic standards using Black, Isort, and Flake8. Use to ensure consistency across large Python codebases and team environments.
Python single-file script development using uv and PEP 723 inline metadata. Prevents invalid patterns like [tool.uv.metadata]. Use when creating standalone Python utilities, converting scripts to uv format, managing script dependencies, implementing script testing, or establishing team standards for script development.
Generate production-ready Python code using Dataverse SDK with error handling, optimization, and best practices