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Found 2,120 Skills
Comprehensive pytest testing skill for Python projects. Write efficient, maintainable tests with fixtures, parametrization, markers, mocking, and assertions. Use when: (1) Writing new tests for Python code, (2) Setting up pytest in a project, (3) Creating fixtures for test dependencies, (4) Parametrizing tests for multiple inputs, (5) Mocking/patching with monkeypatch, (6) Debugging test failures, (7) Organizing test suites with markers, (8) Any Python testing task.
Migrate a Python application from the classic Elastic APM Python agent to the EDOT Python agent. Use when switching from elastic-apm to elastic-opentelemetry.
Create distributable Python packages with proper project structure, setup.py/pyproject.toml, and publishing to PyPI. Use when packaging Python libraries, creating CLI tools, or distributing Python code.
Use when building Python 3.11+ applications requiring type safety, async programming, or production-grade patterns. Invoke for type hints, pytest, async/await, dataclasses, mypy configuration.
Python background job patterns including task queues, workers, and event-driven architecture. Use when implementing async task processing, job queues, long-running operations, or decoupling work from request/response cycles.
Configures Python projects with modern tooling (uv, ruff, ty). Use when creating projects, writing standalone scripts, or migrating from pip/Poetry/mypy/black.
Senior Python developer expertise for writing clean, efficient, and well-documented code. Use when: writing Python code, optimizing Python scripts, reviewing Python code for best practices, debugging Python issues, implementing type hints, or when user mentions Python, PEP 8, or needs help with Python data structures and algorithms.
Complete reference for the Portkey AI Gateway Python SDK with unified API access to 200+ LLMs, automatic fallbacks, caching, and full observability. Use when building Python applications that need LLM integration with production-grade reliability.
Expert in Python testing with pytest and test-driven development
Use when writing or reviewing tests for Python behavior, contracts, async lifecycles, or reliability paths. Also use when tests are flaky, coupled to implementation details, missing regression coverage, slow to run, or when unclear what tests a change needs.
Observability patterns for Python applications. Triggers on: logging, metrics, tracing, opentelemetry, prometheus, observability, monitoring, structlog, correlation id.
Consult this skill for async Python patterns and concurrency. Use when building async APIs, concurrent systems, I/O-bound applications, implementing rate limiting, async context managers. Do not use when CPU-bound optimization - use python-performance instead. DO NOT use when: testing async code - use python-testing async module.