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Found 2,394 Skills
When dealing with python code, these guidelines must always be followed.
Python testing with pytest, coverage, fixtures, parametrization, and mocking. Covers test organization, conftest.py, markers, async testing, and TDD workflows. Use when user mentions pytest, unit tests, test coverage, fixtures, mocking, or writing Python tests.
Optimizes Python library performance through profiling (cProfile, PyInstrument), memory analysis (memray, tracemalloc), benchmarking (pytest-benchmark), and optimization strategies. Use when analyzing performance bottlenecks, finding memory leaks, or setting up performance regression testing.
Comprehensive Python engineering guidelines for writing production-quality Python code. This skill should be used when writing Python code, performing Python code reviews, working with Python tools (uv, ruff, mypy, pytest), or answering questions about Python best practices and patterns. Applies to CLI tools, AI agents (langgraph), and general Python development.
Style, review, and refactoring standards for Python codebases with strong typing, explicit error handling, and maintainable module boundaries. Use when Python artifacts are created, changed, or reviewed and Python-specific quality rules must be enforced.
Python toolkit for computational biology. Use when asked to "parse FASTA", "read GenBank", "query NCBI", "run BLAST", "analyze protein structure", "build phylogenetic tree", or work with biological sequences. Handles sequence I/O, database access, alignments, structure analysis, and phylogenetics.
Эксперт Python разработки. Используй для Python best practices, async, typing и ecosystem.
This skill should be used when the user asks to "connect to MySQL with PyMySQL", "use PyMySQL in Python", "query a MySQL database with Python", "set up PyMySQL", or needs guidance on PyMySQL best practices, transactions, parameterized queries, or cursor types.
Setup and initialization guide for developing AWS CDK (Cloud Development Kit) applications in Python. This skill enables users to configure environment prerequisites, create new CDK projects, manage dependencies, and deploy to AWS.
Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides Python SDK examples.
Python backend for Databricks Apps — FastAPI (default), Flask, Dash, Streamlit, Gradio, Reflex. **Default for a new Databricks App is `databricks-apps` (AppKit — Node/TypeScript/React) — reach for it first.** Use this skill only when the user asks for a Python backend, extends an existing Python app, or the team is Python-only. Covers OAuth auth, app resources, SQL warehouse and Lakebase connectivity, foundation-model / Vector Search / model-serving APIs (via `databricks-python-sdk`), and deployment via CLI or DABs.
Use before writing or editing any Python code — scripts, functions, bug fixes, cron jobs, data-processing one-offs, or additions to an existing .py file — even if the request sounds quick, casual, or "just a simple script." Applies whether the user names a .py file, describes what the script should do, or asks you to add/fix a function in Python. Covers required standards: type hints, no global variables, logging instead of print, wrapping any I/O (HTTP, DB, subprocess, file) in try/except, commenting the why not the what, writing a real test, and where to place design docs. Does not apply to conceptual Python questions with no code to write (e.g. explaining language features, comparing list vs tuple, explaining what an existing traceback means when the user hasn't asked for a fix, package install help, or learning-resource recommendations), and does not apply to non-Python languages.