sqlite-best-practices
SQLite best practices including WAL checkpoint timing for multiprocessing, stateful batch connections for atomicity, and performance configuration. Use when working with SQLite in Python projects requiring concurrent access, multi-table atomicity, or multiprocessing.
python-linting
Python linting and type checking using ruff (formatting + linting) and pyright (type checking). Covers common errors, configuration, fixing violations, and when to use noqa. Use when fixing linting errors, configuring ruff/pyright, or understanding Python code quality tools.
performance-profiling
Python performance profiling with cProfile, line_profiler, memory_profiler, and py-spy. Covers profiling workflows, interpreting results, finding bottlenecks, memory leak detection, and optimization strategies. Use when code is slow, debugging performance issues, optimizing for production, or investigating memory usage.
Python Style Standards
Python coding standards including line length (80 chars), naming conventions (snake_case, PascalCase), type hints, docstrings, exception handling, and logging patterns. Use when writing new Python code or reviewing code quality.
async-python
Python async/await patterns with asyncio, concurrent.futures, threading, and multiprocessing. Covers async context managers, timeouts, cancellation, common pitfalls (blocking in async, missing await, event loop issues), and choosing between async/threading/multiprocessing. Use when writing async code, debugging async issues, choosing concurrency approaches, or testing async functions.
python-packaging
Python package management with poetry, pip, and uv including dependency resolution, lock files, version pinning, security audits (pip-audit, safety), virtual environments, and dependency graphs. Use when managing dependencies, resolving version conflicts, setting up new projects, or investigating security vulnerabilities in packages.
python
Always use this skill when writing or editing python code!
jupytext
This skill should be used when the user asks to "convert notebook to text", "use jupytext", "version control notebooks", "share data between kernels", "set up multi-kernel project", "pair notebooks with Python files", "sync ipynb and py files", or needs multi-kernel projects (Python/R/Stata/SAS) with version-control-friendly notebooks.
marimo
This skill should be used when the user asks to "use marimo", "create a marimo notebook", "debug a marimo notebook", "inspect cells", "understand reactive execution", "fix marimo errors", "convert from jupyter to marimo", or works with marimo reactive Python notebooks.
code-conventions
코드 컨벤션, 코딩 스타일, 코드 스타일, 네이밍, 컨벤션 - Always apply when writing code. Code style, naming rules, function/file size limits for TypeScript, Python, and Java.
sentry-setup-metrics
Setup Sentry Metrics in any project. Use this when asked to add Sentry metrics, track custom metrics, setup counters/gauges/distributions, or instrument application performance metrics. Supports JavaScript, TypeScript, Python, React, Next.js, and Node.js.
sentry-setup-logging
Setup Sentry Logging in any project. Use this when asked to add Sentry logs, enable structured logging, setup console log capture, or integrate logging with Sentry. Supports JavaScript, TypeScript, Python, Ruby, React, Next.js, and other frameworks.
telegram-bot-performance-engineer
This skill should be used when analyzing, profiling, or optimizing Telegram bots built with Python/Telethon, especially for performance bottlenecks, rate limits, caching, API usage, or when the user asks for a full repo review or best practices.
uv-package-manager
Master the uv package manager for fast Python dependency management, virtual environments, and modern Python project workflows. Use when setting up Python projects, managing dependencies, or optimizing Python development workflows with uv.
python-testing-patterns
Implement comprehensive testing strategies with pytest, fixtures, mocking, and test-driven development. Use when writing Python tests, setting up test suites, or implementing testing best practices.
python-performance-optimization
Profile and optimize Python code using cProfile, memory profilers, and performance best practices. Use when debugging slow Python code, optimizing bottlenecks, or improving application performance.
python-packaging
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.
async-python-patterns
Master Python asyncio, concurrent programming, and async/await patterns for high-performance applications. Use when building async APIs, concurrent systems, or I/O-bound applications requiring non-blocking operations.
Page 1 of 15 · 267 results