Loading...
Loading...
Found 1,740 Skills
Appwrite Python SDK skill. Use when building server-side Python applications with Appwrite, including Django, Flask, and FastAPI integrations. Covers user management, database/table CRUD, file storage, and functions via API keys.
Systematic resolution of pyright/mypy type errors with categorization and fix templates. Use when pyright fails, type errors are reported, adding type annotations, or enforcing type safety. Analyzes Python type errors, categorizes them (missing annotations, incorrect types, generic issues, Optional/None handling), and applies fix patterns. Works with .py files and pyright output.
Design comprehensive Python test suites including unit, integration, and E2E tests. Use when establishing testing patterns for new or existing Python applications.
Bootstrap Python MCP server projects and workspaces on macOS using uv and FastMCP with consistent defaults. Use when creating a new MCP server from scratch, scaffolding a single uv MCP project, scaffolding a uv workspace with package/service members, initializing pytest+ruff+mypy defaults, creating README.md, initializing git, running initial validation checks, or starting from OpenAPI/FastAPI with MCP mapping guidance.
Comprehensive Python expertise covering language fundamentals, idiomatic patterns, software design principles, and production best practices. Use when writing, reviewing, debugging, or refactoring Python code. Triggers: Python, .py files, pip, uv, pytest, dataclasses, asyncio, type hints, or any Python library.
在 Python 项目中默认使用 uv 替代 pip 进行依赖管理和虚拟环境创建，提升 10-100x 安装速度。当处理 Python 项目、创建虚拟环境、安装依赖、或用户提到 pip/venv/virtualenv 时使用。
Upgrade Python dependencies using uv, then run post-upgrade checks to ensure nothing is broken.
Comprehensive Python programming guidelines based on Google's Python Style Guide. Use when you needs to write Python code, review Python code for style issues, refactor Python code, or provide Python programming guidance. Covers language rules (imports, exceptions, type annotations), style rules (naming conventions, formatting, docstrings), and best practices for clean, maintainable Python code.
Analyze Python code for style improvements including naming, structure, nesting, and cognitive load reduction
Python data analysis with pandas, numpy, and analytics libraries
pytest testing patterns for Python. Triggers on: pytest, fixture, mark, parametrize, mock, conftest, test coverage, unit test, integration test, pytest.raises.
Write and audit Python code comments using antirez's 9-type taxonomy. Two modes - write (add/improve comments in code) and audit (classify and assess existing comments with structured report). Use when users request comment improvements, docstring additions, comment quality reviews, or documentation audits. Applies systematic comment classification with Python-specific mapping (docstrings, inline comments, type hints).