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Found 2,123 Skills
Python 开发规范，包含 PEP 8 风格、类型注解、异常处理、测试规范等
Automatically generate complete Python project deliverables from natural language requirements through collaboration among four virtual roles: autonomous learning, PM, architect, and senior programmer. Supports feature expansion, project refactoring, and skill invocation. Also supports web search, knowledge integration, version control, Python 3.11+ features, UV package management, loguru logging, and project size adaptation (folder/single file). It provides support for database design and implementation (SQLite, PostgreSQL, MongoDB, vector databases, graph databases), data layer abstraction (Repository pattern), and database switching. Suitable for scenarios such as software requirement clarification, rapid prototyping, project initialization, feature expansion, and code refactoring.
This skill should be used when the user asks to "build a Flet app", "create a Python GUI", "use Flet framework", "write a Flet control", or needs guidance on cross-platform Python UI development with Flet.
Manage call recordings, media storage, Dialogflow integration, and external connections for SIP trunking. This skill provides Python SDK examples.
Initialize Python Project (New or Fork). Use when the user wants to create a new production-ready Python/ML project structure, or fork and enhance an existing project. Uses uv for environment management.
Generates production-ready Python code following PEP standards, type hints, and clean code principles. Use when user requests Python implementation of classes, functions, services, or modules. Triggers on "implement", "write", "Python code", "create class", "create function", "code for".
Design structured logging systems with context propagation. Use to ensure Python applications are observable and logs are machine-readable.
Activate when creating new modules, refactoring class hierarchies, introducing design patterns, or making changes spanning 3+ files in the APM CLI codebase.
Use when designing module boundaries, planning refactors, or reviewing architecture in Python codebases. Also use when facing tangled dependencies, god classes, deep inheritance hierarchies, unclear ownership, or risky structural changes.
Debug Python errors, exceptions, and unexpected behavior. Analyzes tracebacks, reproduces issues, identifies root causes, and provides fixes.
Modern Python tooling best practices using uv, ruff, ty, and pytest. Mandates the Trail of Bits Python coding standards for project setup, dependency management, linting, type checking, and testing. Based on patterns from trailofbits/cookiecutter-python.
Python coding standards with automatic version detection. Use when writing, reviewing, or refactoring Python to ensure adherence to LBYL exception handling patterns, modern type syntax (list[str], str | None), pathlib operations, ABC-based interfaces, absolute imports, and explicit error boundaries at CLI level. Also provides production-tested code smell patterns from Dagster Labs for API design, parameter complexity, and code organization. Essential for maintaining erk's dignified Python standards.