AgentSkillsCN

python-pro

编写符合规范的 Python 代码，使用装饰器、生成器和 async/await 等高级特性。优化性能、实现设计模式并确保全面测试。用于机器学习训练、分析工具、性能剖析或任何 Python 重度任务。

SKILL.md
--- frontmatter
name: python-pro
description: Write idiomatic Python code with advanced features like decorators, generators, and async/await. Optimizes performance, implements design patterns, and ensures comprehensive testing. Use for ML training, analytics tools, performance profiling, or any Python heavy lifting.
metadata:
  short-description: Advanced Python patterns

Python Pro - Advanced Python Patterns

When to Use This Skill

Invoke python-pro for:

  • •Model training/evaluation (e.g., ml/train_models.py)
  • •Analytics tool optimization (async batching, caching)
  • •Performance profiling (bottleneck identification)
  • •Advanced Python patterns (decorators, generators, context managers)
  • •Heavy data processing on large datasets
  • •Executing ML designs from @ml-owner

Use fastapi-production-patterns instead for:

  • •API endpoints, routing, middleware
  • •Pydantic validation, request/response models
  • •CORS configuration, authentication middleware
  • •FastAPI-specific patterns (dependency injection at API layer)

Clear Boundary:

fastapi-production-patternspython-pro
API layer (HTTP, routing)Business logic (ML, analytics)
Pydantic, middleware, CORSDecorators, generators, profiling
FastAPI endpointsCore Python optimization

Executable Scripts

Run these scripts directly for profiling and debugging:

Profile a Function

bash
python scripts/profile_function.py <module.path> <function_name>
python scripts/profile_function.py <module.path> <function_name> --args '{"key": "value"}'

Compare Two Implementations

bash
python scripts/benchmark_compare.py <module_a:func> <module_b:func> --runs 10

Check Memory Usage

bash
python scripts/memory_check.py <module.path> <function_name> --args '{"n_rows": 1000}'

Project ML System (Fill In)

Use references/project_ml.md for ML-specific documentation:

  • •Model locations (fill in)
  • •Training commands (fill in)
  • •Feature list (fill in)
  • •How predictions are served (fill in)
  • •Database tables used for training (fill in)

Core Patterns and Examples

Use references/patterns.md for detailed code patterns and examples across:

  • •Decorators (caching, timing, retries, validation)
  • •Generators (lazy feature building, chunking, async generators)
  • •Async/concurrency (batching, sync-to-async, semaphores)
  • •Profiling (cProfile, line_profiler, memory_profiler, benchmarking)
  • •Type hints and static analysis (TypedDict, Protocol, Generic, mypy/ruff/black)
  • •Testing (fixtures, parametrization, async tests, mocking)
  • •Design patterns (strategy/factory for ML and tool creation)
  • •Quick reference cheat sheet

Usage Guidance

  • •Prefer clear, typed interfaces for analytics and ML modules.
  • •Favor async batching when tool calls are independent.
  • •Profile before optimizing; keep hotspots visible.

Scripts

  • •scripts/skill_info.py: Print skill name and description.