AgentSkillsCN

saving-codeacts

将已执行的Python代码保存为gentools软件包中的可复用工具。当您希望长期保留成功的代码执行记录，以便后续重复使用时，可选用此方法。该流程包括创建软件包结构（api.py、impl.py）、定义Pydantic输出模型，以及实现run()接口。

SKILL.md
--- frontmatter
name: saving-codeacts
description: Save executed Python code as reusable tools in the gentools package. Use when preserving successful code executions for later reuse. Covers creating package structure (api.py, impl.py), defining Pydantic output models, and implementing the run() interface.

Saving Code Actions as Reusable Tools

Save executed Python code as a tool for later reuse.

Package Structure

code
gentools/<category>/<tool>/
├── __init__.py          # Empty file
├── api.py              # Public interface with structured models
└── impl.py             # Implementation details

Procedure

1. Create Package Directory

bash
mkdir -p gentools/<category>/<tool>

Create empty __init__.py files in both <category> and <tool> directories.

2. Define Tool API (api.py)

python
from __future__ import annotations

from pydantic import BaseModel, Field


class OutputModel(BaseModel):
    """Description of output."""
    field: type = Field(..., title="Description")


def run(param1: type, param2: type = default) -> OutputModel:
    """Tool description.

    Args:
        param1: Description
        param2: Description (default: value)

    Returns:
        OutputModel with structured data
    """
    from .impl import implementation_function
    return implementation_function(param1, param2)

Requirements:

  • •Define Pydantic models for structured output
  • •Create run() function with typed parameters
  • •Use lazy import from impl.py inside run()
  • •Include comprehensive docstring
  • •Export OutputModel and run in gentools/<category>/<tool>/__init__.py:
python
from .api import OutputModel, run

__all__ = ["OutputModel", "run"]

3. Implement Details (impl.py)

python
from __future__ import annotations

from mcptools.<category>.<tool> import Params, run_parsed
from .api import OutputModel


def implementation_function(param1: type, param2: type) -> OutputModel:
    """Implementation description."""
    # Use tools from mcptools or gentools packages
    result = run_parsed(Params(...))

    # Transform and return structured output
    return OutputModel(field=result.data)

Requirements:

  • •Import tools from mcptools or gentools packages
  • •Import models from api.py
  • •Return structured models defined in api.py

4. Test the Tool

python
from gentools.<category>.<tool>.api import run

result = run(param1=value1, param2=value2)
print(result)

Best Practices

  • •Separation: Keep API clean; hide complexity in implementation
  • •Type Safety: Use Pydantic models for all outputs
  • •Modularity: Break complex logic into smaller functions
  • •Defaults: Provide sensible defaults for optional parameters