Skill v1.0.0

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gradion-ai/freeact/saving-codeacts

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PublishedApril 27, 2026 at 10:25 PM

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version: "1.0.0" 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

{generated_rel_dir}/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 {generated_rel_dir}/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 {generated_rel_dir}/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

All versions

Skill v1.0.0

Saving Code Actions as Reusable Tools

Package Structure

Procedure

1. Create Package Directory

2. Define Tool API (api.py)

3. Implement Details (impl.py)

4. Test the Tool

Best Practices

2. Define Tool API (`api.py`)

3. Implement Details (`impl.py`)