python-standards

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Python coding standards for Amplifier including type hints, async patterns, error handling, and formatting. Use when writing Python code for Amplifier modules.

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When & Why to Use This Skill

This Claude skill provides a comprehensive set of Python coding standards specifically optimized for Amplifier modules. It focuses on modern Python best practices including strict type hinting, efficient asynchronous patterns, and standardized error handling. By implementing these guidelines, developers can ensure high code quality, maintainability, and seamless integration within the Amplifier ecosystem, leveraging tools like ruff for formatting and uv for dependency management.

Use Cases

  • Standardizing Python codebases for Amplifier modules to ensure consistency across different development teams.
  • Implementing type-safe function signatures and class definitions to minimize runtime errors and improve IDE autocompletion.
  • Refactoring legacy synchronous I/O operations into high-performance asynchronous patterns using asyncio and aiohttp.
  • Establishing a uniform error handling protocol using ToolResult to provide the AI agent with clear and actionable feedback.
  • Setting up automated testing and linting workflows to maintain high-quality code standards before deployment.
namepython-standards
descriptionPython coding standards for Amplifier including type hints, async patterns, error handling, and formatting. Use when writing Python code for Amplifier modules.
version1.0.0
licenseMIT
categorydevelopment
complexitylow

Python Coding Standards

Type Hints

ALL functions must have complete type hints:

from typing import Any

async def process_data(items: list[str], config: dict[str, Any]) -> dict[str, Any]:
    """Process data items with configuration."""
    results = {}
    for item in items:
        results[item] = await transform(item, config)
    return results

Include type hints for self:

class MyClass:
    def __init__(self: "MyClass", name: str) -> None:
        self.name = name

    async def process(self: "MyClass") -> str:
        return f"Processing {self.name}"

Async Patterns

All I/O operations must be async:

# Good
async def read_file(path: Path) -> str:
    content = path.read_text()  # For now, sync is OK
    return content

# Better (when using async libraries)
async def read_file(path: Path) -> str:
    async with aiofiles.open(path) as f:
        return await f.read()

Use asyncio.gather for parallel operations:

async def process_files(files: list[Path]) -> list[dict]:
    tasks = [process_file(f) for f in files]
    return await asyncio.gather(*tasks)

Error Handling

Return errors, don't raise:

from amplifier_core import ToolResult

async def execute(self: "MyTool", input: dict[str, Any]) -> ToolResult:
    """Execute tool operation."""
    try:
        result = await self._process(input)
        return ToolResult(success=True, output=result)
    except ValueError as e:
        logger.error(f"Validation error: {e}")
        return ToolResult(success=False, error={"message": str(e)})

Provide clear error messages:

# Good
return ToolResult(
    success=False,
    error={"message": f"File not found: {path}"}
)

# Bad
return ToolResult(
    success=False,
    error={"message": "Error"}
)

Formatting

Line length: 120 characters

Import organization:

# Standard library
import asyncio
import logging
from pathlib import Path
from typing import Any

# Third-party
import yaml
from pydantic import BaseModel

# Local/Amplifier
from amplifier_core import ModuleCoordinator, ToolResult

Files must end with newline - Add blank line at EOF

Use ruff for formatting:

uv run ruff format .
uv run ruff check . --fix

Dependencies

Use uv for dependency management:

# Add dependency
cd amplifier-module-tool-mytool
uv add package-name

# Add dev dependency
uv add --dev pytest ruff pyright

Never manually edit pyproject.toml dependencies - Use uv add

Testing

Test behavior at protocol level:

import pytest
from amplifier_core.testing import TestCoordinator

@pytest.mark.asyncio
async def test_tool_basic():
    """Test basic tool functionality."""
    coordinator = TestCoordinator()

    # Mount module
    await mount(coordinator, {"timeout": 10})

    # Get and test
    tool = coordinator.get("tools", "my-tool")
    result = await tool.execute({"param": "value"})

    assert result.success
    assert "expected" in result.output

Test pyramid: 60% unit, 30% integration, 10% end-to-end

Common Pitfalls

Don't use blocking I/O

# Bad
content = requests.get(url).text

# Good
async with aiohttp.ClientSession() as session:
    async with session.get(url) as response:
        content = await response.text()

Don't modify module internals from outside

# Bad
tool._internal_state = new_value

# Good
await tool.execute({"operation": "update", "value": new_value})

Don't put logic in mount()

# Bad
async def mount(coordinator, config):
    tool = MyTool()
    await tool.initialize_database()  # Heavy logic
    await coordinator.mount("tools", tool)

# Good
async def mount(coordinator, config):
    tool = MyTool(config)  # Light initialization only
    await coordinator.mount("tools", tool)
    # Heavy logic happens in execute(), not mount()

Remember

  • Run make check before committing
  • Keep modules focused and simple
  • Document public interfaces
  • Test at the protocol level