python-standards
Python coding standards for Amplifier including type hints, async patterns, error handling, and formatting. Use when writing Python code for Amplifier modules.
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.
| name | python-standards |
|---|---|
| description | Python coding standards for Amplifier including type hints, async patterns, error handling, and formatting. Use when writing Python code for Amplifier modules. |
| version | 1.0.0 |
| license | MIT |
| category | development |
| complexity | low |
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 checkbefore committing - Keep modules focused and simple
- Document public interfaces
- Test at the protocol level