Python coding standards for Amplifier including type hints, async patterns, error handling, and formatting. Use when writing Python code for Amplifier modules.
复制安装指令,让 AI 自动完成配置 · 推荐新手
请帮我安装 askskill 上的 "python-standards" 技能: 1. 下载 https://raw.githubusercontent.com/microsoft/amplifier-bundle-skills/main/modules/tool-skills/tests/fixtures/skills/python-standards/SKILL.md 2. 保存为 ~/.claude/skills/python-standards/SKILL.md 3. 装好后重载技能,告诉我可以用了
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}"
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)
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"}
)
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
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
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
# Bad
content = requests.get(url).text
# Good
async with aiohttp.ClientSession() as session:
async with session.get(url) as response:
content = await response.text()
# Bad
tool._internal_state = new_value
# Good
await tool.execute({"operation": "update", "value": new_value})
# 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()
…
Guide for creating new Amplifier modules including protocol implementation, entry points, mount functions, and testing patterns. Use when creating new modules or understanding module architecture.
Adapt a skill written for another AI coding assistant (Claude Code, Cursor, etc.) into a properly structured Amplifier SKILL.md file. Reads the source skill, identifies platform-specific conventions, researches the source platform if needed, and produces an Amplifier-native skill conforming to the Agent Skills specification with Amplifier extensions. Use when the user wants to adapt a skill, port a skill, convert a skill to amplifier, translate a skill, or has a SKILL.md from another platform they want to bring into Amplifier.
Use when your service needs authentication that works without friction locally but secures remote access, automatic TLS certificate setup, or token-based auth with auto-generation and localhost bypass.
Use when building a new CLI tool that needs one-line install via uv or npm, subcommand dispatch with a default action, or 3-tier config resolution (CLI flags, config file, hardcoded defaults).
Review changed code for reuse, quality, and efficiency, then fix any issues found.
Amplifier design philosophy using Linux kernel metaphor. Covers mechanism vs policy, module architecture, event-driven design, and kernel principles. Use when designing new modules or making architectural decisions.