python-pro
Use when building Python 3.11+ applications requiring type safety, async programming, or robust error handling. Generates type-annotated Python code, configures mypy in strict mode, writes pytest test suites with fixtures and mocking, and validates code with black and ruff. Invoke for type hints, async/await patterns, dataclasses, dependency injection, logging configuration, and structured error handling.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/jeffallan/claude-skills --skill python-pro- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads python-pro automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
Python Pro
Modern Python 3.11+ specialist focused on type-safe, async-first, production-ready code.
When to Use This Skill
- Writing type-safe Python with complete type coverage
- Implementing async/await patterns for I/O operations
- Setting up pytest test suites with fixtures and mocking
- Creating Pythonic code with comprehensions, generators, context managers
- Building packages with Poetry and proper project structure
- Performance optimization and profiling
Core Workflow
- Analyze codebase — Review structure, dependencies, type coverage, test suite
- Design interfaces — Define protocols, dataclasses, type aliases
- Implement — Write Pythonic code with full type hints and error handling
- Test — Create comprehensive pytest suite with >90% coverage
- Validate — Run
mypy --strict,black,ruff- If mypy fails: fix type errors reported and re-run before proceeding
- If tests fail: debug assertions, update fixtures, and iterate until green
- If ruff/black reports issues: apply auto-fixes, then re-validate
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Type System | references/type-system.md | Type hints, mypy, generics, Protocol |
| Async Patterns | references/async-patterns.md | async/await, asyncio, task groups |
| Standard Library | references/standard-library.md | pathlib, dataclasses, functools, itertools |
| Testing | references/testing.md | pytest, fixtures, mocking, parametrize |
| Packaging | references/packaging.md | poetry, pip, pyproject.toml, distribution |
Constraints
MUST DO
- Type hints for all function signatures and class attributes
- PEP 8 compliance with black formatting
- Comprehensive docstrings (Google style)
- Test coverage exceeding 90% with pytest
- Use
X | Noneinstead ofOptional[X](Python 3.10+) - Async/await for I/O-bound operations
- Dataclasses over manual init methods
- Context managers for resource handling
MUST NOT DO
- Skip type annotations on public APIs
- Use mutable default arguments
- Mix sync and async code improperly
- Ignore mypy errors in strict mode
- Use bare except clauses
- Hardcode secrets or configuration
- Use deprecated stdlib modules (use pathlib not os.path)
Code Examples
Type-annotated function with error handling
from pathlib import Path
def read_config(path: Path) -> dict[str, str]:
"""Read configuration from a file.
Args:
path: Path to the configuration file.
Returns:
Parsed key-value configuration entries.
Raises:
FileNotFoundError: If the config file does not exist.
ValueError: If a line cannot be parsed.
"""
config: dict[str, str] = {}
with path.open() as f:
for line in f:
key, _, value = line.partition("=")
if not key.strip():
raise ValueError(f"Invalid config line: {line!r}")
config[key.strip()] = value.strip()
return config
Dataclass with validation
from dataclasses import dataclass, field
@dataclass
class AppConfig:
host: str
port: int
debug: bool = False
allowed_origins: list[str] = field(default_factory=list)
def __post_init__(self) -> None:
if not (1 <= self.port <= 65535):
raise ValueError(f"Invalid port: {self.port}")
Async pattern
import asyncio
import httpx
async def fetch_all(urls: list[str]) -> list[bytes]:
"""Fetch multiple URLs concurrently."""
async with httpx.AsyncClient() as client:
tasks = [client.get(url) for url in urls]
responses = await asyncio.gather(*tasks)
return [r.content for r in responses]
pytest fixture and parametrize
import pytest
from pathlib import Path
@pytest.fixture
def config_file(tmp_path: Path) -> Path:
cfg = tmp_path / "config.txt"
cfg.write_text("host=localhost\nport=8080\n")
return cfg
@pytest.mark.parametrize("port,valid", [(8080, True), (0, False), (99999, False)])
def test_app_config_port_validation(port: int, valid: bool) -> None:
if valid:
AppConfig(host="localhost", port=port)
else:
with pytest.raises(ValueError):
AppConfig(host="localhost", port=port)
mypy strict configuration (pyproject.toml)
[tool.mypy]
python_version = "3.11"
strict = true
warn_return_any = true
warn_unused_configs = true
disallow_untyped_defs = true
Clean mypy --strict output looks like:
Success: no issues found in 12 source files
Any reported error (e.g., error: Function is missing a return type annotation) must be resolved before the implementation is considered complete.
Output Templates
When implementing Python features, provide:
- Module file with complete type hints
- Test file with pytest fixtures
- Type checking confirmation (mypy --strict passes)
- Brief explanation of Pythonic patterns used
Knowledge Reference
Python 3.11+, typing module, mypy, pytest, black, ruff, dataclasses, async/await, asyncio, pathlib, functools, itertools, Poetry, Pydantic, contextlib, collections.abc, Protocol
Maintained by @jeffallan, Principal Consultant at Synergetic Solutions
FAQ
What does the python-pro skill do?
Use when building Python 3.11+ applications requiring type safety, async programming, or robust error handling. Generates type-annotated Python code, configures mypy in strict mode, writes pytest test suites with fixtures and mocking, and validates code with black and ruff. Invoke for type hints, async/await patterns, dataclasses, dependency injection, logging configuration, and structured error handling.
How do I install python-pro?
Run npx skills add https://github.com/jeffallan/claude-skills --skill python-pro in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/python-pro/ (personal) or .claude/skills/python-pro/ (project).
Which AI agents can use python-pro?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is python-pro safe to install?
Security partners have reviewed it: Gen Agent Trust Hub flagged risks; Socket passed; Snyk passed; Runlayer passed; ZeroLeaks passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains python-pro?
It is published by jeffallan in jeffallan/claude-skills. It has 8,813 recorded installs.
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