بنقرة واحدة
python-best-practices
Use when reading or writing Python files (.py, pyproject.toml, requirements.txt).
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
القائمة
Use when reading or writing Python files (.py, pyproject.toml, requirements.txt).
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Use when the user says they are stepping away and the agent should continue without interactive approvals
Use when running e2e tests, debugging test failures, or fixing flaky tests. Covers failure taxonomy, fix rules, and workflow. Never changes source code logic or API without spec backing.
Use when auditing how well the shared agent instructions (AGENTS.md, skills) hold up in real sessions — sampling transcripts via recall, scoring them against the gap rubric, and turning findings into ratified amendments
Use when preparing clean, logical git commits from an existing working tree
Use when syncing a feature branch onto the latest origin base branch via git rebase.
Fetch latest from origin, prune remote-tracking refs, delete stale local branches and worktrees, and fast-forward important branches. Use when tidying up a worktree-based repo layout.
| name | python-best-practices |
| description | Use when reading or writing Python files (.py, pyproject.toml, requirements.txt). |
Follows type-first, functional, and error handling patterns from AGENTS.md. This skill covers language-specific idioms only.
Use Python's type system to prevent invalid states at type-check time.
Frozen dataclasses for immutable domain models:
from dataclasses import dataclass
from datetime import datetime
@dataclass(frozen=True)
class User:
id: str
email: str
name: str
created_at: datetime
# Frozen dataclasses are immutable — no accidental mutation
Discriminated unions with Literal:
from dataclasses import dataclass
from typing import Literal
@dataclass
class Success:
status: Literal["success"] = "success"
data: str
@dataclass
class Failure:
status: Literal["error"] = "error"
error: Exception
RequestState = Success | Failure
def handle_state(state: RequestState) -> None:
match state:
case Success(data=data):
render(data)
case Failure(error=err):
show_error(err)
NewType for domain primitives:
from typing import NewType
UserId = NewType("UserId", str)
OrderId = NewType("OrderId", str)
def get_user(user_id: UserId) -> User:
# Type checker prevents passing OrderId here
...
Protocol for structural typing:
from typing import Protocol
class Readable(Protocol):
def read(self, n: int = -1) -> bytes: ...
def process_input(source: Readable) -> bytes:
# Accepts any object with a read() method — no inheritance required
return source.read()
Chain exceptions with from err to preserve the original traceback:
try:
data = json.loads(raw)
except json.JSONDecodeError as err:
raise ValueError(f"invalid JSON payload: {err}") from err
Use a module-level logger with %s formatting (deferred string interpolation):
import logging
logger = logging.getLogger("myapp.widgets")
def create_widget(name: str) -> Widget:
logger.debug("creating widget: %s", name)
widget = Widget(name=name)
logger.debug("created widget id=%s", widget.id)
return widget
For fast type checking, consider ty from Astral (creators of ruff and uv). Written in Rust, significantly faster than mypy or pyright.
uvx ty check # run directly, no install needed
uvx ty check src/ # check specific path
# pyproject.toml
[tool.ty]
python-version = "3.12"
When to choose:
ty — fastest, good for CI and large codebases (early stage, rapidly evolving)pyright — most complete type inference, VS Code integrationmypy — mature, extensive plugin ecosystem