Python

Modern Python at the language level

Write, review, modernize, type, and package Python that reads like a typed, flat-control-flow, stdlib-first program whose dependencies and tooling all live in one pyproject.toml. Types are part of the design, not decoration; the stdlib is large and you reach for it before a dependency; correctness is ruff + a type checker + pytest in one gate.

Targets Python 3.12+ (floor) / 3.14 (current, released 7 Oct 2025): PEP 695 inline type parameters (class Box[T]:, type Alias = ...), asyncio.TaskGroup, and — in 3.14 — deferred annotation evaluation by default (PEP 649/749, no more from __future__ import annotations), PEP 750 template strings (t"..."), and compression.zstd. Tooling pins: uv 0.11 (project + package manager), ruff 0.15 (lint + format), mypy 1.20 --strict (or Astral's ty, still preview — default to mypy), pytest 8.

Scope — what this skill delegates

• Building a FastAPI / ASGI service (routes, Pydantic models, SQLAlchemy, uvicorn) -> fastapi. That skill owns the service shape; this one owns the language it is written in.
• A pytest suite as the primary task — fixtures, parametrize matrices, mocking, coverage, property-based testing -> testing-py (this skill keeps only the baseline: a few tests so verify.sh has something to run, then hands off).
• Language-agnostic threat modeling / authz / OWASP review -> secure-coding (this skill keeps Python-specific safety: no eval/pickle of untrusted data, subprocess without shell=True, secrets over random).
• Containerfile / CI pipeline / deploy mechanics -> deployment (this skill ships only a uv-based CI note).
• Another language -> go, typescript, rust, etc. Django ORM/models/migrations -> django.

Python typing, async language semantics, and uv packaging live here, not in a separate skill — this skill is the canonical authority for the language substrate under any Python program.

Decision rules

Apply on every Python edit:

1. Type the boundary. Every public function, method, and module-level name gets an annotation; let inference handle locals. Untyped public API defeats --strict.
2. Flat control flow. Guard-clause and return/raise early; keep the happy path unindented — arrow code hides the logic.
3. Stdlib before a dependency. pathlib, itertools, functools, dataclasses, collections cover most needs; a new dep is a maintenance liability you must justify.
4. Prefer immutability. @dataclass(frozen=True, slots=True) for value objects; mutate only where you must — shared mutable state is the bug you debug at 2am.
5. Everything passes the gate. ruff check + ruff format --check + mypy --strict + pytest — green locally via scripts/verify.sh before you call it done.

Typing

Type the boundary; run mypy --strict so untyped code and implicit Any are errors, not silent gaps. Use PEP 695 inline syntax for all new generic code — no explicit TypeVar objects:

# Good (3.12+): inline type parameter and the `type` alias statement.
def first[T](xs: list[T]) -> T:
    return xs[0]

class Box[T]: ...
type UserId = int                 # `type` statement: a real alias, lazily evaluated
# Bad (legacy): `T = TypeVar("T")` then a Generic[T] — fine in old code, don't write it new.

Core choices: Protocol (structural, no inheritance) over an ABC when you only need "has these methods"; X | None (not Optional[X]); Literal/Enum for closed value sets; Final for constants; Self for fluent returns; @overload for signature families. Narrow with isinstance, assert, or an is None guard — mypy follows the flow. In 3.14 annotations are lazy by default (PEP 649/749), so forward references resolve without from __future__ import annotations.

Full PEP 695 bounds/constraints/variance, Protocol vs ABC, TypedDict Required/NotRequired, ParamSpec/TypeVarTuple, TypeGuard/TypeIs, cast, and common --strict errors with fixes -> references/typing.md.

Data modeling

Pick the carrier by what the data is, not by habit:

from dataclasses import dataclass

@dataclass(frozen=True, slots=True)
class Point:
    x: float
    y: float
    def translated(self, dx: float, dy: float) -> "Point":
        return Point(self.x + dx, self.y + dy)   # returns a new value, never mutates

Frozen-slots dataclass when you want methods + hashability + clear types; NamedTuple when the thing genuinely is a small tuple you also unpack positionally.

Stdlib idioms

Reach into the stdlib before adding a dependency.

• pathlib for all filesystem paths: Path("data") / name, p.read_text(), p.glob("*.json"), p.with_suffix(".bak") — typed and composable, never os.path.join.
• collections: defaultdict(list), Counter(words), deque(maxlen=100) for ring buffers.
• itertools: chain, groupby, islice, batched (3.12+) instead of hand-rolled loops.
• functools: @cache / @lru_cache for pure memoization, @cached_property, partial.
• contextlib: @contextmanager, ExitStack for dynamic resource sets, suppress(FileNotFoundError).
• logging, never print in a library: print writes to a caller's stdout you do not own; logging.getLogger(__name__) lets them configure it.

Prefer comprehensions over map/filter+lambda; prefer a generator ((... for ...)) when you only iterate once. Use match for structural dispatch over a chain of isinstance:

from functools import cache
from pathlib import Path

@cache
def config_dir() -> Path:                 # computed once, memoized
    return Path.home() / ".config" / "myapp"

def area(shape: object) -> float:
    match shape:                          # structural dispatch, captures by attribute/key
        case {"kind": "circle", "r": float(r)}:
            return 3.14159 * r * r
        case _:
            raise TypeError(f"unknown shape: {shape!r}")

f-strings for formatting; note 3.14's PEP 750 t"..." template strings yield a Template (not a str) for safe custom interpolation (e.g. escaping) — use them when an f-string would inject untrusted text. Full cookbook (itertools/functools/collections recipes, match patterns, dataclass field/default_factory/__post_init__, Enum/StrEnum/IntFlag) -> references/stdlib.md.

Errors & resources

Define a small exception hierarchy rooted in one base so callers can catch broadly or narrowly; chain causes with raise ... from; never write a bare except:.

class AppError(Exception): ...
class NotFoundError(AppError): ...

def load(path: Path) -> str:
    try:
        return path.read_text()
    except FileNotFoundError as e:
        raise NotFoundError(f"missing {path}") from e   # preserves the cause chain

Use except* to handle an ExceptionGroup (what a TaskGroup raises) by member type. Prefer EAFP (try the operation, handle the failure) over LBYL race-prone pre-checks. Always release resources with with (a context manager), not manual try/finally close.

Async (language level)

Use asyncio.run(main()) as the single entry point. asyncio.TaskGroup (3.11+) over bare gather — it is the structured-concurrency primitive: a child failure cancels its siblings and surfaces as an ExceptionGroup, and no task outlives the block.

import asyncio

# Bad: gather leaks the other tasks on first failure and loses structure.
async def fetch_all_bad(ids: list[int]) -> list[bytes]:
    return await asyncio.gather(*(fetch(i) for i in ids))

# Good: TaskGroup — sibling cancellation on error, bounded lifetime, real grouping.
async def fetch_all(ids: list[int]) -> list[bytes]:
    async with asyncio.TaskGroup() as tg:
        tasks = [tg.create_task(fetch(i)) for i in ids]
    return [t.result() for t in tasks]                  # block exited => all done or raised

Bound every wait with async with asyncio.timeout(5.0):. On CancelledError, clean up and re-raise — swallowing it breaks cancellation for the whole tree. Async is for IO-bound concurrency only; CPU-bound work blocks the loop — push it to asyncio.to_thread / a ProcessPoolExecutor (or 3.14's free-threaded build). HTTP servers belong to fastapi, not here. Runtime model, ExceptionGroup/except*, queues with backpressure, cancellation discipline, and sync<->async bridging -> references/async.md.

Project layout & packaging (uv)

Use a src/ layout so tests import the installed package, not the source tree by accident:

myapp/
  src/myapp/__init__.py
  src/myapp/core.py
  tests/test_core.py
  pyproject.toml
  uv.lock              # committed
  scripts/verify.sh

pyproject.toml is the single config — PEP 621 metadata, dependency groups, and tool config. Never hand-edit a requirements.txt; uv add writes the dep and updates uv.lock, which you commit.

[project]
name = "myapp"
version = "0.1.0"
requires-python = ">=3.12"
dependencies = ["httpx>=0.27"]

[project.scripts]
myapp = "myapp.core:main"          # console entry point

[dependency-groups]
dev = ["ruff>=0.15", "mypy>=1.13", "pytest>=8"]

[build-system]
requires = ["uv_build>=0.11"]
build-backend = "uv_build"         # uv's own backend, stable since July 2025

[tool.ruff]
line-length = 100

[tool.mypy]
strict = true

Core uv verbs (each updates uv.lock, which you commit):

uv init --package myapp        # scaffold pyproject.toml + src/ + .venv
uv add httpx                   # add a runtime dep
uv add --dev ruff mypy pytest  # add to the dev group
uv sync --frozen               # install exactly from the lockfile (CI + fresh clones)
uv run pytest -q               # run inside the managed venv
uv python install 3.14         # pin/install an interpreter

Quality gate + verify.sh

One local gate, mirroring CI: ruff check --fix ., ruff format ., mypy --strict src (or ty check), pytest -q. scripts/verify.sh runs all of them, skips a missing tool/dir with a warning, and exits non-zero on any real failure — run ./scripts/verify.sh from the project root before declaring done. CI is astral-sh/setup-uv + uv sync --frozen + the same four commands; full pipeline -> deployment.

pytest baseline

Carry just enough to make verify.sh meaningful — plain test_* functions, assert, pytest.raises, one parametrize, tmp_path for files:

import pytest
from myapp.core import head

@pytest.mark.parametrize("xs, expected", [(["a", "b"], "a"), (["x"], "x")])
def test_head_returns_first(xs: list[str], expected: str) -> None:
    assert head(xs) == expected

def test_head_rejects_empty() -> None:
    with pytest.raises(ValueError):
        head([])

def test_writes_file(tmp_path) -> None:
    (tmp_path / "f.txt").write_text("hi")
    assert (tmp_path / "f.txt").read_text() == "hi"

Deep fixtures, mocking, coverage gates, and property-based testing belong to testing-py — stop at the baseline and hand off.

Security (embedded, Python-specific)

Generic threat modeling and authz live in secure-coding; these Python-specific controls stay here:

# Bad                                          # Good
eval(user_input)                                ast.literal_eval(user_input)   # never eval/exec input
pickle.loads(network_bytes)                     json.loads(network_bytes)      # never unpickle untrusted data
subprocess.run(cmd, shell=True)                 subprocess.run(["ls", path])   # list args, no shell=True
random.random()  # tokens                       secrets.token_urlsafe(32)      # secrets, not random, for secrets

Keep deps locked (uv.lock) and audited (pip-audit / uv resolution); read secrets from env or a secret manager, never hardcode or log them.

Anti-patterns -> STOP

Project grounding (02-DOCS)

In a project that has the harness wiki, record this project's Python conventions in 02-DOCS/wiki/stack/python.md and index it in 02-DOCS/wiki/index.md — the interpreter floor, src/ layout, uv workflow, ruff/mypy config, async-vs-sync stance, and data-modeling defaults. Read it first on every use and bump its Updated date when a convention changes. This is recorded, not gated: never block the task on it, and skip silently when there is no 02-DOCS/ layer.