Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add AratKruglik/claude-sdlc --skill python-conventionsgit clone --depth 1 https://github.com/AratKruglik/claude-sdlcWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/aratkruglik/claude-sdlc/python-conventions)<a href="https://agentmods.dev/skills/aratkruglik/claude-sdlc/python-conventions"><img src="https://agentmods.dev/badge/skills/aratkruglik/claude-sdlc/python-conventions/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/aratkruglik/claude-sdlc/python-conventions"><img src="https://agentmods.dev/badge/skills/aratkruglik/claude-sdlc/python-conventions.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00211 | $0.03519 |
| Opus 5.5 | $0.00084 | $0.01408 |
| Sonnet 5.5 | $0.00042 | $0.00704 |
| Haiku 4.5 | $0.00021 | $0.00352 |
Grade A, and why
python-conventions scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 496 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Conventions (stack-agnostic, Python 3.10+)
This skill encodes idioms that reduce bugs and improve readability in any Python codebase. Apply alongside the active framework plugin's conventions skill (e.g., django-plugin:django-conventions, fastapi-plugin:fastapi-conventions).
Detection
Read pyproject.toml to determine the target Python version before writing code:
# pyproject.toml — Poetry style
[tool.poetry.dependencies]
python = "^3.10"
# pyproject.toml — PEP 621 style
[project]
requires-python = ">=3.10"
Also check for a .python-version file (used by pyenv):
cat .python-version # e.g. "3.12.3"
Match the project's minimum version — do not use Python 3.11+ features (e.g. StrEnum, tomllib, ExceptionGroup) on a 3.10 project. All idioms in this skill apply from 3.10 onwards.
Type hints — always on
Annotate every function parameter, return type, and class attribute. Type hints are documentation that a machine can check.
from __future__ import annotations # enables forward references on 3.10
from collections.abc import Sequence
def greet(name: str, times: int = 1) -> str:
return (f"Hello, {name}!\n" * times).rstrip()
def first_active(users: Sequence[User]) -> User | None: # union syntax (3.10+)
return next((u for u in users if u.is_active), None)
Use from __future__ import annotations at the top of files that reference types not yet defined at parse time (forward references, self-referential types). It defers evaluation of all annotations to strings, avoiding NameError.
For library or shared utility code, use TypeVar and Generic to express parametric types:
from typing import TypeVar, Generic
T = TypeVar("T")
class Result(Generic[T]):
def __init__(self, value: T, ok: bool) -> None:
self.value = value
self.ok = ok
@classmethod
def success(cls, value: T) -> Result[T]:
return cls(value, ok=True)
@classmethod
def failure(cls, value: T) -> Result[T]:
return cls(value, ok=False)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +2 lines 600a6d9d5733
- 1mo ago First seen · 494 lines · 211 tokens per session scan A 05c7d57ddcdc
python-conventions is a skill published in the GitHub repository AratKruglik/claude-sdlc (53 stars, last pushed 2d ago), licensed MIT. It adds 211 tokens to every session and 3,519 once invoked, about $0.0008 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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