appkit: Skill for Claude Code

.claude/skills/python-clean-code/SKILL.md

python-clean-code is a skill for Claude Code from jenreh/appkit. It costs 165 tokens per session (2,217 once invoked), scanned A, original, MIT.

A set of guidelines for writing and refactoring Python code using Clean Code Developer principles. It covers how to divide responsibilities, separate concerns, and design clear interfaces.

In plain words
What is it for?
Use it when creating Python classes, modules, type signatures, or architecture, and when reviewing or restructuring existing Python code.
Why use it?
It helps prevent classes and modules from becoming difficult to change, test, or understand. It provides design checks before implementation and review.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is jenreh/appkit's own configuration. It tells Claude Code how to work on appkit itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything appkit configures →

Reuse

Borrowing it

Nothing to install: this file belongs to jenreh/appkit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/jenreh/appkit/main/.claude/skills/python-clean-code/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/jenreh/appkit

Made for: Claude Code.

Wrote 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.

agentmods badge for python-clean-code

README.md
[![agentmods](https://agentmods.dev/badge/skills/jenreh/appkit/python-clean-code/github.svg)](https://agentmods.dev/skills/jenreh/appkit/python-clean-code)
Your own site
<a href="https://agentmods.dev/skills/jenreh/appkit/python-clean-code"><img src="https://agentmods.dev/badge/skills/jenreh/appkit/python-clean-code/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.

agentmods 80×15 button for python-clean-code

Your own site · 80×15
<a href="https://agentmods.dev/skills/jenreh/appkit/python-clean-code"><img src="https://agentmods.dev/badge/skills/jenreh/appkit/python-clean-code.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 165 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,217 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00165 $0.02217
Opus 5.5 $0.00066 $0.00887
Sonnet 5.5 $0.00033 $0.00443
Haiku 4.5 $0.00016 $0.00222

Measured 1mo ago against content hash 66fe05c96fca, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

python-clean-code 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.

.claude/skills/python-clean-code/SKILL.md · 288 lines

How it starts

The opening of the file, as written. The whole thing — 288 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Clean Code Python

Apply these principles whenever writing or refactoring Python code. Don't narrate the framework to the user — just produce code that embodies these values. Use the self-review checklist before presenting any non-trivial code.

Before writing code

Think through: what is the single responsibility of this component? What are its dependencies? What will callers need from it? Design the interface and type signatures before the body. Only implement what the current requirement demands.


Principles

SRP — Single Responsibility Principle

One class, one reason to change. If a class touches persistence AND domain logic AND notifications, split it.

# Bad: three responsibilities in one class
class User:
    def save(self) -> None: ...          # persistence
    def send_welcome_email(self) -> None: ...  # notifications
    def validate_email(self) -> bool: ...  # domain rule

# Good: one class per responsibility
class User: ...              # domain model only
class UserRepository: ...    # persistence
class UserMailer: ...        # notifications

SoC — Separation of Concerns

Cross-cutting concerns (logging, caching, auth, retries) belong in decorators or middleware, not scattered inline inside business methods.

# Bad: logging woven into business logic
def get_user(user_id: int) -> User:
    log.info("Fetching user %d", user_id)
    user = db.query(User).get(user_id)
    log.info("Fetched %s", user.name)
    return user

# Good: concern at the boundary, not the body
@log_call
def get_user(user_id: int) -> User:
    return repo.get(user_id)

SLA — Single Level of Abstraction

Every line in a function should live at the same conceptual level. Don't mix raw SQL with domain calls or HTTP requests with UI logic.

# Bad: two levels in one function
def process_order(order_id: int) -> None:
    row = db.execute("SELECT * FROM orders WHERE id=?", [order_id]).fetchone()
    order = Order(id=row[0], total=Decimal(row[1]))
    send_confirmation_email(order.customer_email)

# Good: uniform level of abstraction
def process_order(order_id: int) -> None:
    order = order_repo.get(order_id)
    notifier.send_confirmation(order)

Read the full file on GitHub · 288 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

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.

  1. 1mo ago First seen · 288 lines · 165 tokens per session scan A 66fe05c96fca

Subscribe to this mod's changes

python-clean-code is a skill published in the GitHub repository jenreh/appkit (4 stars, last pushed 2d ago), licensed MIT. It adds 165 tokens to every session and 2,217 once invoked, about $0.0007 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 2026-08-31.

Related

Other skills, from other repositories

remember

Review the current conversation and capture valuable knowledge — best practices, coding conventions, architecture decisions, workflows, and user feedback — into persistent memory (AGENTS.md) or reusable skills. Use when the user says: (1) remember this, (2) save what we learned, (3) update memory, (4) capture…

langchain-ai/deepagents · 71 tokens

code-review

Perform a structured code review of changes, checking for correctness, style, tests, and potential issues.

langchain-ai/deepagents · 23 tokens

code-review

Code review assistance with linting, style checking, and best practices.

agno-agi/agno · 16 tokens

splitting-oversized-modules

Split an oversized Python module (a thousand-plus-line logic.py, models.py, api.py, or its test file) into a package of one module per concern, mechanically and provably without changing behavior. Use on a request to split / break up / decompose a god module or move functions out of one, once a human has agreed to…

PostHog/posthog-foss · 197 tokens

red-team-review

Unified adversarial review, two modes. Strategic Matrix v4.3 (MTA-004) for arguments/plans/decisions: Priors → Rubric → Adversarial Lenses → SWOT/TOWS → MCDA → Blind Spot/Kill Switch → Summary. Deliverable Audit for built artefacts (assignments, reports, code outputs): isolated fresh-context hunt against the brief…

winstonkoh87/Athena-Public · 103 tokens

qa-frontend

Internal PostHog developer frontend/browser QA skill. Use only when a PostHog developer explicitly asks to run frontend QA, browser-test a PR, verify a UI flow against the local PostHog stack, use qa-frontend, or QA current frontend changes with browser/runtime evidence, or to make a feature reel (see reel mode…

PostHog/posthog · 216 tokens