code-cleanup

A focused guide for simplifying and refactoring Python files changed during the current work session. Refactoring changes code structure without changing its intended behavior.

In plain words
What is it for?
Use it after editing Python files when you need cleanup, simpler logic, consistent type annotations, or logging fixes.
Why use it?
It removes dead code and unnecessary complexity while keeping the changes small and checking formatting, linting, and types.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/jenreh/appkit/code-cleanup
Any agent
npx skills add jenreh/appkit --skill code-cleanup
Clone the repo
git clone --depth 1 https://github.com/jenreh/appkit

Made for: Claude Code, Codex.

Per session 46 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 486 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00046 $0.00486
Opus 5 $0.00023 $0.00243
Sonnet 5 $0.00009 $0.00097
Haiku 4.5 $0.00005 $0.00049

Measured 2d ago against content hash 830e4bb71acd, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

code-cleanup 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 2d 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/code-cleanup/SKILL.md · 44 lines

What it actually says

Code Cleanup

Target files

If $ARGUMENTS is provided, use those files. Otherwise, run:

git diff --name-only HEAD && git ls-files --others --exclude-standard

Filter the output to .py files only — those are the files to clean up. If no modified Python files are found, report that and stop.

Rules

  • Minimal diff — change only what clearly improves the file; do not restructure things that already work.
  • No new features — preserve all existing behavior exactly.
  • Per-file scope — do not modify files outside the target list unless an import must be fixed.

What to apply (per file)

  • Remove dead code, unused imports, redundant comments, and unnecessary variables.
  • Simplify overly complex conditionals; prefer early returns over deep nesting.
  • Break functions > 20 lines into focused, well-named helpers — but only when the split is obvious.
  • Replace verbose loops with list/dict/set comprehensions or generator expressions where readable.
  • Ensure every function and method has a type annotation.
  • No print statements — use logging (default level: logger.debug).
  • No f-strings in logger calls: log.info("x: %s", x) not log.info(f"x: {x}").
  • If a file exceeds 1000 lines after refactoring, split it and update imports accordingly.

Process

  1. Determine the target file list as described above and show it to the user.
  2. For each file: read it, identify improvements, apply as a single focused edit pass.
  3. After all edits: run task format && task lint && task typecheck and fix any issues.
  4. Summarize what changed per file (one line per change group).
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. 2d ago First seen · 44 lines · 46 tokens per session scan A 830e4bb71acd

Subscribe to this mod's changes

code-cleanup is a skill published in the GitHub repository jenreh/appkit (4 stars, last pushed 6d ago), licensed MIT. It adds 46 tokens to every session and 486 once invoked, about $0.0002 per session on Opus 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.

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