cleanup

cleanup is a cursor rule for Cursor from ApexIQ/skillsmith. It costs 101 tokens per session, scanned A, a copy of benchmark, MIT.

A cleanup workflow for removing obsolete code and project artifacts.

In plain words
What is it for?
Use it to clean up supported projects, then run tests or the closest available validation command.
Why use it?
It provides defined steps for cleaning a project while keeping the work tied to its supported libraries and architecture areas.

Cursor rule for Cursor

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 rules/apexiq/skillsmith/cleanup
Clone the repo
git clone --depth 1 https://github.com/ApexIQ/skillsmith

Made for: Cursor.

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 cleanup

README.md
[![agentmods](https://agentmods.dev/badge/rules/apexiq/skillsmith/cleanup.svg)](https://agentmods.dev/rules/apexiq/skillsmith/cleanup)
Your own site
<a href="https://agentmods.dev/rules/apexiq/skillsmith/cleanup"><img src="https://agentmods.dev/badge/rules/apexiq/skillsmith/cleanup.svg" alt="Measured on agentmods" height="20"></a>
Per session 101 This file is loaded in full into every session.
When invoked 101 The same file — it is already loaded in full.
Security scan A 0 findings. Scan, not verified.
Origin 83% copy Near-identical to another mod 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 $0.00101 $0.00101
Opus 5 $0.00051 $0.00051
Sonnet 5 $0.00020 $0.00020
Haiku 4.5 $0.00010 $0.00010

Measured today against content hash e0fb9fa9085f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

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 today.

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.

Origin

This is a copy

83% identical to benchmark — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

.cursor/rules/workflows/cleanup.mdc · 11 lines

What it actually says

  • Read .agent/workflows/cleanup.md.
  • Goal: cleanup obsolete code and artifacts for library click, pytest, arch-business-logic, arch-ui, arch-unknown
  • Skills: notebooklm, loki_mode, terraform_module_library, javascript_testing_patterns, makepad_skills
  • Follow the workflow steps, then verify with project tests or the closest validation command.
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. today First seen · 11 lines · 101 tokens per session scan A e0fb9fa9085f

Subscribe to this mod's changes

cleanup is a cursor rule published in the GitHub repository ApexIQ/skillsmith (5 stars, last pushed 5mo ago), licensed MIT. It adds 101 tokens to every session, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to benchmark, differing in 8 lines, and is treated as a copy.