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 Open-Agent-Tools/General-OAT-Skills --skill cleanupgit clone --depth 1 https://github.com/Open-Agent-Tools/General-OAT-SkillsWrote 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/open-agent-tools/general-oat-skills/cleanup)<a href="https://agentmods.dev/skills/open-agent-tools/general-oat-skills/cleanup"><img src="https://agentmods.dev/badge/skills/open-agent-tools/general-oat-skills/cleanup/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/open-agent-tools/general-oat-skills/cleanup"><img src="https://agentmods.dev/badge/skills/open-agent-tools/general-oat-skills/cleanup.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00053 | $0.00423 |
| Opus 5 | $0.00026 | $0.00211 |
| Sonnet 5 | $0.00011 | $0.00085 |
| Haiku 4.5 | $0.00005 | $0.00042 |
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 9d 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.
What it actually says
Perform a complete code cleanup by following these steps:
Output Format: Use clear section headers, timestamps for each step, and progress indicators for long-running operations.
Tool Validation: First verify that required tools are installed (uv, ruff, mypy, pytest) and identify the project source layout (src/ or app/).
Checklist
- Ruff linting and formatting
- Mypy type checking
- Pytest suite
- Summary
- Run ruff linting and formatting (parallel): Execute
uv run ruff check src --fixanduv run ruff format src(or useappif that's the project structure) in parallel to automatically fix linting issues and format code. Re-run to verify all issues resolved. - Run mypy type checking: Execute
uv run mypy src(orapp) according to the project structure and fix any errors found. Re-run to verify fixes. - Run pytest via UV: Execute
uv run pytestwith appropriate timeout handling. If tests timeout, run them in logical sections (e.g., by directory or test file groups) to ensure all tests are executed and results are captured.
Error Handling: For each step, run the tool, fix issues found, then re-run to confirm the fix. If unable to fix after two attempts, report the specific error details and suggest manual fixes.
Update the checklist above as each step completes. Provide a final summary of all changes made.
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.
- 9d ago First seen · 37 lines · 53 tokens per session scan A 3ccca089e34e
cleanup is a skill published in the GitHub repository Open-Agent-Tools/General-OAT-Skills (3 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 53 tokens to every session and 423 once invoked, about $0.0003 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.
Other skills, from other repositories
merge-queue-readiness
Pre-queue merge-group CI simulation. Triggered before adding a PR to a GitHub merge queue. Prevents merge-queue kick-outs from integration test failures.
restore-internals-seams-in-finally-blocks-after-each-test
When delegating a task affected by this skill, include.
run-affected-test-file-in-isolation-and-in-co-run-with-its-test-
When delegating a task affected by this skill, include.
use-internals-di-seam-pattern-for-test-injectable-functions
When delegating a task affected by this skill, include.
propagation-test
When delegating a task affected by this skill, include.
mock-module
When delegating a task affected by this skill, include.