parallel-cleanup

parallel-cleanup is a command for coding agents from ethanolivertroy/my-agent-stuff. It costs 3 tokens per session (859 once invoked), scanned A, original, MIT.

A command that starts a cleanup review of the current code changes using two independent reviewers. It asks them to inspect the repository and look for unnecessary or misleading code and comments.

In plain words
What is it for?
Use it after making code changes when you want a fresh review focused on removing AI-generated clutter and other avoidable implementation problems.
Why use it?
It helps catch clutter, leftover debugging code, duplicated types, and checks that hide real errors before changes are finished. The reviewers work from the files and current diff rather than relying on earlier conversation context.

Command

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 commands/ethanolivertroy/my-agent-stuff/parallel-cleanup
Clone the repo
git clone --depth 1 https://github.com/ethanolivertroy/my-agent-stuff

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 parallel-cleanup

README.md
[![agentmods](https://agentmods.dev/badge/commands/ethanolivertroy/my-agent-stuff/parallel-cleanup.svg)](https://agentmods.dev/commands/ethanolivertroy/my-agent-stuff/parallel-cleanup)
Your own site
<a href="https://agentmods.dev/commands/ethanolivertroy/my-agent-stuff/parallel-cleanup"><img src="https://agentmods.dev/badge/commands/ethanolivertroy/my-agent-stuff/parallel-cleanup.svg" alt="Measured on agentmods" height="20"></a>
Per session 3 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 859 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 $0.00003 $0.00859
Opus 5 $0.00002 $0.00430
Sonnet 5 $0.00001 $0.00172
Haiku 4.5 $0.00000 $0.00086

Measured 4d ago against content hash 4491517710a2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

parallel-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 4d 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.

commands/parallel-cleanup.md · 60 lines

How it starts

The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Run a fresh-context parallel cleanup review of the current work.

Use the subagent tool. First inspect available agents/skills if needed, then launch two reviewer subagents in parallel with context: "fresh". Do not use forked context unless I explicitly ask for it. Reviewers must inspect the repository, relevant instructions, and current diff directly from files and commands. They must not rely on the main conversation history.

Do not write reviewer output files into the repository unless I explicitly ask for artifacts. Prefer output: false for each reviewer task.

Reviewer 1: deslop pass.

If the deslop skill is available, pass it to this reviewer. If not, inline the guidance below. Ask this reviewer to look for AI-slop patterns in the changed scope:

  • comments that restate code, placeholder text, stale rationale, or debug leftovers;
  • defensive checks that hide useful errors, return vague defaults, or validate trusted internal data after a real boundary was already crossed;
  • type escapes, broad casts, duplicated type definitions, or object-bag typing where a local source-of-truth type exists;
  • style drift from nearby non-slop code and project instructions;
  • generated-sounding docs, changelog text, UI copy, status text, or test names;
  • pass-through wrappers, dead helpers, duplicate helper signatures, duplicated test harness setup, or abstractions that do not enforce an invariant;
  • UI or CLI copy that is noisy, vague, brittle, or makes the user do extra interpretation.

Tell this reviewer to treat tool output and slop-scan-style findings as leads, not verdicts. It should flag only concrete issues in the requested scope with evidence, severity, file/line references, and the smallest safe fix.

Reviewer 2: verbosity pass.

If the verbosity-cleaner skill is available, pass it to this reviewer. If not, inline the guidance below. Ask this reviewer to look for needless verbosity in code, tests, docs, status text, grouped messages, receipts, and changelog wording:

  • single-use helpers that merely paraphrase an expression;
  • temporary variables that only name obvious expressions;
  • nested returns or branches that can become direct returns without hiding intent;
  • multi-line cleanup scaffolding that can use a local direct pattern while preserving cleanup semantics;
  • repeated boilerplate that can use an existing local fixture or a small local helper;
  • tests that restate formatter details already covered at a cheaper layer;
  • regression tests where one focused assertion would cover the bug but wrapper/API-adjacent tests only repeat the same claim;
  • prose that says the same thing twice, sounds generic, or buries the important rule.

Tell this reviewer that shorter is only better when it is clearer and preserves behavior, error signals, cleanup semantics, useful invariants, and local style.

Both reviewers are review-only. They must not edit files unless I explicitly ask for a writer pass. Their response should be review feedback, not a context summary. Ask them to return concise, evidence-backed findings with file/line references and suggested fixes.

While reviewers run, do your own narrow inspection if useful. After they return, synthesize the feedback into:

  • fixes worth doing now;
  • optional improvements;
  • feedback to ignore or defer, with a short reason.

Do not blindly apply every reviewer suggestion.

Autofix mode: if the invocation contains the exact word autofix, treat it as workflow control, not cleanup scope. Remove it before deciding the cleanup target. After synthesis, apply only fixes worth doing now, validate, and summarize. Do not apply optional improvements unless explicitly requested. If there are no fixes worth doing now, do not edit.

Without autofix mode, ask before applying fixes unless I already told you to address review feedback. When you ask, end with a compact numbered menu so I can respond with a number. Use wording suited to the findings, but include these choices when applicable:

Reply with [1], [2], or further instructions:
[1] Apply only the fixes worth doing now.
[2] Apply the fixes worth doing now plus optional improvements.

Read the full file on GitHub · 60 lines

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. 4d ago First seen · 60 lines · 3 tokens per session scan A 4491517710a2

Subscribe to this mod's changes

parallel-cleanup is a command published in the GitHub repository ethanolivertroy/my-agent-stuff (11 stars, last pushed 1mo ago), licensed MIT. It adds 3 tokens to every session and 859 once invoked, about $0.0000 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 2026-08-30.