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 RobinNorberg/oh-my-copilot --skill ai-slop-cleanergit clone --depth 1 https://github.com/RobinNorberg/oh-my-copilotWrote 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/robinnorberg/oh-my-copilot/ai-slop-cleaner)<a href="https://agentmods.dev/skills/robinnorberg/oh-my-copilot/ai-slop-cleaner"><img src="https://agentmods.dev/badge/skills/robinnorberg/oh-my-copilot/ai-slop-cleaner/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/robinnorberg/oh-my-copilot/ai-slop-cleaner"><img src="https://agentmods.dev/badge/skills/robinnorberg/oh-my-copilot/ai-slop-cleaner.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 | $0.00025 | $0.01602 |
| Opus 5.5 | $0.00010 | $0.00641 |
| Sonnet 5.5 | $0.00005 | $0.00320 |
| Haiku 4.5 | $0.00003 | $0.00160 |
Grade A, and why
ai-slop-cleaner 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.
This is a copy
88% identical to ai-slop-cleaner — 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.
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Slop Cleaner
Use this skill to clean AI-generated code slop without drifting scope or changing intended behavior. In OMC, this is the bounded cleanup workflow for code that works but feels bloated, repetitive, weakly tested, or over-abstracted.
When to Use
Use this skill when:
- the user explicitly says
deslop,anti-slop, orAI slop - the request is to clean up or refactor code that feels noisy, repetitive, or overly abstract
- follow-up implementation left duplicate logic, dead code, wrapper layers, boundary leaks, or weak regression coverage
- the user wants a reviewer-only anti-slop pass via
--review - the goal is simplification and cleanup, not new feature delivery
When Not to Use
Do not use this skill when:
- the task is mainly a new feature build or product change
- the user wants a broad redesign instead of an incremental cleanup pass
- the request is a generic refactor with no simplification or anti-slop intent
- behavior is too unclear to protect with tests or a concrete verification plan
OMC Execution Posture
- Preserve behavior unless the user explicitly asks for behavior changes.
- Lock behavior with focused regression tests first whenever practical.
- Write a cleanup plan before editing code.
- Prefer deletion over addition.
- Reuse existing utilities and patterns before introducing new ones.
- Avoid new dependencies unless the user explicitly requests them.
- Keep diffs small, reversible, and smell-focused.
- Stay concise and evidence-dense: inspect, edit, verify, and report.
- Treat new user instructions as local scope updates without dropping earlier non-conflicting constraints.
Scoped File-List Usage
This skill can be bounded to an explicit file list or changed-file scope when the caller already knows the safe cleanup surface.
- Good fit:
oh-my-copilot:ai-slop-cleaner skills/ralph/SKILL.md skills/ai-slop-cleaner/SKILL.md - Good fit: a Ralph session handing off only the files changed in that session
- Preserve the same regression-safe workflow even when the scope is a short file list
- Do not silently expand a changed-file scope into broader cleanup work unless the user explicitly asks for it
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.
- 1mo ago First seen · 146 lines · 25 tokens per session scan A a133b3eb6516
ai-slop-cleaner is a skill published in the GitHub repository RobinNorberg/oh-my-copilot (5 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 1,602 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 88% identical to ai-slop-cleaner, differing in 8 lines, and is treated as a copy.
Other skills, from other repositories
resolve-pr-feedback
Use when asked to resolve, address, fix, or reply to review feedback on a GitHub Pull Request or GitLab Merge Request, including review comments, unresolved threads, requested changes, or "apply the review comments" requests.
review-pr
Use when asked to review a GitHub Pull Request or GitLab Merge Request, including PR/MR URLs, identifiers, discussions, findings, inline comments, approvals, or request-changes actions.
code-review
Reusable review core for structured findings on supplied code or diffs. Invoked by review-pr, review-local, and the pre-push self-review in resolve-pr-feedback; not for reviewing a PR/MR or local working tree directly, use those skills instead.
review-architecture
Read-only architecture and design-quality review of an existing codebase. Finds hot spots from git churn, file growth, complexity and files that change together, then checks them against layering, dependency direction, cohesion, SOLID, simplicity, test safety and platform-support rules (runtime and framework end of…
review-and-resolve-pr
Use when asked to review a GitHub Pull Request or GitLab Merge Request and act on the result in one autonomous pass, including "review and fix this PR", "review this MR and resolve the feedback", "close the review loop on this PR", or any request that pairs reviewing a PR/MR with applying the findings. Reviews the…
review-local
Use when reviewing local code before committing and the user wants findings for a file, folder, uncommitted changes, staged changes, commits, patches, or snippets without involving a pull request or merge request.