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 naimkatiman/continuous-improvement --skill ai-slop-cleanergit clone --depth 1 https://github.com/naimkatiman/continuous-improvementWrote 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/naimkatiman/continuous-improvement/ai-slop-cleaner)<a href="https://agentmods.dev/skills/naimkatiman/continuous-improvement/ai-slop-cleaner"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/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/naimkatiman/continuous-improvement/ai-slop-cleaner"><img src="https://agentmods.dev/badge/skills/naimkatiman/continuous-improvement/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 | $0.01606 |
| Opus 5.5 | $0.00010 | $0.00642 |
| Sonnet 5.5 | $0.00005 | $0.00321 |
| Haiku 4.5 | $0.00003 | $0.00161 |
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
86% identical to ai-slop-cleaner — 0 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-claudecode: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 Changed · +12 lines 266ed0e1f362
- 1mo ago First seen · 134 lines · 25 tokens per session scan A 27bc14e53457
ai-slop-cleaner is a skill published in the GitHub repository naimkatiman/continuous-improvement (9 stars, last pushed 1mo ago), licensed MIT. It adds 25 tokens to every session and 1,606 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to ai-slop-cleaner, differing in 0 lines, and is treated as a copy.
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