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 | $0.00013 | $0.00803 |
| Sonnet 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 2d 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.
- 2d ago Changed · +12 lines 266ed0e1f362
- 7d 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 (7 stars, last pushed 4d ago), licensed MIT. It adds 25 tokens to every session and 1,606 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to ai-slop-cleaner, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
security-audit
Security review and hardening workflow — root-cause analysis of vulnerabilities, authentication and authorization checks, least privilege, input handling, secret hygiene, and security regression tests. Use when reviewing code for security, fixing a vulnerability, hardening a feature, or handling auth, permissions…
ap-policies
Attach a completion policy gate (shell check or judge-agent rubric) to a session or fan-in group so turn-end only passes when the gate is green, then optionally auto-commit pending human ack. Use when the user says "gate this session on tests passing", "attach a policy", "commit only if tests are green", "judge…
behavior-implement
Implement behavior through a red-green-refactor cycle when focused automated tests are proportionate. Use for product behavior changes and for engineering tooling and infrastructure only when native checks are insufficient and concrete complexity or failure risk warrants dedicated tests.
Pair Programming
AI-assisted pair programming with multiple modes (driver$navigator$switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Features automatic role switching, continuous code review, security scanning, and performance optimization with…
build-test
Run the project's build / typecheck / lint / test commands and emit the build.passing + tests.passing signals devloop convergence reads.
review-prs
Review a GitHub pull request in the googleapis/mcp-toolbox repo against the team's reviewer checklist: PR title/description conventions, linked issue, logic errors and unhandled edge cases, breaking changes, test coverage, docs updates, security (input handling), and new dependencies. Use whenever a maintainer asks…