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 yeaight7/agent-powerups --skill ai-slop-cleanergit clone --depth 1 https://github.com/yeaight7/agent-powerupsWrote 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/yeaight7/agent-powerups/ai-slop-cleaner)<a href="https://agentmods.dev/skills/yeaight7/agent-powerups/ai-slop-cleaner"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/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/yeaight7/agent-powerups/ai-slop-cleaner"><img src="https://agentmods.dev/badge/skills/yeaight7/agent-powerups/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 | $0.00048 | $0.00970 |
| Opus 5 | $0.00024 | $0.00485 |
| Sonnet 5 | $0.00010 | $0.00194 |
| Haiku 4.5 | $0.00005 | $0.00097 |
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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Reduce AI-generated code bloat through systematic, smell-by-smell cleanup that preserves existing behavior and raises signal quality.
When to Use
- A code path works but feels bloated, noisy, repetitive, or over-abstracted.
- A user asks to "cleanup", "refactor", or "deslop" AI-generated output.
- Follow-up implementation left duplicate code, dead code, weak boundaries, or missing tests.
- A disciplined cleanup workflow is needed without broad rewrites.
- The user wants a reviewer-only anti-slop pass via
--reviewmode.
This skill accepts an optional file list scope. If a changed-files list is provided, keep the cleanup strictly bounded to those files. Do not silently expand a changed-file scope.
Inputs
- Codebase or module to clean (or explicit file list scope).
- Existing test suite (required — behavior must be locked before editing).
Workflow
-
Lock behavior with regression tests first
- Identify the behavior that must not change.
- Add or run targeted regression tests before touching cleanup candidates.
- If behavior is currently untested, write the narrowest test coverage needed first.
-
Create a cleanup plan before code
- List the specific smells to remove.
- Bound the pass to the requested files/scope.
- Order fixes from safest/highest-signal to riskiest.
- Do not start coding until the cleanup plan is explicit.
-
Categorize issues
- Duplication — repeated logic, copy-paste branches, redundant helpers
- Dead code — unused code, unreachable branches, stale flags, debug leftovers
- Needless abstraction — pass-through wrappers, speculative indirection, single-use helper layers
- Boundary violations — hidden coupling, leaky responsibilities, wrong-layer imports or side effects
- Missing tests — behavior not locked, weak regression coverage, gaps around edge cases
-
Execute passes one smell at a time
- Pass 1: Dead code deletion
- Pass 2: Duplicate removal
- Pass 3: Naming and error handling cleanup
- Pass 4: Test reinforcement
- Re-run targeted verification after each pass.
- Do not bundle unrelated refactors into the same edit set.
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.
- 10d ago First seen · 112 lines · 48 tokens per session scan A a9f9e366e3ff
ai-slop-cleaner is a skill published in the GitHub repository yeaight7/agent-powerups (6 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 970 once invoked, about $0.0002 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
simplify-code
Sequential 3-lens cleanup of recent code changes.
hqe
Comprehensive codebase health auditing, remediation, and verification skill based on the canonical HQE Protocol v5.0.0.
systematic-debugging
4-phase root cause debugging: understand bugs before fixing.
github-code-review
Review PRs: diffs, inline comments via gh or REST.
requesting-code-review
Pre-commit review: security scan, quality gates, auto-fix.
python-debugpy
Debug Python: pdb REPL + debugpy remote (DAP).