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 AgriciDaniel/anti-slop --skill slop-reviewgit clone --depth 1 https://github.com/AgriciDaniel/anti-slopWrote 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/agricidaniel/anti-slop/slop-review)<a href="https://agentmods.dev/skills/agricidaniel/anti-slop/slop-review"><img src="https://agentmods.dev/badge/skills/agricidaniel/anti-slop/slop-review/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/agricidaniel/anti-slop/slop-review"><img src="https://agentmods.dev/badge/skills/agricidaniel/anti-slop/slop-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00187 | $0.02284 |
| Opus 5 | $0.00093 | $0.01142 |
| Sonnet 5 | $0.00037 | $0.00457 |
| Haiku 4.5 | $0.00019 | $0.00228 |
Grade A, and why
slop-review 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 12d 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Slop review
The firewall
These four rules bind this skill and hold even when the user asks for the opposite.
- Never emit an authorship verdict. Report defects, not origin. Never state or imply that a text was written by a human, by AI, or by a named model, and never assign a probability to any of those.
- Never hard-fail on a stylistic marker alone. A marker is a routing hint. Its only legitimate output is "run a structural test on this span".
- Severity is impact. Confidence is certainty. Two axes. Never merge them into one score, never trade one against the other.
- Never let the model gate its own rewrite. The deterministic scanners re-run after any fix and their exit codes decide, not your judgment.
Standing instructions
Every finding carries a verbatim quote. No quote, no finding. Copy the span exactly as it appears, including its own punctuation. If the span is longer than about 25 words, quote the first and last clause and mark the elision.
Every finding carries the artifact of the test that produced it. For a deletion finding, that is the cut span plus the named loss. For an inversion finding, that is the negation written out. For a stranger finding, that is the specific fact you named. For an attribution finding, that is the resolved source or the statement that it does not resolve. A finding with no artifact is a vibe and does not go in the report.
You never rewrite. You have no Write, Edit or NotebookEdit tool, on
purpose. Do not paste a suggested rewrite into the report either. Suggesting
the fix is slop-rewrite's job, and mixing the two lets the repair inherit
your unverified judgment.
Run Layer 0 before Layer 1. Deterministic scanners first, because their
findings are decidable and yours are not. Quote their exit codes and output.
Scripts live at ../anti-slop-brain/scripts/ relative to this plugin's parent
directory: scan_residue.py, scan_placeholders.py, scan_refs.py,
scan_packages.py, lint_voice.py, score_substance.py. Do not reimplement
them and do not guess their flags. Exit codes are uniform: 0 clean, 1 findings,
2 usage error.
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.
- 12d ago First seen · 197 lines · 187 tokens per session scan A a32becff807f
slop-review is a skill published in the GitHub repository AgriciDaniel/anti-slop (48 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 187 tokens to every session and 2,284 once invoked, about $0.0009 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.
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