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 jlwin/unslop_ai --skill unslopgit clone --depth 1 https://github.com/jlwin/unslop_aiWrote 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/jlwin/unslop_ai/unslop)<a href="https://agentmods.dev/skills/jlwin/unslop_ai/unslop"><img src="https://agentmods.dev/badge/skills/jlwin/unslop_ai/unslop/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/jlwin/unslop_ai/unslop"><img src="https://agentmods.dev/badge/skills/jlwin/unslop_ai/unslop.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 | $0.00238 | $0.11185 |
| Opus 5.5 | $0.00095 | $0.04474 |
| Sonnet 5.5 | $0.00048 | $0.02237 |
| Haiku 4.5 | $0.00024 | $0.01119 |
Grade A, and why
unslop 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 — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Unslop
Prevents and removes the patterns that make readers recognize text as AI-generated – in German and English. This file follows its own rules; the tells it quotes are data, not violations.
Core principle: three levels, one universal rule
AI slop lives on three levels. Swapping words alone achieves little: structural features by themselves identify AI text with over 93 % accuracy, and professional surface rewriting barely shifts detection (StoryScope 2026). Word lists also age with every model generation. So always work through all three levels, weighted toward 2 and 3:
- Word level – vocabulary, stock phrases, punctuation. The weakest but most visible signal. Lists:
references/word-lists.md. - Sentence and rhythm level – uniform sentence lengths, parataxis chains, triads, negative parallelisms, load-bearing metaphors, hedge stacking, copula avoidance.
- Structure level – the spelled-out takeaway, uniform paragraphs, template scaffolding, vague instead of named references, narrative arcs that explain and resolve everything, shape convergence across texts. The most durable signal.
The universal rule: swap the vague claim for the checkable fact. That means a figure, a name, a date, a price, or the mechanism behind the claim. „Deutlich schneller“ needs a sourced time comparison; „von 4 Klicks auf 1“ needs sourced click counts and does not itself prove a shorter duration. But when the concrete fact is missing, mark the gap; never invent a number, anecdote, or source. A made-up detail damages trust more than an honest gap.
The short version
When attention is scarce, use these ten checks:
- Swap vague claims for sourced, checkable facts; keep relative measures such as „halbiert“ at their original strength. Flag missing evidence rather than inventing it.
- Pick the register first (profile table below): the same rule can be right in one register and wrong in the next.
- State the core point once, except in summaries required by the register; every sentence adds information; no formulaic opener or generic closer.
- Vary sentence construction without staccato; avoid parataxis chains and decorative triads, keeping groups whose meaning has three parts.
- At most one rhetorical negative parallelism per piece, usually none; corrective instructions, corrected figures, and authenticity statements stay.
- Check word-list hits in context; apply tier thresholds and protected uses. Keep the punctuation budget (em dash: rare in prose, zero on slides).
- Prose: actor and verb early, the everyday word. Slides: sober nominal style.
- Headings and slide titles name the content, usually with noun labels; deliberate action titles carry checkable claims.
- Name things (sources, tools, dates); name feelings instead of performing them.
- Never add facts, persona, or staccato the source did not have.
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 Changed · +5 lines · +57 tokens per session 134329c26cc2
- 19d ago Changed · +1 lines 948bf4ca774d
- 20d ago Changed · +12 lines c53c408497d4
- 23d ago Changed · +12 lines 42c4dd9fec1e
- 24d ago Changed · +7 lines 63b9aa643412
- 1mo ago First seen · 223 lines · 181 tokens per session scan A e58079ef8266
unslop is a skill published in the GitHub repository jlwin/unslop_ai (4 stars, last pushed 12d ago), licensed MIT. It adds 238 tokens to every session and 11,185 once invoked, about $0.0010 per session on Opus 5.5. 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 2026-09-19.
Other skills, from other repositories
humanizer-de
Edit-Pass für bestehenden deutschen Text: Register/Rhythmus messen, belegtreu redigieren, Naturalness prüfen, KI-Schreibmuster/KI-Tells auditieren und entfernen; deutschen Text humanisieren; German AI Text Humanizer.
humanize-korean
A Korean-language editing skill that makes AI-written text sound more natural while preserving its meaning, facts, numbers, names, quotations, and genre. It focuses on patterns such as translation-like wording, repetitive structure, and overly mechanical rhythm.
humanize-scan
A Korean-language checker that measures signs commonly associated with AI-written prose and shows sample edits. It is a quick scan, not a full rewrite.
commit-ko
A Korean commit-message editor that removes stiff, translated-sounding wording while preserving the message's meaning, scope, and type. A commit message is the short record attached to a change in Git.
humanize-redo
A second editing pass for Korean text produced by the humanize-korean workflow. It can revisit remaining issues in selected categories or paragraphs, with adjustable editing strictness.
humanize
A command for making AI-written Korean text sound more natural through an automated editing process.