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 swan-gtm/gtm-skills --skill anti-ai-slop-writinggit clone --depth 1 https://github.com/swan-gtm/gtm-skillsWrote 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/swan-gtm/gtm-skills/anti-ai-slop-writing)<a href="https://agentmods.dev/skills/swan-gtm/gtm-skills/anti-ai-slop-writing"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/anti-ai-slop-writing/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/swan-gtm/gtm-skills/anti-ai-slop-writing"><img src="https://agentmods.dev/badge/skills/swan-gtm/gtm-skills/anti-ai-slop-writing.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 | $0.00117 | $0.01394 |
| Opus 5 | $0.00059 | $0.00697 |
| Sonnet 5 | $0.00023 | $0.00279 |
| Haiku 4.5 | $0.00012 | $0.00139 |
Grade A, and why
anti-ai-slop-writing 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 9d 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 — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Applies to any prose before it ships. Produces copy with the recognisable AI patterns removed, in two disciplined passes.
The core idea: AI writing fails in two different ways, and each needs its own pass. Some patterns are rhetorical moves with no human upside - they are wrong every time they appear. Others are ordinary words and structures that read fine once but scream machine when they cluster. Scan for each separately.
This skill is the subtractive half of a two-pass writing standard. It takes tells out. Its companion, the human-mannerisms pass, does the opposite job - it puts voice in, so clean copy does not read flat. They are deliberately kept as two skills, not one, because the two jobs do not always both apply. Every piece that ships should have its AI tells removed; not every piece should be made to sound casual. A formal announcement, a compliance note, or a technical one-pager needs to be clean, not chatty - run this pass and stop. A LinkedIn post, a founder's thought leadership, or a personal email needs voice on top - run this pass first, then the mannerisms pass. Bundling them would force a casual register onto writing that should stay plain. Keeping them separate lets you apply the floor everywhere and the texture only where it fits.
The two passes
Run both, in order, before returning any prose.
Pass one - zero-instance patterns. Scan for rhetorical moves that are a tell every time. Any hit gets rewritten, not softened. The full catalogue is in references/zero-instance-patterns.md; the ones that slip through most often:
- Negative parallelism - asserting what something IS by first saying what it ISN'T about the same subject: "It's not X, it's Y", "Not just X, Y", "We don't just X, we Y". The test: remove the negated half. If the point still stands, the negation was scaffolding - cut it and state the claim directly.
- False-suspense transitions - "Here's the thing", "Here's what nobody tells you", "Here's what changed". The whole class, not set phrases. Does it name the revelation in the same beat, or just tease one? If it teases, cut it.
- Self-answered rhetorical questions - "The result? Devastating."
- The colon setup - the "abstract noun, colon, payoff" cadence ("The reality:", "The takeaway:"). Banned as a device; state the point as a plain sentence.
- Formulaic openers and closers - "In today's fast-paced world", "At its core", humblebrag announcement openers ("Thrilled to share"), and "In conclusion", "Ultimately", "At the end of the day".
- Patronising analogy - "Think of it like...", "It's like...". Even one forced analogy is too many; name the thing directly.
- Manufactured stakes - grandiose inflation ("this reshapes everything") and phantom-future projection ("a year from now you'll wish you'd..."). Stakes come from a present cost the reader already pays.
- Invented specificity - numbers, dates, percentages, or named moments not grounded in the source. If the source has no number, use no number.
Pass two - clustering patterns. Scan for vocabulary and structure that read fine once but signal machine when stacked. The full lists are in references/clustering-and-formatting.md. The shape of the rule: a single tricolon, one em dash, one strong adjective is human; three tricolons, an em dash in every sentence, and a wall of grandiose nouns is not. Watch grandiose nouns (tapestry, landscape, realm, ecosystem), pompous verbs (delve, leverage, unlock, foster, elevate), magic adverbs (deeply, seamlessly, fundamentally), anaphora (same opener three-plus times), uniform sentence and paragraph length, bold-first bullets, and emoji or decorative-unicode sprinkling.
What ships with it
2 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.
- 9d ago First seen · 49 lines · 117 tokens per session scan A a09bbd302235
anti-ai-slop-writing is a skill published in the GitHub repository swan-gtm/gtm-skills (150 stars, last pushed 2d ago), licensed MIT. It adds 117 tokens to every session and 1,394 once invoked, about $0.0006 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 2026-09-03.
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