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 Global-mindee/WAY --skill anti-ai-writinggit clone --depth 1 https://github.com/Global-mindee/WAYWrote 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/global-mindee/way/anti-ai-writing)<a href="https://agentmods.dev/skills/global-mindee/way/anti-ai-writing"><img src="https://agentmods.dev/badge/skills/global-mindee/way/anti-ai-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/global-mindee/way/anti-ai-writing"><img src="https://agentmods.dev/badge/skills/global-mindee/way/anti-ai-writing.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 | $0.00073 | $0.02839 |
| Opus 5 | $0.00036 | $0.01419 |
| Sonnet 5 | $0.00015 | $0.00568 |
| Haiku 4.5 | $0.00007 | $0.00284 |
Grade A, and why
anti-ai-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 7d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Anti-AI Writing
The goal is not "don't sound like AI." That framing loses — blocklists rot, and chasing a negative gives you a beige voice. The goal is: sound like a specific person who has thought about the thing and has something to say. Specificity is the moat. Voice is the moat. Everything below serves those two.
Apply with judgment. Spirit beats letter. If a rule makes the sentence worse, break it.
Where this runs
This skill matters most on written content — text a reader's eye scans. Run the full pass there: captions, carousels, newsletters, threads, LinkedIn, long-form, DMs, landing copy. It's the last filter on every written piece.
On spoken content (reel and video scripts — the words get said aloud), run the spoken subset only:
- Apply: specificity (Level 3+), kill hollow reframes that don't pay off, cut borrowed-authority / thinkfluencer filler, cut significance inflation. These hurt out loud too.
- Skip: the formatting tells (em dashes, bullets, hashtags don't exist in speech), and go light on the vocab blocklist — spoken cadence forgives, and your voice plus the viral-hooks and storytelling skills are already doing the heavy de-AI-ing. Over-applying prose rules to a script makes it sound stilted, not human.
The split can live inside a single piece. When you produce a reel, the script is spoken (subset), but the caption that ships with it is written (full pass). Apply the right intensity to each part of the output, not one blanket pass.
The 5 diseases (diagnose before you fix)
Most AI writing fails for one of five reasons. Name the disease and the fix is obvious. If you can't name it, read the line aloud — the disease becomes audible.
- Vagueness compression — describes a category, not a thing. "Users were frustrated" → "users clicked export six times because nothing loaded."
- Significance inflation — treats normal facts like turning points. "This marks a pivotal shift in onboarding" → state the fact, let the reader weigh it.
- Hedged confidence — has a position but won't commit. "It could be argued that…" → take the position or cut the sentence.
- Rhythmic flatness — every sentence the same length, every paragraph three sentences. → break the meter.
- Borrowed authority — sounds like a McKinsey deck or a LinkedIn thinkfluencer. No fingerprint. → write it the way you'd say it to one person.
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.
- 7d ago First seen · 137 lines · 73 tokens per session scan A 0cebbc155c9f
anti-ai-writing is a skill published in the GitHub repository Global-mindee/WAY (11 stars, last pushed 3d ago), licensed MIT. It adds 73 tokens to every session and 2,839 once invoked, about $0.0004 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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