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 agentmods add skills/blader/humanizer/humanizernpx skills add blader/humanizer --skill humanizergit clone --depth 1 https://github.com/blader/humanizerWhat 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 | $0.00061 | $0.06633 |
| Opus 5 | $0.00030 | $0.03317 |
| Sonnet 5 | $0.00012 | $0.01327 |
| Haiku 4.5 | $0.00006 | $0.00663 |
Grade A, and why
humanizer scanned grade A with 1 finding 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 yesterday.
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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- **Curly quotes alone.** macOS, Word, Google Docs, and most CMSes auto-curl by default. Curly quotes only count when stacked with other tells. Copies of this mod
5 near-identical copies found in the catalogue:
- humanizer — 100% identical, 0 lines differ
- humanizer — 100% identical, 0 lines differ
- humanizer — 100% identical, 0 lines differ
- humanizer — 98% identical, 1 lines differ
- a1-humanize — 94% identical, 29 lines differ
How it starts
The opening of the file, as written. The whole thing — 457 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanizer: remove AI writing patterns
Rewrite AI-sounding text so it reads like the writer, not a chatbot. Do not change what it says or make up details.
The patterns below come from Wikipedia's "Signs of AI writing", maintained by WikiProject AI Cleanup.
What to do
When given text to humanize:
- Find AI patterns. Check the text against the patterns below.
- Keep every claim. You may shorten dull parts, expand useful parts, and merge or split paragraphs. Keep the information even when you change the structure.
- Do not invent facts. Do not add a fact, name, number, date, quote, or citation unless it comes from the source or the user. If a sentence needs a missing detail, ask for it or use a simpler sentence. You may add an opinion or reaction when the writer's voice calls for one, but you may not add a factual claim. Fiction is exempt because invented details are part of the task.
- Match the voice. Use the right tone for the text, such as formal, casual, or technical. Add personality only when the text and the writer call for it.
The input type controls what you return. See How to return the result. Use the same rewrite process in every mode.
Match the writer's voice
If the user provides a writing sample (their own previous writing), analyze it before rewriting:
- Read the sample first. Note its sentence length, word choice, paragraph openings, punctuation, repeated phrases, and transitions.
- Match those habits. Do not replace casual words with formal ones or remove deliberate quirks.
- If there is no sample, use the guidance below.
A writing sample takes priority over these style rules. If the sample uses em dashes, keep them at about the same rate. Do not apply §14 as a ban.
Add personality only when it fits
Removing AI patterns is only half the job. The result should still sound like a person.
Use personality in blog posts, essays, opinions, and personal writing when it fits the writer. Keep reference, technical, legal, and factual text neutral. Do not add opinions or first-person language where they do not belong.
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.
- yesterday First seen · 457 lines · 61 tokens per session scan A 14fc8a965b6e
humanizer is a skill published in the GitHub repository blader/humanizer (38,958 stars, last pushed 13d ago), licensed MIT. It adds 61 tokens to every session and 6,633 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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