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 Aboudjem/humanizer-skill --skill humanizergit clone --depth 1 https://github.com/Aboudjem/humanizer-skillWrote 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/aboudjem/humanizer-skill/humanizer)<a href="https://agentmods.dev/skills/aboudjem/humanizer-skill/humanizer"><img src="https://agentmods.dev/badge/skills/aboudjem/humanizer-skill/humanizer/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/aboudjem/humanizer-skill/humanizer"><img src="https://agentmods.dev/badge/skills/aboudjem/humanizer-skill/humanizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Anti-Refusal · line 132 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
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 | $0.00139 | $0.09377 |
| Opus 5.5 | $0.00056 | $0.03751 |
| Sonnet 5.5 | $0.00028 | $0.01875 |
| Haiku 4.5 | $0.00014 | $0.00938 |
Grade A, and why
humanizer 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 1mo 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 — 444 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanizer: Make Text Sound Like a Human Wrote It
Take text that smells like a chatbot wrote it and rewrite it as a specific, opinionated human. Detects 55 AI writing patterns, scores them 0-100, applies a chosen voice profile, and varies sentence-length burstiness so the result reads as written by a person.
Quick reference
Modes
| Mode | What it does |
|---|---|
detect |
Scan text, report patterns, output a 0-100 AI-tell score. No rewrite. |
rewrite |
Full transform with voice injection. Default mode. |
edit |
In-place file editing using the Edit tool. Minimal targeted changes. |
Voices
| Voice | Personality | Best for |
|---|---|---|
casual |
Contractions, first person, fragments | Blog posts, social media |
professional |
Selective contractions, dry wit | Business comms, reports |
technical |
Precise vocabulary, code-like clarity | API docs, READMEs |
warm |
"We" language, empathy, short paragraphs | Tutorials, onboarding |
blunt |
Shortest sentences, no hedging, active voice | Internal comms, reviews |
Pattern catalog (55 total)
| Category | Count | IDs |
|---|---|---|
| Content | 8 | P1 to P8 |
| Language & Style | 10 | P9 to P18 |
| Communication | 3 | P19 to P21 |
| Filler & Hedging | 9 | P22 to P30 |
| Emerging | 13 | P31 to P43 |
| Craft & Forensic | 12 | P44 to P55 |
Flags
| Flag | Effect |
|---|---|
--score |
Prepend a [Score: NN/100] AI-tell density header |
--iterate N |
Loop detect, rewrite, detect until convergence (max N=3) |
--aggressive |
Heavier rewrite, shorter sentences, more personality |
--purpose |
Layer essay, email, marketing, technical, or general rules |
--openings N |
Generate N maximally-different opening hooks, surface the strongest |
--ignore-code |
Mask fenced code blocks before detect/score (do not flag inside them) |
--ignore-quotes |
Mask blockquotes before detect/score (do not rewrite quoted text) |
What ships with it
5 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.
- 1mo ago Changed 71f0825d5ccc
- 1mo ago First seen · 444 lines · 139 tokens per session scan A 12b5f6496d1f
humanizer is a skill published in the GitHub repository Aboudjem/humanizer-skill (290 stars, last pushed 1mo ago), licensed MIT. It adds 139 tokens to every session and 9,377 once invoked, about $0.0006 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 2026-08-30.
Other skills, from other repositories
voiceprint
Use when writing an article, newsletter, blog post or long social post that must genuinely be the user's own writing. Triggers on "voiceprint", "write this in my voice", "turn my recording into an article", "cut my transcript down", "make this actually mine", "write from my video". Turns the user's own spoken words…
patina
Detect and rewrite AI writing patterns in Korean, English, Chinese, and Japanese text so it reads as if a human wrote it. Meaning-preservation (MPS) verified.
clarity
Draft, rewrite, or review reader-facing prose so it is specific, useful, and recognizably the author's without inventing facts or performing humanness. Use for essays, articles, newsletters, documentation, talks, launch copy, and other important prose that feels generic, hollow, or AI-shaped. Supports co-write…
untell
Audit an AI detector and measure what its verdict is worth, using a closed-loop detector-feedback rewrite as the instrument (the "untell" skill). Use when the user wants to check whether text reads as AI-generated, find out why a detector flagged something, measure a detector's false-positive rate on human writing…
book-genesis-full
Production Mode — full 17-phase pipeline with all 19 skills. Use when you want the complete industrial pipeline with research, evaluation, editorial packaging. For most users, use /book-genesis (Craft Mode) instead.
voice-fingerprint
Builds the Voice DNA document — a per-character voice specification that the writer uses to differentiate every character. Includes global narrative voice, per-character voice cards with speech patterns + voice-under-pressure variants, anti-pattern checklist, and benchmark samples. The document that makes the…