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 eigent-ai/agent-skills --skill humanizergit clone --depth 1 https://github.com/eigent-ai/agent-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/eigent-ai/agent-skills/humanizer)<a href="https://agentmods.dev/skills/eigent-ai/agent-skills/humanizer"><img src="https://agentmods.dev/badge/skills/eigent-ai/agent-skills/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/eigent-ai/agent-skills/humanizer"><img src="https://agentmods.dev/badge/skills/eigent-ai/agent-skills/humanizer.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 | $0.00000 | $0.01179 |
| Opus 5.5 | $0.00000 | $0.00472 |
| Sonnet 5.5 | $0.00000 | $0.00236 |
| Haiku 4.5 | $0.00000 | $0.00118 |
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 — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Overview
Detects and rewrites 29 AI writing patterns across content, language, style, and communication categories. Patterns include: significance inflation, vague attributions, copula avoidance, synonym cycling, em dash overuse, sycophantic tone, chatbot artifacts, and more. Also supports voice calibration from your own writing samples.
Source Repository
- GitHub: blader/humanizer
- Install upstream:
npx skills add blader/humanizer
/humanize
Full 29-pattern detection and rewrite. Analyzes the input for all AI-sounding patterns, rewrites the content, then runs a second-pass "obviously AI?" audit before returning the final version.
Workflow
- Receive the text to humanize and any brand voice notes.
- Scan for all 29 patterns — flag each instance found.
- Rewrite the text: fix flagged patterns, preserve the core argument and facts.
- Run a second-pass audit: read the rewrite cold and ask "does any sentence still sound AI-generated?"
- Return the final rewritten text with a brief summary of the main changes made.
Example prompts
| Use case | Task prompt |
|---|---|
| Blog post | Humanize this blog post draft. Remove any AI-sounding patterns. Preserve the core argument but make it read like a person who actually has opinions wrote it. |
| Rewrite this LinkedIn post. It currently sounds like ChatGPT wrote it. Cut the significance inflation, remove the em dashes, and make it direct. | |
| Press release | Run the 29-pattern check on this press release. Flag every AI pattern you find, then rewrite it. The brand voice is confident and plain-spoken, not corporate. |
/voice-calibrate
Accepts 2–3 writing samples from the target author, extracts their stylistic fingerprint (sentence rhythm, vocabulary, punctuation habits), then applies that fingerprint to any AI-generated text.
Workflow
- Receive 2–3 writing samples from the target author.
- Analyze samples for: average sentence length, punctuation habits, vocabulary range, tonal register, structural patterns (how they open/close paragraphs), and idioms they favor.
- Document the fingerprint as a short style profile.
- Apply the fingerprint to the target AI text, rewriting to match the author's natural voice.
- Return the rewritten text with the style profile so it can be reused in follow-up requests.
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 First seen · 97 lines · 0 tokens per session scan A 630c7c9eea91
humanizer is a skill published in the GitHub repository eigent-ai/agent-skills (24 stars, last pushed 12d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 1,179 tokens. 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.
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