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 vaayne/agent-kit --skill humanizergit clone --depth 1 https://github.com/vaayne/agent-kitWrote 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/vaayne/agent-kit/humanizer)<a href="https://agentmods.dev/skills/vaayne/agent-kit/humanizer"><img src="https://agentmods.dev/badge/skills/vaayne/agent-kit/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/vaayne/agent-kit/humanizer"><img src="https://agentmods.dev/badge/skills/vaayne/agent-kit/humanizer.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 | $0.00034 | $0.00939 |
| Opus 5.5 | $0.00014 | $0.00376 |
| Sonnet 5.5 | $0.00007 | $0.00188 |
| Haiku 4.5 | $0.00003 | $0.00094 |
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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanizer
Turn text that sounds model-written into text that sounds human-written. The goal is not to add quirks or make the prose messy. Preserve meaning and author intent while removing AI tells, promotional padding, formulaic structure, and the fake smoothness that makes text feel generated.
Workflow
-
Classify the task
- If the user asks to humanize, de-AI, or make text sound natural, rewrite the text.
- If the user asks for a review or AI-writing audit, report the problems instead of rewriting.
- If the user provides a file path, read the file and either rewrite or edit it as requested.
- If the user provides a writing sample, calibrate to that voice before rewriting the target text.
-
Preserve meaning and coverage
- Keep the core information and stance.
- Cover the same substantive points as the source.
- Flag suspicious facts, citations, and data. Do not invent sources to patch gaps.
-
Audit patterns before rewriting
- For a thorough review, read
references/ai-writing-patterns.md. - For a fast, strict cleanup, read
references/stop-slop-rules.md. - Look for clusters of tells. Do not rewrite normal human prose because of one em dash, one formal word, or one transition.
- For a thorough review, read
-
Run the stop-slop final gate
- Cut greeting residue, meta commentary, filler, fake profundity, and boilerplate.
- Prefer active voice and concrete nouns.
- The final rewrite must contain no em dash or en dash.
Output format
Default: deliver the rewrite only — no commentary, no preamble, no issue list.
When the user asks for a review or AI-writing audit instead of a rewrite, report the problems:
## AI tells
- [the most important problems, each with a suggested fix]
Add brief notes only when they change a decision the user must make — suspicious facts, voice choices, or source meaning you had to interpret.
Voice calibration
When the user provides a writing sample, inspect it before rewriting:
- Sentence length: short, long, or mixed?
- Word choice: casual, technical, formal, sharp, plain?
- Paragraph openings: direct, contextual, narrative?
- Punctuation habits: parentheses, colons, semicolons, dash substitutes?
- Transitions: explicit connectors or hard cuts?
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.
- 1mo ago Changed · -11 lines · -64 tokens per session 22553c0fd820
- 1mo ago First seen · 101 lines · 98 tokens per session scan A 467245e5737f
humanizer is a skill published in the GitHub repository vaayne/agent-kit (53 stars, last pushed 4d ago), licensed MIT. It adds 34 tokens to every session and 939 once invoked, about $0.0001 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.
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