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 tushaarmehtaa/tushar-skills --skill humanizegit clone --depth 1 https://github.com/tushaarmehtaa/tushar-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/tushaarmehtaa/tushar-skills/humanize)<a href="https://agentmods.dev/skills/tushaarmehtaa/tushar-skills/humanize"><img src="https://agentmods.dev/badge/skills/tushaarmehtaa/tushar-skills/humanize/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/tushaarmehtaa/tushar-skills/humanize"><img src="https://agentmods.dev/badge/skills/tushaarmehtaa/tushar-skills/humanize.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 | $0.00039 | $0.01054 |
| Opus 5.5 | $0.00016 | $0.00422 |
| Sonnet 5.5 | $0.00008 | $0.00211 |
| Haiku 4.5 | $0.00004 | $0.00105 |
Grade A, and why
humanize 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Humanize
Make prose sound like its author writing clearly, not like a different fashionable voice. Remove generic model habits only when the text supplies evidence for the edit. Preserve facts, calibrated uncertainty, technical terms, personality, and the register required by the format.
Keep the boundary clear
Use this skill for voice preservation and synthetic-writing patterns.
- Use
landing-copy,cold-outreach,readme, orui-copywhen the primary job is creating or correcting that format's substance. - Use
remove-ai-slopfor rendered interface patterns and cross-route design convergence. - Use an article-development workflow when the argument, reporting, or structure needs major editorial work before a prose pass.
A request to “humanize,” “de-slop,” preserve voice, or detect AI-like patterns belongs here. A generic “make this better” should stay with the format-specific skill unless the evidence shows voice is the main problem.
Choose the mode
- Edit — return revised prose while preserving the author's position and factual meaning.
- Detect — identify checkable patterns and explain their effect without rewriting.
- Calibrate — derive a compact voice profile from supplied samples for a later or included edit.
Infer the mode and state it briefly. Do not claim that a detector can determine who or what wrote the text, and do not output an “AI percentage.”
Establish the writing contract
Read the full draft before changing sentences. Determine:
- audience, format, and intended reader action or belief;
- point of view, vocabulary, cadence, humor, formality, and recurring intentional habits;
- statements that carry facts, uncertainty, legal scope, attribution, or personal experience;
- passages that already sound specific and alive.
Infer the voice from a substantial mixed-quality draft. If the text is almost entirely synthetic and the user explicitly wants their personal voice, ask for one representative sample in a single concise question. If they only want clearer prose, proceed in an appropriate neutral register and say that no personal voice sample was available.
What ships with it
3 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 First seen · 94 lines · 39 tokens per session scan A 4c31cf3518a8
humanize is a skill published in the GitHub repository tushaarmehtaa/tushar-skills (12 stars, last pushed 5d ago), licensed MIT. It adds 39 tokens to every session and 1,054 once invoked, about $0.0002 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 2026-09-05.
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