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 uchicago-dsi/ai-sci-skills --skill humanizegit clone --depth 1 https://github.com/uchicago-dsi/ai-sci-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/uchicago-dsi/ai-sci-skills/humanize)<a href="https://agentmods.dev/skills/uchicago-dsi/ai-sci-skills/humanize"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-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/uchicago-dsi/ai-sci-skills/humanize"><img src="https://agentmods.dev/badge/skills/uchicago-dsi/ai-sci-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 | $0.00041 | $0.02917 |
| Opus 5.5 | $0.00016 | $0.01167 |
| Sonnet 5.5 | $0.00008 | $0.00583 |
| Haiku 4.5 | $0.00004 | $0.00292 |
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 20d 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/humanize
Revise the requested prose in place so it sounds like the author and so a reader gets it on the first pass. This is an editing command, not an AI-detection claim and not a report the user has to process.
Two jobs, one pass:
- Remove the machine register. Formulaic transitions, inflated framing, announcements that something is important, stacked hedging, generic praise.
- Make it plainer. Replace a term with what it does. Say the thing instead of naming the method that did it. Default to the plainer wording and make each technical term earn its place.
Neither job may change what is true.
Resolve the target and the audience
- Use the file, passage, or scope in
$ARGUMENTS. Forall, include prose relevant to the current deliverable and exclude rendered output, dependencies, vendored material, data, and code. - Plain is relative to a reader, so establish who is reading: the audience named
in
$ARGUMENTS, or the one the document itself names, or ask in one line. A specialist audience raises the bar for which terms are shared; it does not license jargon, and it never licenses a sentence that only makes sense to someone who already knows the answer. - Read applicable project instructions and any documented voice profile
(
voice-profile.md,STYLE.md, or the local equivalent) before editing. - Use American spelling unless the target or the venue says otherwise: center, normalization, hematocrit, behavior, labeled, analyze. British forms creep in from quoted sources and from earlier drafts, so check rather than assume.
Resolve the register
Register is how a sentence sounds: contractions, person and voice, how much jargon the reader shares, whether naming a method is the job or a lapse. It differs enough between a slide and a manuscript that one set of defaults cannot serve both, and the difference is not a single axis — a machine learning conference paper and a clinical physics manuscript disagree with each other about as much as either disagrees with a talk.
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.
- 20d ago Changed · +3 lines 923d201a375f
- 1mo ago First seen · 252 lines · 41 tokens per session scan A 65c3d4625f47
humanize is a skill published in the GitHub repository uchicago-dsi/ai-sci-skills (20 stars, last pushed 4d ago), licensed MIT. It adds 41 tokens to every session and 2,917 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 2026-09-23.
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