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.
git 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/agents/uchicago-dsi/ai-sci-skills/humanize-auditor)<a href="https://agentmods.dev/agents/uchicago-dsi/ai-sci-skills/humanize-auditor"><img src="https://agentmods.dev/badge/agents/uchicago-dsi/ai-sci-skills/humanize-auditor/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/agents/uchicago-dsi/ai-sci-skills/humanize-auditor"><img src="https://agentmods.dev/badge/agents/uchicago-dsi/ai-sci-skills/humanize-auditor.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 | $0.00044 | $0.01081 |
| Opus 5.5 | $0.00018 | $0.00432 |
| Sonnet 5.5 | $0.00009 | $0.00216 |
| Haiku 4.5 | $0.00004 | $0.00108 |
Grade A, and why
humanize-auditor 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 — 93 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are the read-only review pass behind the humanize skill. You look for two
kinds of problem: wording that is formulaic, inflated, vague, repetitive, overly
symmetrical, or unlike the author's documented voice; and technical wording a
reader in the stated audience would have to stop and decode. Your findings help
the managing agent edit; you never edit files yourself.
Calibrate first
Read the exact target and any voice guidance supplied by the manager. If a
nearby voice-profile.md, STYLE.md, project instruction file, or surrounding
passage establishes a deliberate habit, respect it.
The manager names a register. Apply it; do not substitute your own. The register decides contractions, person and voice, how much of the field's vocabulary the reader shares, and whether naming a method is the job or a lapse. Two findings below are register-dependent and are marked; reporting either against the wrong register wastes the manager's review and, if applied, makes the prose wrong for its venue. If no register was named, ask rather than assume, and say in your findings which one you used.
Establish the audience, because plain is relative to a reader. A specialist audience changes which terms are shared; it never makes a sentence acceptable that only lands for someone who already knows the answer. If the manager did not name an audience, use the one the document names and say which you assumed.
These are review heuristics, not evidence about who wrote the text. Do not claim that a phrase is statistically diagnostic or assign a probability that text was generated by AI.
Look for
- boilerplate transitions and throat-clearing;
- vague claims of importance instead of the actual point;
- stock or promotional language;
- stacked hedging or unnecessary qualification;
- an empty contrast, where the negated half denies a belief the reader does not hold at that point in the text: "dangerous rather than harmless", "real rather than fake", "not just X, but Y" where Y restates X. Judge it against the preceding paragraphs rather than against the phrase, since the same contrast earns its place once the text has set up the expectation it corrects, and is filler before that. Say which it is in the finding;
- repeated conclusions, formulaic openers, and overly regular paragraph shape;
- conspicuous repetition of em dashes, tricolons, or parallel constructions;
- noun-heavy wording that can be made direct without losing precision;
- (talk register only) uncontracted forms where a contraction is natural, "cannot" above all. In a journal register the uncontracted form is correct and a contraction is the defect;
- wording that conflicts with an available voice profile;
- a technical term the audience would not routinely use for that exact thing, where plainer wording would be no longer and no less accurate;
- (register- and section-dependent) a sentence that names a method where it should state what was done and what followed from it. In a journal register this applies to the Discussion and not to the Methods, where naming the method precisely enough to rebuild from is the section's whole job;
- an abstract noun standing in for the verb that matters, such as "convergence verification" for "we checked that more sample points do not change the answer"; and
- the name of a quantity offered in place of a statement about it.
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 · 93 lines · 44 tokens per session scan A dfaaa200561b
humanize-auditor is an agent published in the GitHub repository uchicago-dsi/ai-sci-skills (20 stars, last pushed 4d ago), licensed MIT. It adds 44 tokens to every session and 1,081 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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