humanize-auditor

humanize-auditor is an agent for Claude Code from uchicago-dsi/ai-sci-skills. It costs 44 tokens per session (1,081 once invoked), scanned A, original, MIT.

A read-only prose review agent for the humanize skill, which edits writing to sound more natural and use clearer language. It identifies formulaic wording, vague phrases, and technical terms that the intended reader may not understand.

In plain words
What is it for?
Use it to inspect drafts before editing, especially when the audience, tone, or technical vocabulary needs careful attention.
Why use it?
It gives the editing agent specific problems to fix without changing the source itself. This helps preserve the author's voice while making text easier to read.

Agent for Claude Code

Written for Claude Code: a Claude Code subagent (agents/*.md). Also seen: model in frontmatter.

Good fit Use it to inspect drafts before editing, especially when the audience, tone, or technical vocabulary needs careful attention.

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Install with agentmods
npx agentmods add agents/uchicago-dsi/ai-sci-skills/humanize-auditor
Install

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.

Clone the repo
git clone --depth 1 https://github.com/uchicago-dsi/ai-sci-skills

Made for: Claude Code.

Wrote 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.

agentmods badge for humanize-auditor

README.md
[![agentmods](https://agentmods.dev/badge/agents/uchicago-dsi/ai-sci-skills/humanize-auditor/github.svg)](https://agentmods.dev/agents/uchicago-dsi/ai-sci-skills/humanize-auditor)
Your own site
<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.

agentmods 80×15 button for humanize-auditor

Your own site · 80×15
<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>
Per session 44 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,081 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 1mo ago against content hash dfaaa200561b, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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.

agents/humanize-auditor.md · 93 lines

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.

Read the full file on GitHub · 93 lines

Changes

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.

  1. 1mo ago First seen · 93 lines · 44 tokens per session scan A dfaaa200561b

Subscribe to this mod's changes

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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