humanize

humanize is a skill for Claude Code from Aperivue/medsci-skills. It costs 57 tokens per session (4,605 once invoked), scanned A, original, MIT.

An academic writing editor that looks for common patterns associated with AI-generated prose in medical manuscripts and replies to peer reviewers. It rewrites flagged passages while preserving technical claims, numbers, and citations.

In plain words
What is it for?
Reviewing and revising academic manuscripts and response letters, especially when the writer wants to remove repetitive or unnatural AI-style wording.
Why use it?
It helps make drafts sound more natural without changing their scientific meaning. It also limits revisions so unrelated parts of the document are not rewritten.

Skill for Claude Code

Written for Claude Code: ${CLAUDE_SKILL_DIR} variable. Also seen: model in frontmatter; reads .claude/ paths.

Part of the medsci-writing plugin — 7 skills shipped together

Good fit Reviewing and revising academic manuscripts and response letters, especially when the writer wants to remove repetitive or unnatural AI-style wording.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/aperivue/medsci-skills/humanize
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.

Any agent
npx skills add Aperivue/medsci-skills --skill humanize
Clone the repo
git clone --depth 1 https://github.com/Aperivue/medsci-skills

Made for: Claude Code.

Or install medsci-writing, the plugin that ships this one along with the rest of its 7 skills.

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

README.md
[![agentmods](https://agentmods.dev/badge/skills/aperivue/medsci-skills/humanize.svg)](https://agentmods.dev/skills/aperivue/medsci-skills/humanize)
Your own site
<a href="https://agentmods.dev/skills/aperivue/medsci-skills/humanize"><img src="https://agentmods.dev/badge/skills/aperivue/medsci-skills/humanize.svg" alt="Measured on agentmods" height="20"></a>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,605 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00057 $0.04605
Opus 5 $0.00028 $0.02302
Sonnet 5 $0.00011 $0.00921
Haiku 4.5 $0.00006 $0.00460

Measured 8d ago against content hash 0372a021d140, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

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 8d ago.

The scan reads SKILL.md. This mod also ships 4 executable files (scripts/check_rewrite_fidelity.py, scripts/check_sentence_variety.py, tests/test_rewrite_fidelity.sh, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/humanize/SKILL.md · 296 lines

How it starts

The opening of the file, as written. The whole thing — 296 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Humanize Skill

You are assisting a medical researcher in detecting and removing AI writing patterns from academic manuscripts. Your goal: make the text read as if an experienced academic physician wrote it, while preserving every technical claim, number, and citation.

Communication Rules

  • Communicate with the user in their preferred language.
  • All manuscript edits are in English.
  • Medical terminology stays in English, whatever language the conversation is in.

Reference Files

  • Pattern reference: ${CLAUDE_SKILL_DIR}/references/ai_patterns.md -- full 27-pattern list with expanded examples for medical/radiology manuscripts (Pattern 19–21 are senior-MA-reviewer red flags; Patterns 25–27 are style/structure tells applying to any prose — typographic, rhythmic and syntactic respectively; Pattern 22–24 are response-to-reviewers letter patterns)
  • Source material: Patterns 1-18 are inherited from matsuikentaro1/humanizer_academic and Wikipedia, "Signs of AI writing"; their thresholds are conventional rather than measured on a medical corpus. Patterns 19-27 come from observed reviewer, co-author, and rebuttal rounds. references/ai_patterns.md records the grounding per pattern.

Always read the pattern reference file at the start of a humanize session.


Workflow

Phase 1: Scan

Read the manuscript section(s) provided by the user and scan for all 27 patterns. For response-to-reviewers letters and cover letters, prioritise patterns 22-24.

For each pattern found:

  1. Record the pattern number and name.
  2. Count occurrences.
  3. Extract the exact passage from the text.
  4. Note the location (paragraph number or line range).

Output: Pattern Frequency Table

## AI Pattern Scan Report

Section: {section name}
Word count: {N}

| # | Pattern | Count | Severity | Example from text |
|---|---------|-------|----------|-------------------|
| 1 | Significance inflation | 3 | HIGH | "...pivotal role in diagnostic imaging..." |
| 7 | AI vocabulary words | 5 | HIGH | "Additionally,...", "crucial finding..." |
| 8 | Copula avoidance | 2 | MEDIUM | "...serves as the gold standard..." |
| ... | ... | ... | ... | ... |

Patterns not detected: 2, 4, 9, 14, 15

Total AI pattern instances: {N}
AI pattern density: {N per 1000 words}

Read the full file on GitHub · 296 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. 8d ago First seen · 296 lines · 57 tokens per session scan A 0372a021d140

Subscribe to this mod's changes

humanize is a skill published in the GitHub repository Aperivue/medsci-skills (287 stars, last pushed yesterday), licensed MIT. It adds 57 tokens to every session and 4,605 once invoked, about $0.0003 per session on Opus 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.

Related

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