/academic-humanizer

name: academic-humanizer
version: 0.3.3
description: |
  Improve the clarity and voice of AI-assisted academic writing (papers, theses, rebuttals) and
  funding proposals (NSF Project Summary/Description, NIH Specific Aims): preserve scholarly
  conventions, match claims to evidence (and, for proposals, claims to feasibility), and match the
  author's own voice. It never changes a number, result, or citation, and it is not for evading
  AI-use disclosure. Use when editing AI-assisted academic prose or grant proposals.
license: MIT
compatibility: claude-code codex morphmind opencode
allowed-tools: [Read, Write, Edit, Grep, Glob, AskUserQuestion]

Academic Humanizer

Improve the clarity and voice of AI-assisted *academic* writing while keeping the precise, evidence-bound voice that scholarship requires and matching the author's own style. It preserves every number, result, and citation, and it is not a tool for evading AI-use disclosure.

When to use

Editing or reviewing academic prose: paper sections, abstracts, rebuttals, related work, and **funding proposals** (NSF Project Summary/Description, NIH Specific Aims, fellowship and foundation proposals; see Layer 6). **Not** for blogs, marketing, or personal essays, and **never** inject opinion, humor, or first-person "personality" into a manuscript. For technical writing, neutral and precise *is* the human voice. One caveat for proposals: their register is different from a paper's, since they are sold on vision and feasibility, so the ambition language a paper would trim is appropriate there; apply Layer 6, not the paper layers' stricter trimming, to vision statements.

Core principle

Academic writing already has a correct human voice: neutral, precise, third-person plural ("we"), every claim tied to its evidence. The job is to (1) strip the AI *tells* without casualizing, and (2) enforce the discipline a general humanizer misses: **every claim earns its number, figure, or citation, and no verb is stronger than its evidence.**

Process

1. **Read** the manuscript and any author writing sample; note the document type (paper vs. funding proposal) and the target venue or funding agency. For proposals, also apply Layer 6 and preserve appropriate vision. 2. **Audit** (do not edit yet): list each detected pattern with its location and proposed fix, and each empirical claim's evidence status. 3. **Rewrite**: same structure and content, all claims and citations preserved, tells removed, over-claims matched to evidence, legitimate hedging kept. 4. **Report**: cleaned text plus a short change log (patterns removed, claims softened or given evidence pointers, voice notes). Cover everything the original covered: if it had five paragraphs, so does the rewrite.

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Layer 1: General AI-tell catalog

Scan for and fix the general patterns, subject to the academic exceptions in Layer 3: inflated significance ("marking a pivotal moment"); superficial "-ing" tails that fake depth ("..., highlighting..."); promotional/figurative language ("rich", "vibrant", "groundbreaking"); vague attributions ("experts argue" with no cite); AI vocabulary (*delve, underscore, intricate, tapestry, testament, landscape (abstract), pivotal, showcase, foster, leverage (filler), realm, seamless*); copula avoidance ("serves as" -> "is"); negative parallelisms ("not just X, but Y"); rule-of-three padding; elegant variation (cycling synonyms for one referent); filler ("it is worth noting that", "in order to"); **overlong, clause-stacked sentences (split them; see 2.11)**; and **em-dashes (remove entirely; recast with commas, colons, parentheses, or separate sentences)**.

**Before:** *Additionally, an enduring testament to the method's value is its ability to delve into intricate dependencies, showcasing a seamless integration that underscores its pivotal role.* **After:** *The method also captures higher-order dependencies, which the baselines miss (Table 2).*

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Layer 2: Academic AI tells (remove or fix)

2.1 Over-claiming verbs

Empirical work *shows* and *provides evidence*; it does not *prove* or *demonstrate* universal truths. **Watch:** demonstrate, prove, establish, confirm, guarantee; "significantly" with no test/number. **Before:** *We prove that our method significantly outperforms all prior approaches.* **After:** *Our method improves held-out accuracy by 4--7 points over the strongest prior approach (Table 3); the gain is significant at p < 0.01 by a paired test.*

2.2 Significance hype

**Watch:** paves the way for, a crucial/pivotal step toward, has the potential to revolutionize, opens new avenues, sheds light on, of paramount importance, bridges the gap. **Before:** *This work paves the way for a new paradigm and sheds light on a problem of paramount importance.* **After:** *This work addresses one failure mode of prior methods: error accumulation under long-horizon rollout (Section 4).*

2.3 Empty intensifiers

**Watch:** extensive/comprehensive/thorough experiments, a wide range of, numerous, various. **Before:** *We conduct extensive experiments on a wide range of datasets.* **After:** *We evaluate on three datasets (ImageNet, CIFAR-100, and iNaturalist).*

2.4 Novelty padding

**Watch:** "novel" used more than once per section; "to the best of our knowledge"; "for the first time". **Before:** *We propose a novel framework and, to the best of our knowledge, are the first to study this.* **After:** *We study online calibration under delayed labels, which prior calibration work (offline) does not address.*

2.5 Formulaic openers

**Watch:** "In recent years, X has attracted increasing attention"; "With the rapid development of..."; "Despite recent advances,...". **Before:** *In recent years, tabular deep learning has attracted increasing attention.* **After:** *Tabular deep learning has a structural limitation: most models discard feature-type metadata and must relearn it from data.*

2.6 Connective ove