Some of us write a lot of papers and grant proposals, and our team started using AI to help with drafts. The problem is that AI-assisted drafts come out generic and verbose, with "In recent years..." openers, inflated phrasing, and over-long sentences. They also drift from the author's own voice and lose the precision scholarship depends on.
There are tools called "humanizers," but they are built for blogs and marketing. Run one on a paper or an NSF proposal and it flattens the precision along with everything else. The careful wording academic writing depends on is the first thing to go.
So we put together our own. To calibrate it, we had the AI compare its own drafts with our team's accepted papers and funded proposals, and we went through the differences by hand. It is nothing fancy, and it is not about gaming review, defeating detectors, or adding fake novelty. We wanted AI-assisted drafts to read clearly and in the author's own voice, with the numbers, citations, and claims left exactly as written.
This is an editing aid for clarity and voice, calibrated to an author's own prior accepted work. It does not generate findings, invent data, or change citations, and it is not designed to evade AI-use detection. Using it does not remove your obligation to disclose AI assistance: always follow the disclosure policy of the venue you submit to.
Caution
Before (a generic AI draft):
In recent years, continual learning has attracted increasing attention and achieved remarkable success. However, existing methods still face crucial challenges. In this proposal, we propose a novel framework that leverages cutting-edge techniques to delve into these intricate problems, paving the way for a transformative paradigm that will revolutionize the field.
Tip
After (clear, in the author's voice, with claims tied to evidence):
Continual learning matters, but today's methods stay empirical and their principles are unclear. That limits reliability and progress. This proposal builds a principled framework on three fronts: adaptation, soft supervision, and cross-domain knowledge. We demonstrate it on autonomous driving and network management.
More before/after passes are in examples/before-after.md: a general
example, an NIH Specific Aims page, and a funded NSF CAREER summary.
- Sharpens clarity and voice: trims generic AI phrasing ("paves the way", "extensive experiments", "to the best of our knowledge", "In recent years...", delve/underscore/tapestry, rule-of-three, very long sentences, em-dashes) and brings the draft closer to the author's own style.
- Keeps claims tied to evidence: no verb stronger than the data (
prove→show empirically), and vague magnitudes become attributed ranges. - Leaves real scholarship alone: evidence-tied hedging, passive voice where it fits,
we, definitions, symbols, and every citation. It doesn't change a number or a reference. - Has a separate mode for grant proposals (NSF, NIH): it keeps the vision a paper would trim, and spends most of the effort on the first pages, since that's what reviewers score.
git clone https://github.com/AIScientists-Dev/academic-humanizer ~/.claude/skills/academic-humanizerIt is a plain SKILL.md plus examples, so it also runs as a skill or system prompt for Codex and
MorphMind. Point your agent at SKILL.md.
/academic-humanizer
[paste a section, or point at main.tex]
# optionally: "match my voice from prior_paper.pdf; target venue: ICLR"
The rules here reflect one group's voice. Fork the repo and adapt them to your own: point it at a few of your past papers, keep the checks that fit your field, and adjust the rest. It is meant to be personalized, not a one-size-fits-all filter.
Six layers: general AI-tell catalog → academic-specific tells → preserve scholarly conventions →
claim↔evidence matching → voice/venue calibration → funding-proposal mode (NSF/NIH structure,
first-page primacy, claim↔feasibility). The audit→rewrite loop is defined in SKILL.md.
Layer 6 distills the stable structure of NSF and NIH proposals. For current, binding requirements (page limits, formatting, deadlines), consult the source:
- NSF: Proposal & Award Policies & Procedures Guide (PAPPG)
- NSF: CAREER program
- NIH: Write Your Application (Specific Aims, Significance, Innovation, Approach)
- blader/humanizer (MIT). Focus: removing general AI-writing patterns for blog, casual, and encyclopedic text. This skill reuses its general AI-tell catalog (Layer 1) and extends it for academic prose.
- koaeraser/ARMS. Focus: an autonomous pipeline for statistics/methodology research papers (idea → validated, revised manuscript). A complementary, broader-scope project that informed the claim-evidence and numerical-precision emphasis here.
This skill is the narrower piece: a single-purpose editing pass that sharpens clarity and matches claims to evidence while preserving the author's scholarly voice.
MIT.