academic-humanizer

     

Why we built this

Some of us write a lot of papers and grant proposals, and our team started using AI to help with drafts. The catch is that AI writing is easy to spot: the "In recent years..." openers, the puffed-up phrasing, the very long sentences, the em-dashes. Reviewers pick up on it.

There are tools called "humanizers" that try to remove that AI flavor, but they're made for blogs and marketing. Run one on a paper or an NSF proposal and it cuts the precision along with the AI flavor. The careful wording academic writing depends on is the first thing to go.

So we put together our own for the group. To get the rules, 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's nothing fancy, and it isn't about gaming review or adding fake novelty. We just wanted AI-polished drafts to still read like a person wrote them, with the numbers, citations, and claims left alone.

See it work

[!CAUTION] Before (AI-generated, every tell present):

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 (the AI tells are gone; the vision and ambition stay):

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.

What it does

• Removes the usual AI tells: "paves the way", "extensive experiments", "to the best of our knowledge", "In recent years...", delve/underscore/tapestry, rule-of-three, very long sentences, and em-dashes.
• 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.

Install

git clone https://github.com/AIScientists-Dev/academic-humanizer ~/.claude/skills/academic-humanizer

It 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.

Use

/academic-humanizer
[paste a section, or point at main.tex]
# optionally: "match my voice from prior_paper.pdf; target venue: ICLR"

How it works

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.

References

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)

Acknowledgments

• 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 de-AI-ifies existing academic prose and matches claims to evidence while preserving scholarly voice.

License

MIT.