What does this skill do, and when should you use it?
Academic Humanizer is an editing-focused Agent Skill for AI-assisted academic prose (papers, theses, rebuttals) and funding proposals (NSF Project Summary/Description, NIH Specific Aims). It works in six layers: a general AI-tell catalog, academic-specific tells, protected scholarly conventions, claim-to-evidence matching, author voice and venue calibration, and a dedicated proposal mode. It never changes a number, result, or citation, and it is explicitly not designed to evade AI-use disclosure. It suits researchers who draft with AI but refuse to let their manuscripts read like everyone else's.
Reads the manuscript (paper sections, abstracts, rebuttals, or NSF/NIH proposal pages) and an optional writing sample of the author's prior work; audits first, listing each detected pattern with its location and each empirical claim's evidence status; then rewrites under the six-layer rules, checking that every claim is backed by a number, figure, or citation and that no verb outruns its evidence. Proposal mode additionally enforces claim-to-feasibility matching and first-page primacy. Output is the cleaned text plus a short change log (patterns removed, claims softened or given evidence pointers, voice notes).
- A PhD student or postdoc whose AI-drafted paper section needs 'paves the way' / 'delve' tells removed and over-claims matched to Table 3 before submission
- A faculty member preparing an NSF CAREER application who must polish a one-page Project Summary with Overview / Intellectual Merit / Broader Impacts headings
- An applicant writing an NIH R01 Specific Aims page who wants the central hypothesis checked for falsifiability and aims phrased as parallel, independently valuable
- A student polishing a rebuttal who wants polite hedging preserved while only generic AI boilerplate is stripped
- A PI standardizing a lab's writing style by feeding prior accepted papers as voice samples
How do you install this skill?
- This is a static source-only review; nothing was executed and confidence is low.
- SKILL.md version 0.3.3 conflicts with the README badge (0.3.2) and there is no changelog; note the versioning gap.
- Repository license metadata is NOASSERTION while the LICENSE file is MIT; verify the license labeling on distribution channels.
- The skill rewrites manuscript files directly and does not require pre-edit confirmation or describe rollback; run on a copy first.
- The skill targets English academic writing only; no Chinese support. Core function has no overseas-service dependency and is usable offline from mainland-China networks.
- No tests or CI evidence; the guarantee that numbers and citations are preserved rests solely on instructions—verify manually after each use.
- Shell / CLI
- Local filesystem
git clone https://github.com/AIScientists-Dev/academic-humanizer ~/.claude/skills/academic-humanizer — it is a plain SKILL.md plus examples, so Claude Code picks it up directly; Codex and MorphMind users can point their agent at SKILL.md as a skill or system prompt.
git clone --depth 1 https://github.com/AIScientists-Dev/academic-humanizer.git ~/.claude/skills/academic-humanizerGenerated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- match my voice from prior_paper.pdf; target venue: ICLR
In Claude Code, enter: /academic-humanizer, then paste a section or point at main.tex; optionally add "match my voice from prior_paper.pdf; target venue: ICLR". The skill runs an audit pass, then rewrites, then returns a change report.
What are this skill's strengths and limitations?
- Six layers cover the full editing pipeline from generic AI tells to NSF/NIH structure, with abundant real before/after examples including a funded NSF CAREER summary
- Explicitly preserves legitimate scholarly constructs (calibrated hedging, passive voice, 'we', definitions and symbols), avoiding the flattening that generic humanizers cause
- Strict never-touch policy on numbers, equations, and citations, plus claim-evidence matching on every sentence
- MIT-licensed ruleset designed to be forked and personalized to your own voice and field
- Voice matching depends on the model's judgment; there is no automated evaluation of fidelity to the author's style
- The GitHub license field reads NOASSERTION (README and SKILL.md state MIT); verify licensing yourself
- Rules reflect one research group's voice and may not fit every discipline or non-English writing
- Layer 6 distills stable NSF/NIH structure; current binding requirements (page limits, formatting) must still be checked against official sources
- No test suite; effectiveness is demonstrated by examples rather than benchmarks
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Academic Humanizer this page | 55 · Use with care | ★ 2.1k | 3mo ago | NOASSERTION |
| Multi-Perspective Academic Paper Review Simulator | 52 · Use with care | ★ 52k | 12d ago | NOASSERTION |
| OfficeCLI Academic Paper Skill | 51 · Use with care | ★ 32k | 12d ago | Apache-2.0 |
| Deep Research Agent Team | 49 · Use with care | ★ 52k | 12d ago | NOASSERTION |
| Academic Paper Writing Expert Team | 0 · Not recommended | ★ 52k | 12d ago | NOASSERTION |
Compared with blader/humanizer (MIT, general AI-tell removal for blog and casual text), this skill reuses that Layer 1 catalog but extends it with academic evidence discipline and a proposal mode. Compared with koaeraser/ARMS (an autonomous idea-to-manuscript pipeline for statistics/methodology papers), this is the narrower, single-pass editing tool.
How did FollowSkills review this skill?
SKILL.md declares a least-privilege toolset (Read/Write/Edit/Grep/Glob/AskUserQuestion), an audit-before-edit workflow with a change report, and README states clear ethical boundaries (no altering numbers/citations, not for evading AI disclosure); data flow is largely transparent. Deducted for: no explicit user confirmation gate before writes, no rollback mechanism described, and unverified publisher identity.
Instructions are self-consistent: six layers, audit→rewrite→report loop, preserve-rules do not contradict fix-rules, examples cover major patterns. Deducted for: no tests, thin handling of abnormal inputs (missing author sample, non-English text, malformed files), and weak failure feedback; static read caps score at 10.
Use and non-use boundaries are explicit (academic papers and NSF/NIH proposals, not blogs/marketing), trigger description is precise, and NSF/NIH structural knowledge is concrete. Deducted for: English-only scope with no Chinese-language support and no declared environment-fit notes, limiting fit for Chinese-language users.
Docs are well layered (frontmatter, layered body, README, examples, upstream attribution, MIT LICENSE), progressive disclosure is reasonable. Deducted for: version mismatch (SKILL.md 0.3.3 vs README badge 0.3.2), no changelog, unclear maintenance ownership/update path, and license metadata reported as NOASSERTION.
The core task (strip AI tells, preserve scholarly conventions, claim-evidence alignment) has a clear path and concrete before/after examples; marginal value over generic humanizers is well argued. Deducted for: no executed verification, output quality depends on model compliance, and the referenced examples file was not available in this review.
Auditable primary material exists: layered rules, before/after examples, upstream acknowledgments (blader/humanizer, ARMS), official NSF/NIH references, with claims vs. inference separated. Deducted for: no test suite, no CI, no independently reproducible results; evidence types are thin, and the static cap is 5.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →