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.
npx skills add Yila-AI/awesome-research-skills --skill academic-humanizergit clone --depth 1 https://github.com/Yila-AI/awesome-research-skillsWrote 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.
[](https://agentmods.dev/skills/yila-ai/awesome-research-skills/academic-humanizer)<a href="https://agentmods.dev/skills/yila-ai/awesome-research-skills/academic-humanizer"><img src="https://agentmods.dev/badge/skills/yila-ai/awesome-research-skills/academic-humanizer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yila-ai/awesome-research-skills/academic-humanizer"><img src="https://agentmods.dev/badge/skills/yila-ai/awesome-research-skills/academic-humanizer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00078 | $0.01340 |
| Opus 5.5 | $0.00031 | $0.00536 |
| Sonnet 5.5 | $0.00016 | $0.00268 |
| Haiku 4.5 | $0.00008 | $0.00134 |
Grade A, and why
academic-humanizer 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 1mo ago.
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.
How it starts
The opening of the file, as written. The whole thing — 139 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Academic Humanizer
Remove the template voice, not the scholarship. Make academic prose more specific, natural, and recognizably the author's while preserving the research exactly.
This Skill improves writing quality. It does not certify that text is human-written, optimize for an AI detector, or remove an author's obligation to disclose AI assistance under the relevant journal, institution, or funder policy.
Load only what is needed
- Always read
references/preservation-and-evidence.md,references/pattern-catalog.md, andreferences/output-contract.md. - Read
references/voice-calibration.mdonly when the author provides writing samples or explicitly asks to match their voice. - When source and revision are available as local text, use
scripts/check_invariants.pyas a deterministic first check. A passing result never replaces the semantic audit.
Use this Skill when
The user asks to:
- remove “AI味”, AI-like phrasing, template language, or generic LLM prose from academic text;
- make a paper sound less mechanical, less inflated, or more like the author;
- audit an abstract, paper section, thesis passage, rebuttal, or reviewer response for recurring AI-writing patterns;
- calibrate a draft against the author's prior academic writing.
For ordinary Chinese-to-English translation, grammar correction, or general publication polishing without a de-templating or voice-matching goal, sci-ssci-polishing is the better entry point.
Inputs
Required:
- the passage or document to audit or revise.
Useful but optional:
- language, field, section type, and target venue;
- one or more genuine writing samples from the author;
- protected terminology or phrases;
- desired intensity: audit only, standard, or strong.
Do not block on optional context. Without author samples, use a neutral, precise, venue-appropriate academic voice and state that personal voice matching was not performed.
Workflow
1. Classify the task
Identify:
- language: Chinese, English, or mixed;
- scope: passage, paragraph set, section, or full document;
- rhetorical function: Abstract, Introduction, Methods, Results, Discussion, Conclusion, literature review, rebuttal, or mixed;
- requested mode: audit only, standard rewrite, strong rewrite, or voice matching.
What ships with it
7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 139 lines · 78 tokens per session scan A 7fa73c713b80
academic-humanizer is a skill published in the GitHub repository Yila-AI/awesome-research-skills (139 stars, last pushed 6d ago), licensed Apache-2.0. It adds 78 tokens to every session and 1,340 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-05.
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