by Boom5426 · Agent Tool · ★ 579
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/Boom5426/Nature-Paper-Skills.gitTaken from the project's README on GitHub. Check the security audit before running it.
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 579 |
| Forks | 47 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 67/100 |
| Last Updated | 2026-10-13 |
| Created | 2026-04-12 |
| Platforms | python |
| Est. Tokens | ~16k |
🧬 Nature-Paper-Skills 面向 系列期刊稿件的 agent skill 仓库 从初稿搭建 · 结构修订 · 图文对齐 · 引用核验 · 投稿前预检 到 返修回复的全链路 · · 证据边界优先 简体中文 · English · 快速开始 · 技能地图 · 默认工作流 [!NOTE] 这是一个强约束的仓库，不是"通用论文写作技巧集合"。默认立场是 journal-first、claim-driven、证据边界优先；未明确 venue 时按 系列期刊导向处理。 ✨ 特性 🎯 一图一主张： 先定每张图的论点， 出图， 查正确性 🧱 结构先于润色：先用 reverse outline 稳住证据链，再做句子级 🔬 证据边界优先：Abstract / Introduction 不允许比下游证据更强 📊 统计与图注可审计： 守住独立实验单元 n、多重比较、图注统计 📎...
These tools work well together with Nature-Paper-Skills for enhanced workflows:
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AI handles execution, humans own the direction, and every run becomes an inspectable research artifact on disk
AI handles execution, humans own the direction, and every run becomes an inspectable research artifact on disk
An AI workflow skill pack for research, competitions, and innovation projects.
📄 [Skill] Vision-in-the-loop LaTeX typesetting agent — auto-compile, render, diagnose, and fix paper layouts
A desktop-first literature manager for PDF reading, translation, paper overviews, and AI agent workflows.
GitHub page for "Large Language Model-Brained GUI Agents: A Survey"
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: