No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by Azhi-ss · Agent Tool · ★ 110
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is academic-figure-skills safe to install? View the security audit →
Academic Figure Skills Academic paper figure skills for Claude Code, Cursor, Codex & Gemini CLI. AI 驱动的学术论文配图技能包：仓库分析 → 配图规划 → 色盲友好配色 → JSON 配图规范 / 现代 pastel 提示词。 是什么？ 7 个可独立安装的 agent skill，覆盖「分析代码仓库/论文 → 规划配图 → 选配色 → 生成图片 prompt → 调用生图模型」全链路。支持 classic 框线学术图（CVPR/Nature）和 pastel 柔彩图（ICLR/NeurIPS 2024-2025）两种风格。10 个真实仓库基准验证。 7 skills · palette SSOT · classic / pastel · 10 real-repo benchmarks · Install: 快速开始（30 秒） 然后对 agent 说： 或 先看有哪些 skill（不安装）： 示例配图 以下为使用本技能包生成提示词后创建的学术配图示例： Claude Opus 4.6 提示词 + Gemini NanoBanana2Flash 豆包 2.0 Pro 提示词 + Gemini NanoBanana2Flash <img src="docs/images/
| Stars | 110 |
| Forks | 10 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 65.5368897749709/100 |
| Last Updated | 2026-09-14 |
| Created | 2026-04-09 |
| Platforms | claude-code, python |
| Est. Tokens | ~15k |
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academic-figure-skills is AI skills for academic paper figures: repo analysis, figure planning, colorblind palettes, JSON figure specs. Install: npx skills add Azhi-ss/academic-figure-skills. It is categorized as a Agent Tool with 110 GitHub stars.
academic-figure-skills is primarily written in Python. It covers topics such as academic-writing, agent-skills, claude-code.
You can find installation instructions and usage details in the academic-figure-skills GitHub repository at github.com/Azhi-ss/academic-figure-skills. The project has 110 stars and 10 forks, indicating an active community.
academic-figure-skills is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to academic-figure-skills on Agent Skills Hub include FigMirror, econ-writing-skill, claude-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. 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: