Aut_Sci_Write — security grade SAFE, quality 65/100

Security audit verdict: SAFE · quality 65/100

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 ShZhao27208 · Codex Skill · ★ 180

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

🔒 Is Aut_Sci_Write safe to install? View the security audit →

About Aut_Sci_Write

AutSciWrite Autonomous Scientific Writer A modular Claude Code Skills suite for the full academic research lifecycle 🌐 Live Demo · 📦 Install · 📖 中文说明 English AutSciWri

academic-researchagentclaude-codecodexllm-toolsopenclawopencodepptxskillszotero

Quick Facts

Stars180
Forks25
LanguagePython
CategoryCodex Skill
Quality Score65.2378396350999/100
Open Issues3
Last Updated2026-08-13
Created2026-04-06
Platformsclaude-code, codex, node, python
Est. Tokens~17k

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Frequently Asked Questions

What is Aut_Sci_Write?

Aut_Sci_Write is Academic research skills suite for AI Agent — literature search/download (WoS+Elsevier+Springer), PDF extraction, figure cropping, review writing, Zotero sync, and PPT/Html generation.. It is categorized as a Codex Skill with 180 GitHub stars.

What programming language is Aut_Sci_Write written in?

Aut_Sci_Write is primarily written in Python. It covers topics such as academic-research, agent, claude-code.

How do I install or use Aut_Sci_Write?

You can find installation instructions and usage details in the Aut_Sci_Write GitHub repository at github.com/ShZhao27208/Aut_Sci_Write. The project has 180 stars and 25 forks, indicating an active community.

What are the best alternatives to Aut_Sci_Write?

The top alternatives to Aut_Sci_Write on Agent Skills Hub include memorix, OpenContext, code-on-incus. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

View on GitHub → Browse Codex Skill tools