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 →
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🔒 Is Claude-Code-Skills-for-Academics safe to install? View the security audit →
Claude Code Skills Personal skills repo for academic empirical finance research workflows. Setup Fresh install (empty ) Clone this repo directly into the Claude Code global skills directory: Selective install (skills directory already exists) If already contains other skills, do not clone on top of it — it will fail. Instead, clone into a temporary location and copy only the skills you want: Updating After editing skills on GitHub or locally: Skills Systematic dataset profiling protocol — un
| Stars | 150 |
| Forks | 30 |
| Language | Python |
| Category | AI Tool |
| Quality Score | 62.8582548304153/100 |
| Last Updated | 2026-08-21 |
| Created | 2026-03-26 |
| Platforms | claude-code, python |
| Est. Tokens | ~14k |
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Claude-Code-Skills-for-Academics is A collection of Skills for academics.. It is categorized as a AI Tool with 150 GitHub stars.
Claude-Code-Skills-for-Academics is primarily written in Python.
You can find installation instructions and usage details in the Claude-Code-Skills-for-Academics GitHub repository at github.com/aspi6246/Claude-Code-Skills-for-Academics. The project has 150 stars and 30 forks, indicating an active community.
The top alternatives to Claude-Code-Skills-for-Academics on Agent Skills Hub include pivotnacci, MedgeClaw, SciToolAgent. 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: