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 Imbad0202 · Codex Skill · ★ 11.0k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is academic-research-skills-codex safe to install? View the security audit →
Academic Research Skills for Codex Codex-native packaging of the Academic Research Skills suite. This is the sibling Codex distribution of Academic Research Skills for Claude Code. This repository vendors the ARS workflow content as a single Codex skill: The original Claude Code ARS checkout is not modified. Upstream content is copied from fresh GitHub clones and adapted through the Codex router in . Claude Code Version This repository is the Codex package. For the original Claude Code version of Academic Research Skills, use Imbad0202/academic-research-skills. Use the Claude Code repo when you want the native Claude Code skill layout, Claude-specific agent-team behavior, or the original ARS developm
| Stars | 11,004 |
| Forks | 481 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 69.9131263163624/100 |
| Last Updated | 2026-09-16 |
| Created | 2026-04-25 |
| Platforms | codex, python |
| Est. Tokens | ~17k |
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academic-research-skills-codex is Codex-native Academic Research Skills suite for human-in-the-loop academic research workflows. It is categorized as a Codex Skill with 11.0k GitHub stars.
academic-research-skills-codex is primarily written in Python. It covers topics such as academic-pipeline, academic-research, academic-writing.
You can find installation instructions and usage details in the academic-research-skills-codex GitHub repository at github.com/Imbad0202/academic-research-skills-codex. The project has 11.0k stars and 481 forks, indicating an active community.
The top alternatives to academic-research-skills-codex on Agent Skills Hub include humanizer, claude-skills, Auto-claude-code-research-in-sleep. 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: