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 tigerless-labs · Agent Tool · ★ 204
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is paper-radar safe to install? View the security audit →
paper-radar What 28 AI labs published on arXiv, over any date range paper-radar finds the AI papers 28 labs put on arXiv, over any date range you pick. Every count splits two ways — papers the lab led, and papers it merely appears on. Method: match against ROR and email domains, using the author block read off each paper's HTML. You need it because arXiv does not carry affiliation. The field has a 1% fill rate; Semantic Scholar reaches 7%; OpenAlex returns zero for preprints. So "what did Google publish in the last two weeks" has no API that answers it. Install Hand it to your agent Paste this into Claude Code, Codex, or anything else with a shell: That is the whole install. The skill is one self-contained folder — , pure stdlib, nothing to bu
| Stars | 204 |
| Forks | 32 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 64.1316344840746/100 |
| Last Updated | 2026-09-01 |
| Created | 2026-08-04 |
| Platforms | python |
| Est. Tokens | ~6k |
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paper-radar is Finds the AI papers 28 tech companies put on arXiv over any date range, and splits lead authorship from bylines. A SKILL.md agent skill — no ML, no state, stdlib only.. It is categorized as a Agent Tool with 204 GitHub stars.
paper-radar is primarily written in Python.
You can find installation instructions and usage details in the paper-radar GitHub repository at github.com/tigerless-labs/paper-radar. The project has 204 stars and 32 forks, indicating an active community.
The top alternatives to paper-radar on Agent Skills Hub include awesome-android-agent-skills, agent-skills, claude-code-skill-factory. 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: