by CUTEPKQ · Agent Tool · ★ 143
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 143 |
| Forks | 3 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 52/100 |
| Open Issues | 1 |
| Last Updated | 2026-07-31 |
| Created | 2026-06-13 |
| Platforms | python |
| Est. Tokens | ~13k |
NEWTON: Agentic Planning for Physically Grounded Video Generation About • Pipeline • Tools • Roadmap • Automatic Start • Quickstart • Usage • Citation https://github.com/user-attachments/assets/0334d3dd-f59a-4954-9b98-c4c268720c33 🔭 About Video generation models produce visually compelling results but systematically violate physical commonsense — on VideoPhy-2, the best model achieves only 32.6%...
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Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: