by ARahim3 · Codex Skill · ★ 215
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
git clone https://github.com/ARahim3/kaggle-tpu-labTaken from the project's README on GitHub.
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 | 215 |
| Forks | 40 |
| Language | Python |
| Category | Codex Skill |
| License | MIT |
| Quality Score | 57/100 |
| Open Issues | 6 |
| Last Updated | 2026-09-15 |
| Created | 2026-08-31 |
| Platforms | claude-code, codex, python |
| Est. Tokens | ~13k |
kaggle-tpu-lab Run open models on Kaggle's free TPU v5e-8 and get a public endpoint that speaks the OpenAI and Anthropic APIs. Point Claude Code, Codex CLI, opencode or anything else at it. No GPU, no cloud bill, about twenty minutes from pressing Run to a URL.
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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: