by dstackai · Agent Tool · ★ 2.3k
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
uv tool install "dstack[all]" -U
uv tool install dstack -UTaken from the project's README on GitHub. Check the security audit before running it.
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. Full audit →
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 | 2,305 |
| Forks | 273 |
| Language | Python |
| Category | Agent Tool |
| License | MPL-2.0 |
| Quality Score | 68/100 |
| Open Issues | 65 |
| Last Updated | 2026-11-06 |
| Created | 2022-01-04 |
| Platforms | docker, k8s, python |
| Est. Tokens | ~14k |
is a unified control plane for GPU provisioning and orchestration that works with any GPU cloud, Kubernetes, or on-prem clusters. It streamlines development, training, and inference, and is compatible with any hardware, open-source tools, and frameworks. Accelerators supports , , , , and accelerators out of the box.
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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: