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 Blackwellboy · Agent Tool · ★ 132
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is model-serving-minefield safe to install? View the security audit →
Model Serving Minefield You just lost hours to a "model problem" that turned out to be a serving or config bug. You are not crazy, and you are not the first: this is the registry where those bugs live, so the next person loses minutes instead of an evening. Every entry here produced a confidently wrong measurement on a real serving path: chat templates, tool parsers, reasoning fields, quantization kernel paths, container toolchains, memory allocation, eval harnesses, versioning. The common shape: the request looks correct, the response looks correct, and the number is still wrong, because something happened between the two that nobody inspected. Request-shaped checks cannot catch any of these. Each entry leads with the symptom you would actually observe, then the mechanism, the stacks and builds it bit, the check that catches it, and the fix.
| Stars | 132 |
| Forks | 16 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 58.8458730596341/100 |
| Open Issues | 9 |
| Last Updated | 2026-09-18 |
| Created | 2026-07-27 |
| Platforms | python |
| Est. Tokens | ~25k |
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model-serving-minefield is Community registry of LLM serving-path traps that produce confidently wrong measurements: templates, tool parsers, reasoning fields, quant kernel paths, CUDA toolchains, KV allocation, eval harnesses,. It is categorized as a Agent Tool with 132 GitHub stars.
model-serving-minefield is primarily written in Python. It covers topics such as benchmarking, chat-template, cuda.
You can find installation instructions and usage details in the model-serving-minefield GitHub repository at github.com/Blackwellboy/model-serving-minefield. The project has 132 stars and 16 forks, indicating an active community.
The top alternatives to model-serving-minefield on Agent Skills Hub include tessera, kaggle-tpu-lab, Deskdrop. 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: