by Blackwellboy · Agent Tool · ★ 167
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 | 167 |
| Forks | 17 |
| Language | Python |
| Category | Agent Tool |
| License | MIT |
| Quality Score | 61/100 |
| Open Issues | 20 |
| Last Updated | 2026-11-02 |
| Created | 2026-07-27 |
| Platforms | python |
| Est. Tokens | ~25k |
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.
Looking for a model-serving-minefield alternative? If you're comparing model-serving-minefield with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
LLM server for NVIDIA Blackwell only (GeForce RTX 50, RTX PRO). On one RTX 5090, Qwen3.8-27B one user: 96.3 to
From teacher to tiles — a from-scratch LLM distillation & serving engine: custom Triton/CUDA kernels, FSDP dis
Frontier-class open models on a free Kaggle TPU v5e-8: GLM-5.3-Flash 320B MoE (~64 tok/s, our own JAX engine)
An offline coding agent for local models. One command sets up the best model your GPU can run, and on 24 GB+ R
Up to 4× faster LLM decoding on Apple Silicon, lossless. Native MLX port of DeepSeek's DSpark & z-lab's DFlash
The production framework for Predictive and Generative AI. Serve any model as an API in one line, with OpenAI/
Explore other popular agent tool tools:
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: