serving-systems

serving-systems is an MLOps & model serving skill for AI agents, published by uw-syfi in the vibesys repository on GitHub. Skillspace rates serving-systems D (65/100) and it passed the safety scan; the rating is based on static analysis only.

LLM and multimodal serving-system development. Activate when a task touches inference servers, latency / throughput / TTFT / TPOT, KV-cache, batching, attention kernels, CUDA graphs, speculative decoding, structured / grammar-constrained output, quantization, MoE serving, prefix caching, multi-modal (vision/speech/image/video) serving, porting a model to vLLM / SGLang / TensorRT-LLM, or serving on AWS Trainium (Neuron, torch-neuronx, NKI kernels).

Grade
D
Quality
65 / 100
Safety
Passed
GitHub stars
84

Unproven: rated by static analysis only, not yet compared head-to-head.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

View source on GitHubuw-syfi/vibesys

Our review

Architecture
78 / 100
Maturity
Structured: ships references or scripts
Security scan
Low-risk patterns
License
MIT
Adoption
Adopted by 0 other repositories, in 0 collections

What the scanner flagged

  • mentions secrets in text

Not checked yet

  • Not judged on the rubric yet (not a contender in its category)
  • No blind task eval yet
  • Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
  • Sibling files (scripts, references) not fetched; security scan covers SKILL.md only

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