vllm-bench-serve
vllm-bench-serve is an MLOps & model serving skill for AI agents, published by ascend-ai-coding in the awesome-ascend-skills repository on GitHub. Skillspace rates vllm-bench-serve C (72/100) and it passed the safety scan; the rating is based on static analysis only.
Interactive online benchmark orchestrator for vLLM inference services using `vllm bench serve`. Supports single benchmarks, multi-case batch execution with result aggregation, and auto-optimization to find optimal concurrency/throughput under latency SLO constraints (TTFT, TPOT, P99, success rate). Use this skill whenever the user wants to benchmark, stress test, or measure performance of a running LLM/multimodal/embedding inference service — even if they don't say "vllm bench serve" explicitly. Do NOT use for offline inference throughput, service deployment/startup, profiling/tracing, health checks only, or analyzing existing benchmark results without running new tests.
- Grade
- C
- Quality
- 72 / 100
- Safety
- Passed
- GitHub stars
- 136
Unproven: rated by static analysis only, not yet compared head-to-head.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 84 / 100
- Maturity
- Structured: ships references or scripts
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
- Adoption
- Adopted by 0 other repositories, in 0 collections
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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