Individual skill
omh-inference-serving
[omh] Self-hosted LLM serving on GPUs: choose the serving engine and quantization from decision tables, prepare deployment as an idempotent runbook with observed-only verification, and measure the endpoint with the standard TTFT/TPOT/goodput protocol. Use when the user says: inference-serving, inference serving, serve this model, serve the model, model serving, serving endpoint, vllm, llama.cpp.
agent-skills/omh-inference-serving/SKILL.md
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- Last checked
- Nov 9, 2026
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- 5,268
- Repository last updated
- Nov 9, 2026
- License
- MIT
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- agent Repository topic
- ai Repository topic
- ai-agent Repository topic
- ai-tools Repository topic
- claude-code Repository topic
- codex Repository topic
- hermes-agent Repository topic
- hermes-skill Repository topic
- skills Repository topic
- devops Derived category
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SKILL.mdcore · 8,248 bytesreferences/serving-bench.mddoc · 2,279 bytesreferences/serving-runbooks.mddoc · 3,547 bytes
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# Inference Serving
This is an OMH `inference-serving` workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).
## Why This Exists
`inference-serving` exists so serving an LLM runs as one decided, gated, measured process instead of scattered flag folklore: the engine choice is a table, the deployment is an idempotent runbook whose only completion evidence is the observed verification, and the benchmark speaks the standard metric vocabulary.
## Do Not Use When
- A new model generation needs recognition, calibration, routing, and pricing onboarding.
- The user wants their own machine's model routing or providers configured.
- The question is whether a coding runtime/executor can run at all.
- The goal is application or system performance rather than the serving endpoint itself; use `ulw-perf`.