Planning skill that interrogates your use case before any code is written, then recommends the right Twilio Conversations stack — ConversationRelay, Memory, Intelligence, Orchestrator, TaskRouter — and the implementation skills to follow.
Dynamo Router Starter
Summary
NVIDIA's official skill for bringing up Dynamo inference routing — pick a router mode, enable KV-aware routing, and smoke-test the frontend endpoint before claiming anything works.
Features
- Bring-up for round-robin, KV-aware, least-loaded, device-aware and direct router modes
- Kubernetes env patch (DYN_ROUTER_MODE) instead of rewriting the deployment recipe
- Detects missing worker KV events and falls back to approximate mode explicitly
- check_router_health.py smoke test over /v1/models and one chat completion
- Refuses to report throughput gains from a smoke test — benchmarks are a separate skill
- Symptom-to-cause troubleshooting table for empty model lists, hangs and dropped port-forwards
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Skill Content
Usage Instructions
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Example Usage
"Switch our Dynamo frontend on the dynamo-demo namespace to KV-aware routing and prove the endpoint still serves a chat completion."
Description
NVIDIA Dynamo distributes inference across a fleet of workers, and its router mode is the setting that decides whether that fleet is actually being used well. Round-robin is the safe baseline; KV-aware routing sends a request to the worker most likely to already hold its prefix in cache; least-loaded and device-aware weighted trade differently again. This skill from NVIDIA's verified skills catalogue gets an agent from nothing to a working, proven routing setup.
It covers both deployment shapes. Locally, that is python3 -m dynamo.frontend --router-mode ... on a chosen HTTP port. On Kubernetes, it patches only the frontend service's environment — DYN_ROUTER_MODE — rather than rewriting a recipe, and it knows the specific trap that catches people: if the backend workers are not publishing KV cache events, KV mode will sit and wait, so the skill sets DYN_ROUTER_USE_KV_EVENTS=false for approximate mode and tells you it has done so.
The part worth having is the discipline around results. Every bring-up ends with check_router_health.py, which verifies /v1/models and issues one real chat completion. The skill then explicitly forbids drawing a throughput conclusion from that single request: mode comparisons must hold model, workers, prompt set, concurrency and sampling constant, repeated-prefix prompts are required to demonstrate KV reuse, and anything short of a proper benchmark run must be labelled a smoke comparison. It also ships a symptom-to-cause troubleshooting table — empty /v1/models, hanging KV mode, dropped port-forwards — and hands off to dynamo-troubleshoot when the endpoint is genuinely unhealthy.
Every skill in NVIDIA's catalogue ships with a verifiable NVIDIA signature (skill.oms.sig), governance metadata, and an auto-generated benchmark report. Needs Python 3.10+ with the dynamo package, and kubectl access for Kubernetes runs.
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