Skip to content
mcp/skillhub

Agent Skill

Dynamo Router Starter skill

dynamo-router-starterNVIDIA/skillsskills/dynamo-router-starter/SKILL.md

Start or patch Dynamo router modes and run router endpoint smoke checks. Use for round-robin, KV-aware, least-loaded, or device-aware routing setup; use recipe-runner for recipe deployment and troubleshoot for failure diagnosis.

3.7kApache-2.0

always on

50 tokname + description

once used

1,292 tokSKILL.md body

bundled files

61 scripts

repo stars

3.7kGitHub

Install the Dynamo Router Starter skill

Skills CLI (Claude Code, Codex, Cursor and others)

npx skills add https://github.com/NVIDIA/skills --skill dynamo-router-starter

Claude Code, by hand (all projects)

git clone --depth 1 https://github.com/NVIDIA/skills.git /tmp/skills
mkdir -p ~/.claude/skills
cp -R /tmp/skills/skills/dynamo-router-starter ~/.claude/skills/dynamo-router-starter

For one project only, copy the folder into .claude/skills/ in that project instead. The agent picks it up when a task matches its description.

What it costs your context

Agent Skills load in three steps. Tokens counted with the o200k tokenizer; your model's count will differ a little.

WhenWhat loadsCost
Every sessionName and description50 tok
When the agent uses itThe SKILL.md instructions1,292 tok
Only if opened6 bundled files (20 KB)varies

Until it is used, dynamo-router-starter costs about 27× less than the median MCP server we measured (1,363 tokens on every turn). Installing all 150 skills from NVIDIA/skills would load 20,796 tokens of descriptions. Skills vs MCP study

Checks

  • Ships scripts the agent can run. Read them before installing.

Checked 2026-10-22 · method

What's inside

The headings of its SKILL.md. Read the full file on GitHub.

  • Dynamo Router Starter
  • Purpose
  • Prerequisites
  • Required Inputs
  • Instructions
  • 1. Establish A Baseline
  • 2. Enable KV Routing
  • 3. Smoke Test
  • 4. Compare Modes Carefully
  • Available Scripts
  • Examples
  • Output Contract
  • Limitations
  • Troubleshooting
  • Benchmark
  • References

More skills from NVIDIA/skills

All skills in this repo
  1. Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.

    3.7k2356 tok idle · 2,307 in use
  2. Customize NVIDIA Nemotron Voice Agent's Generic Pipecat example for healthcare appointment, five-field patient intake, or custom tool-calling workflows without a separate backend.

    3.7k1744 tok idle · 2,849 in use
  3. Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.

    3.7k159 tok idle · 4,607 in use
  4. Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.

    3.7k164 tok idle · 3,627 in use
  5. Calibrates pre-recorded `cam_*.mp4` datasets through the AutoMagicCalib REST API. Use for user-supplied local MP4s; route live RTSP streams to `amc-run-rtsp-calibration`.

    3.7k157 tok idle · 4,576 in use
  6. Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.

    3.7k67 tok idle · 4,277 in use
  7. boltz2-nimNVIDIA/skills

    Use Boltz2 NIM for biomolecular structure prediction and binding affinity. Invoke for Boltz2, protein structures, protein-ligand/DNA/RNA complexes, SMILES or CCD ligands, pIC50/IC50 affinity scoring, mmCIF output, hosted NVIDIA API calls, or local Docker deployment.

    3.7k71 tok idle · 1,433 in use
  8. cdf-anomalygenNVIDIA/skills

    Use when running the CDF AnomalyGen pipeline over a defect dataset — mask placement, fine-tuning, synthetic defect-image generation (SDG), evaluation, quality refinement, and pseudo-labeling — even when the user names only one stage.

    3.7k54 tok idle · 9,563 in use