a680d0472b
Adds a Claude Code plugin structure that exposes each NVIDIA DGX Spark
playbook as a triggerable skill, with an index skill ('dgx-spark') that
routes users to the right leaf based on intent and encodes the
relationship graph between playbooks (prerequisites, alternatives,
composes-with, upgrade paths).
Structure:
- overrides/*.md hand-curated frontmatter + Related sections
- scripts/generate.mjs zero-dep Node generator: nvidia + overrides → skills
- scripts/install.sh symlinks skills into ~/.claude/skills (--plugin mode available)
- skills/ committed, browsable, installable without Node
- .github/workflows/ auto-regenerates skills/ when playbooks/overrides change
Initial curated leaves: ollama, open-webui, vllm, connect-to-your-spark.
Remaining 37 leaves use generator fallback (title + tagline + summary
extracted from README) and can be curated incrementally via overrides/.
1.6 KiB
| name | description |
|---|---|
| dgx-spark-multi-agent-chatbot | Deploy a multi-agent chatbot system and chat with agents on your Spark — on NVIDIA DGX Spark. Use when setting up multi-agent-chatbot on Spark hardware. |
Build and Deploy a Multi-Agent Chatbot
Deploy a multi-agent chatbot system and chat with agents on your Spark
This playbook shows you how to use DGX Spark to prototype, build, and deploy a fully local multi-agent system. With 128GB of unified memory, DGX Spark can run multiple LLMs and VLMs in parallel — enabling interactions across agents.
At the core is a supervisor agent powered by gpt-oss-120B, orchestrating specialized downstream agents for coding, retrieval-augmented generation (RAG), and image understanding. Thanks to DGX Spark's out-of-the-box support for popular AI frameworks and libraries, development and prototyping are fast and frictionless. Together, these components demonstrate how complex, multimodal workflows can be executed efficiently on local, high-performance hardware.
Outcome: You will have a full-stack multi-agent chatbot system running on your DGX Spark, accessible through your local web browser. The setup includes:
- LLM and VLM model serving using llama.cpp servers and TensorRT LLM servers
- GPU acceleration for both model inference and document retrieval
- Multi-agent system orchestration using a supervisor agent powered by gpt-oss-120B
- MCP (Model Context Protocol) servers as tools for the supervisor agent
Full playbook: /Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/multi-agent-chatbot/README.md