mirror of
https://github.com/NVIDIA/dgx-spark-playbooks.git
synced 2026-04-26 11:53:53 +00:00
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/.
25 lines
1.4 KiB
Markdown
25 lines
1.4 KiB
Markdown
---
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name: dgx-spark-rag-ai-workbench
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description: Install and use AI Workbench to clone and run a reproducible RAG application — on NVIDIA DGX Spark. Use when setting up rag-ai-workbench on Spark hardware.
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---
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<!-- GENERATED:BEGIN from nvidia/rag-ai-workbench/README.md -->
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# RAG Application in AI Workbench
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> Install and use AI Workbench to clone and run a reproducible RAG application
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This walkthrough demonstrates how to set up and run an agentic retrieval-augmented generation (RAG)
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project using NVIDIA AI Workbench. You'll use AI Workbench to clone and run a pre-built agentic RAG
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application that intelligently routes queries, evaluates responses for relevancy and hallucination, and
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iterates through evaluation and generation cycles. The project uses a Gradio web interface and can work
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with both NVIDIA-hosted API endpoints or self-hosted models.
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**Outcome**: You'll have a fully functional agentic RAG application running in NVIDIA AI Workbench with a web
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interface where you can submit queries and receive intelligent responses. The system will demonstrate
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advanced RAG capabilities including query routing, response evaluation, and iterative refinement,
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giving you hands-on experience with both AI Workbench's development environment and sophisticated RAG
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architectures.
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**Full playbook**: `/Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/rag-ai-workbench/README.md`
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<!-- GENERATED:END -->
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