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a680d0472b
dgx-spark-playbooks/skills/dgx-spark-jax/SKILL.md
Jason Kneen a680d0472b feat: scaffold skills plugin from DGX Spark playbooks 
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/.
2026-04-19 10:22:08 +01:00

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name description
dgx-spark-jax Optimize JAX to run on Spark — on NVIDIA DGX Spark. Use when setting up jax on Spark hardware.

Optimized JAX

Optimize JAX to run on Spark

JAX lets you write NumPy-style Python code and run it fast on GPUs without writing CUDA. It does this by:

  • NumPy on accelerators: Use jax.numpy just like NumPy, but arrays live on the GPU.
  • Function transformations:
    • jit → Compiles your function into fast GPU code
    • grad → Gives you automatic differentiation
    • vmap → Vectorizes your function across batches
    • pmap → Runs across multiple GPUs in parallel

Outcome: You'll set up a JAX development environment on NVIDIA Spark with Blackwell architecture that enables high-performance machine learning prototyping using familiar NumPy-like abstractions, complete with GPU acceleration and performance optimization capabilities.

Full playbook: /Users/jkneen/Documents/GitHub/dgx-spark-playbooks/nvidia/jax/README.md