Inspect, search, diagnose, and run qualified recipes with approval
This playbook installs four native Agent Skills plus a dependency-free dgx-assist command-line tool into a project you choose. Asking your AI coding agent to serve a model or explain why a GPU is unavailable produces answers grounded in your actual hardware instead of recalled generic advice.
The skills route the agent through a fixed workflow: inspect the real hardware, search a pinned snapshot of NVIDIA guidance, resolve an exact named model to a qualified recipe, run preflight, ask you to approve, then act and verify. Every mutation requires your confirmation, and dgx-assist only ever stops services it started and labelled as its own.
The same CLI works without an agent. Add --json and it emits a stable versioned envelope for scripts and CI; omit it and you get readable headings, tables, and action previews for terminal use.
You'll install the four skills and the dgx-assist CLI into a project, then drive a qualified vLLM inference workload from a plain-language request through preflight, approval, launch, and verification on your hardware platform.
You'll also be able to:
Required:
Optional:
Two safety rules matter more than the rest, and the skills enforce them for you: select GPUs by UUID rather than by nvidia-smi index, which is not a CUDA ordinal on this hardware platform; and treat an unrecognized software build as unknown rather than assuming it behaves like a qualified one.
Use the matrix below to confirm your hardware platform, recommended default local settings, and whether multi-node applies.
| Hardware platform | OS | Memory | Recommended default local settings | Multi-node capable hardware |
|---|---|---|---|---|
| DGX Station | DGX OS (Linux) | Large HBM + Grace DRAM | Four Agent Skills + dgx-assist; GB300 compute capability 10.3; smoke recipe Qwen/Qwen2.5-Coder-1.5B-Instruct on vLLM | — |
Hardware requirements
10.3, confirmed by nvidia-smi --query-gpu=compute_cap --format=csvSoftware requirements
python3 --versiondocker info | grep -i runtimeclaude --version, codex --version, gemini --version, or CursorAll required assets can be found in the playbook repository.
assets/install.sh — Thin wrapper that runs the Python installerassets/installer.py — Installs, updates, migrates, and uninstalls the skills; writes .dgx-station/install-manifest.json and manages the delimited NVIDIA block in your agent context fileassets/dgx-assist.pyz — The standalone zipapp CLI providing host inspection, guidance search, recipe resolution, preflight, service lifecycle, MIG planning, and diagnosticsassets/skills/dgx-station/ — Platform compatibility, coherency, GPU selection, container, CDI, and power guidanceassets/skills/dgx-station-inference/ — Exact-model recipe resolution, preflight, approved launch, verification, and owned service lifecycleassets/skills/dgx-station-mig/ — Driver-discovered MIG inspection, planning, approved apply, and restoration evidenceassets/skills/dgx-station-diagnose/ — Read-only diagnostics, playbook correlation, redacted bundles, and one allowlisted fix at a timeassets/AGENTS.md — The managed context block written into your agent's project context fileassets/install.sh uninstall --target /path/to/project to remove every manifest-owned file and the delimited context block. The installer preserves any managed file you modified and leaves unrelated context untouched. A user-scope CLI installed with install-cli is removed by deleting ~/.local/bin/dgx-assist. Cached state lives under ~/.cache/dgx-assist/ and ~/.local/state/dgx-assist/ and can be deleted.dgx-assist now recognizes a WSL2 guest and reports Compatibility: WSL2 with read-only diagnostics instead of an untrusted release markerdgx-assist, inspect the release profile, search pinned guidance, and run the qualified vLLM smoke recipe with approval on supported hardware platforms