
Description
Inspect and guide NVIDIA DGX Station GB300 development using the local dgx-assist CLI and pinned NVIDIA playbooks. Use for general Station platform questions, Software 1.0 or 2.0 compatibility, GB300 or RTX GPU selection, UUID ordering, mixed ATS/HMM coherency, CDMM, general containers, CDI, CUDA visibility, or vsloshd power-sloshing behavior. Do not use for vLLM or SGLang container selection or tuning, serving a named model, changing MIG, or troubleshooting a reported failure when the dedicated Station skill applies.
SKILL.md
DGX Station
Ground every Station-specific answer in current host evidence and a retrieved NVIDIA passage.
Workflow
- Run
scripts/dgx-assist system inspect --json. - Read references/software-compatibility.md. Use
compatibility.profile_id,support_level, andcapabilities; do not branch on a version prefix. - Form a narrow playbook query from the user's question and run
scripts/dgx-assist playbook search "<query>" --json. - Apply the source-precedence rules in references/sources.md. Use only passages applicable to the detected profile. Cite their URL, heading, line span, source commit, and source-file digest.
- On the Software 1.0 capability-scoped profile, provide the supported inspection, diagnostics, compatibility guidance, and exact recipes qualified for that profile. Do not treat an absent Software 2.0-only service as a fault.
- Before any platform action, require its named capability to be
true. If it is false, explain the profile restriction separately from relevant read-only guidance. - If
version_specific_guidanceis false or retrieval abstains, say no applicable version-specific passage was found and avoid inventing a platform command. - Answer from the combined profile and applicable passage. Label unknown evidence as unknown.
Safety requirements
- Before any mutating action, display the exact command to be run and obtain explicit user approval immediately before executing it. Never batch approvals or carry one forward to a later action.
- Never assume an
nvidia-smiindex is a CUDA ordinal. - Use GPU UUIDs for every proposed launch. When multiple GPUs are deliberately visible, place the GB300 UUID first.
- Do not apply either legacy or Software 2.0 mixed-device behavior without checking the detected profile and observed ATS/HMM state.
- Do not tell the user to install or operate Fabric Manager as a normal Station requirement.
- When
mixed_coherency_serviceis true, inspectmixed-coherency-gpu-select.service, its generated environment, and container exposure separately. - When
dynamic_power_sloshingis true, inspectvsloshd. Always inspect observed caps and never propose an ad hoc power-cap change. - Never install or change the driver, kernel, OS, firmware, or packages.
- Never display credential values.
Read references/platform.md when interpreting coherency, container ordering, power, memory placement, or qualification evidence. Read references/software-compatibility.md before applying version-specific guidance. Read references/sources.md when guidance may come from the Development Guide or bring-up guide. Read references/cli.md for command and JSON details.
More skills from the dgx-spark-playbooks repository
View all 15 skillsanalysis-methods
write Python analysis code for FHIR data
Jul 14Data AnalysisFHIRHealthcareNVIDIA +1case-summary
summarize clinical patient cases from FHIR
Jul 14FHIRHealthcareNVIDIASummarizationclinical-delegation
delegate clinical tasks to specialist agents
Jul 14AgentsHealthcareMulti-AgentNVIDIAclinical-knowledge
provide clinical reference and regulatory context
Jul 14Clinical TrialsHealthcareNVIDIARegulatory Compliancecohort-compare
analyze patient cohorts from FHIR endpoints
Jul 14Data AnalysisFHIRHealthcareNVIDIAdgx-diagnose
diagnose NVIDIA DGX Station hardware issues
Jul 14AI InfrastructureDebuggingNVIDIAObservability
More from NVIDIA
View publishernemoclaw-user-guide
retrieve NemoClaw documentation and configuration
NemoClaw
Jul 20DocumentationMCPSearchmcore-build-and-dependency
manage Megatron-LM development environments
Megatron-LM
Jul 27ContainersDeploymentPythonmcore-bump-base-image
update NVIDIA PyTorch base images
Megatron-LM
Jul 14CI/CDDeploymentmcore-cicd
manage CI/CD pipelines for Megatron-LM
Megatron-LM
Jul 27CI/CDDeploymentGitHubmcore-create-issue
investigate CI failures and create issues
Megatron-LM
Jul 14DebuggingGitHubTriagemcore-linting-and-formatting
lint and format Megatron-LM code
Megatron-LM
Jul 14Best PracticesCode Analysis