Resolve, tune, preflight, launch, verify, inspect, and stop exact-model inference on NVIDIA DGX Station through dgx-assist. Use for vLLM or SGLang container selection, NGC versus upstream, GPU memory utilization, CPU or KV offload, HBM fit, KV-cache sizing, ISL or context length, prefix caching, chunked prefill, batching, concurrency, performance tuning, serving or deploying a named model, an OpenAI-compatible endpoint, Station recipe models, or an owned inference service. Require an exact model ID for recipe resolution or model-specific tuning, and never recommend or substitute a different model.
npx skills add https://github.com/NVIDIA/dgx-spark-playbooks --skill dgx-station-inference
Serve only trusted, published, current, physically validated recipes. Bundled
recipes are bound to the installed package; downloaded refreshes must be
signature-verified.
scripts/dgx-assist recipe models --json, show the alphabetical IDs and runnable states without ranking them, then ask which exact model to use.scripts/dgx-assist system inspect --json.scripts/dgx-assist playbook search "<model backend Station inference concern>" --json; cite relevant passages. Abstain from invented platform guidance if no passage overlaps.compatibility.capabilities.recipe_execution. If it is false, stop before resolution or launch and explain the detected profile separately from applicable qualitative guidance.scripts/dgx-assist recipe resolve --model "<exact-id>" --json. Add --backend only if the user requested one. Bind to localhost by default; only if the user explicitly requested and confirmed external exposure, add --bind-host "<approved-host>" here so preflight and run evaluate the host that was actually approved.recipe show for the resolved recipe, then recipe preflight.10. Only after approval, run recipe run --resolution-id "<id>" --allow-download --yes as applicable. Add --allow-external-bind only when the resolution carries the non-local bind host the user approved in step 9; never introduce external exposure that was not resolved and previewed.
11. Report the model-identity and smoke verification from the receipt.
recipe status to report only owned services.recipe stop --service-id "<id>" --dry-run.--yes.setup_command, serve.command, or shell text from a recipe.--yes only as the non-interactive representation of approval already obtained.latest tag, documentation example, or upstream tuning value into an executable launch.dgx-assist may run it.recipe_execution is false or no current runnable recipe exists for the exact model, do not recommend a numeric launch value, including zero, from parameter-count arithmetic or raw free-memory evidence. Explain only the sourced qualitative tradeoff.Read references/workflow.md before resolving or running. Read references/vllm-best-practices.md for container selection, HBM/offload, KV cache, prefix caching, chunked prefill, or concurrency questions. Read references/json-types.md when interpreting a resolution, preflight, or receipt.
Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.
Access NCBI GEO for gene expression/genomics data. Search/download microarray and RNA-seq datasets (GSE, GSM, GPL), retrieve SOFT/Matrix files, for transcriptomics and expression analysis.
Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.
Multi-objective optimization framework. NSGA-II, NSGA-III, MOEA/D, Pareto fronts, constraint handling, benchmarks (ZDT, DTLZ), for engineering design and optimization problems.
Statistical modeling toolkit. OLS, GLM, logistic, ARIMA, time series, hypothesis tests, diagnostics, AIC/BIC, for rigorous statistical inference and econometric analysis.
Add unsigned integer (uint) type support to PyTorch operators by updating AT_DISPATCH macros. Use when adding support for uint16, uint32, uint64 types to operators, kernels, or when user mentions enabling unsigned types, barebones unsigned types, or uint support.
Convert PyTorch AT_DISPATCH macros to AT_DISPATCH_V2 format in ATen C++ code. Use when porting AT_DISPATCH_ALL_TYPES_AND*, AT_DISPATCH_FLOATING_TYPES*, or other dispatch macros to the new v2 API. For ATen kernel files, CUDA kernels, and native operator implementations.
Write docstrings for PyTorch functions and methods following PyTorch conventions. Use when writing or updating docstrings in PyTorch code.
Take nvidia/dgx-station-inference from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.