- Dgx Station MigInspect NVIDIA DGX Station GB300 MIG state and installed-driver profiles, create a digest-bound layout plan, disclose disruption and restoration, and apply an approved still-valid plan. Use when the user asks to enable, disable, partition, reconfigure, inspect, or troubleshoot MIG instances or needs MIG UUIDs. Never assume static profile IDs or terminate GPU clients.NVIDIA/dgx-spark-playbooks1,393
- Fhir BasicsTeaches agents how FHIR R4 APIs work, what resources are available, how to query them with search parameters, and how to correctly parse all response formats including component Observations.NVIDIA/dgx-spark-playbooks1,393
- Fhir BasicsTeaches agents how FHIR R4 APIs work, what resources are available, how to query them with search parameters, and how to correctly parse all response formats including component Observations.NVIDIA/dgx-spark-playbooks1,393
- Mig ConfigureConfigure NVIDIA MIG (Multi-Instance GPU) partitions on the DGX Station GB300, including enabling MIG mode, choosing a profile layout, creating instances, and retrieving MIG UUIDs. Use when the user asks to partition the GB300, set up MIG, run multiple models in isolation on one GPU, or reconfigure existing MIG instances.NVIDIA/dgx-spark-playbooks1,393
- Molecular VizVisualize drug-protein complexes using build_viewer.py, PubChem, and OpenFold3 NIM. Use when asked to show a molecular structure, drug target, or protein visualization.NVIDIA/dgx-spark-playbooks1,393
- Molecular VizVisualize drug-protein complexes using build_viewer.py, PubChem, and OpenFold3 NIM. Use when asked to show a molecular structure, drug target, or protein visualization.NVIDIA/dgx-spark-playbooks1,393
- Sglang SetupDeploy an SGLang inference server on an NVIDIA DGX Station GB300 with the cu130 container, RadixAttention prefix caching, and structured JSON output support. Use when the user asks to serve a model with SGLang, start an SGLang endpoint, or needs structured-output inference on DGX Station.NVIDIA/dgx-spark-playbooks1,393
- Vllm SetupDeploy a vLLM inference server on an NVIDIA DGX Station GB300 with validated container, GPU targeting, and tuning parameters. Use when the user asks to serve a model with vLLM, start a vLLM endpoint, or set up OpenAI-compatible inference on DGX Station.NVIDIA/dgx-spark-playbooks1,393
- Developer Bump VersionBump the Earth2Studio version on main to start a new development cycle. This unblocks commits on main after a release branch has been merged. Performs a CHANGELOG update (new blank section) and a hatch version bump. Use when main already has the release merge but still carries the old version string, or when the developer-release-rebase skill detects the version was already bumped during rebase and only the changelog/version housekeeping remains.NVIDIA/earth2studio1,188
- Developer Release RebasePrepare a new minor alpha release of Earth2Studio by rebasing the release candidate branch onto main, bumping the version, updating the changelog, updating the README latest-news highlights, stripping example version tags, and pushing for PR. Use when releasing, cutting a release, preparing a release branch, rebasing a release, or bumping the version for a new development cycle.NVIDIA/earth2studio1,188
- Earth2studio Create DatasourceCreate and validate Earth2Studio data source wrappers (DataSource, ForecastSource, DataFrameSource, ForecastFrameSource) from remote stores. May add Python dependencies to pyproject.toml as part of development. Do NOT use for fetching data with existing sources, model inference, or Earth2Studio installation/setup tasks.NVIDIA/earth2studio1,188
- Earth2studio Create DiagnosticCreate Earth2Studio diagnostic model wrappers for single-step data transformations, including simple derived diagnostics, packaged AutoModel diagnostics, and generative or diffusion diagnostics. Do NOT use for prognostic time-stepping models, data sources, or installation.NVIDIA/earth2studio1,188
- Earth2studio Create PrognosticCreate Earth2Studio prognostic (time-stepping forecast) model wrappers. Do NOT use for diagnostic models, data sources, or installation.NVIDIA/earth2studio1,188
- Earth2studio Data FetchFetch weather/climate data via Earth2Studio data sources for specific variables and times. Do NOT use for inference pipelines, model discovery, or installation.NVIDIA/earth2studio1,188
- Earth2studio Deterministic ForecastBuild deterministic forecast scripts with Earth2Studio (model, data source, IO, inference). Do NOT use for ensemble, diagnostics, data-only fetch, or install.NVIDIA/earth2studio1,188
- Earth2studio DiscoverFind Earth2Studio models, data sources, and examples for a weather/climate use case. Do NOT use for writing inference code, downloading data, or installation.NVIDIA/earth2studio1,188
- Earth2studio InstallGuide installing Earth2Studio via uv or pip, selecting model extras, and configuring the environment. Do NOT use for writing inference code, choosing models, or PhysicsNeMo questions.NVIDIA/earth2studio1,188
- Cudaq GuideUse for CUDA-Q setup, simulation targets, QPU access, and @cudaq.kernel authoring guidance.NVIDIA/cuda-quantum1,148
- Cudaq ImportingUse when porting circuits from another framework (e.g. Qiskit) into CUDA-Q kernels while preserving the source algorithm and validation fidelity.NVIDIA/cuda-quantum1,148
- Cuopt DeveloperModify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.NVIDIA/cuopt1,053
- Cuopt InstallInstall cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.NVIDIA/cuopt1,053
- Cuopt Multi Objective ExplorationTrace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).NVIDIA/cuopt1,053
- Cuopt Numerical Optimization ApiLP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.NVIDIA/cuopt1,053
- Cuopt Numerical Optimization FormulationLP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.NVIDIA/cuopt1,053
- Cuopt Routing Api PythonVehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.NVIDIA/cuopt1,053
- Cuopt Server Api PythoncuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.NVIDIA/cuopt1,053
- Cuopt Skill EvolutionAfter solving a non-trivial problem, detect generalizable learnings and propose skill updates. Always active — applies to every interaction.NVIDIA/cuopt1,053
- Cuopt User RulesBase rules for end users calling NVIDIA cuOpt (routing/LP/MILP/QP/install/server). Not for cuOpt internals — use cuopt-developer for those.NVIDIA/cuopt1,053
- Udf BenchmarkAssists with benchmarking and profiling the performance of an Apache Spark UDF on the GPU. This is step 3 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-* -> udf-benchmark). Use this skill when you have a CPU UDF and a RapidsUDF or SQL implementation, and need to benchmark the performance of the CPU UDF against the GPU implementation.NVIDIA/cudf-spark1,008
- Udf Convert To CudaAssists with converting a non-aggregating Apache Spark UDF to a native CUDA RapidsUDF using JNI and libcudf. This is step 2 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-cuda -> udf-benchmark). Use this skill when you have a CPU UDF with a unit test and need to convert it to a native CUDA implementation. Prefer udf-convert-to-cudf unless a CUDA implementation is necessary for performance or correctness, or if requested by the user.NVIDIA/cudf-spark1,008
- Udf Convert To CudfAssists with converting an Apache Spark UDF to a GPU-accelerated RapidsUDF using cuDF Java APIs. This is step 2 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-cudf -> udf-benchmark). Use this skill when you have a CPU UDF with a unit test and need to convert it to a RapidsUDF.NVIDIA/cudf-spark1,008
- Udf Convert To SqlAssists with converting an Apache Spark UDF to a functionally equivalent Spark SQL expression. This is step 2 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-sql -> udf-benchmark). Use this skill when you have a CPU UDF with a unit test and need to convert it to SQL for GPU acceleration.NVIDIA/cudf-spark1,008
- Udf Gen TestAssists with generating a unit test for an Apache Spark UDF. This is step 1 of 3 in the UDF conversion workflow (udf-gen-test -> udf-convert-to-* -> udf-benchmark). Use this skill when you have a CPU UDF and need to create a unit test for the UDF before converting it into a GPU-compatible implementation.NVIDIA/cudf-spark1,008
- Udf Judge ConversionReviews generated UDF tests and GPU/SQL implementations for robustness, anti-cheating, and GPU execution integrity. Use when the user requests a judge/review-agent pass, or when manually reviewing a completed conversion.NVIDIA/cudf-spark1,008
- Udf Optimize CudfIteratively optimizes a cuDF RapidsUDF implementation for GPU performance. Use after testing and benchmarking with udf-benchmark. Runs a loop of profiling, optimizing, testing, and benchmarking until performance converges or the iteration budget is exhausted.NVIDIA/cudf-spark1,008
- Autofl NvflareHelp coding agents work on an NVFlare-based Auto-FL harness that follows an autoresearch-style loop. Use when the user wants to create, edit, debug, or extend program.md, task folders such as tasks/cifar10/ and tasks/vlm_med/, task-local job.py, client.py, model.py, shared custom_aggregators.py, mutation policies, results.tsv logging, or coding-agent prompts for a bounded federated-learning research loop. This skill is specifically for NVFlare harness work where the Client API loop, DIFF upload NVIDIA/NVFlare981
- Autofl Nvflare ReportGenerate and commit a markdown report after an Auto-FL NVFlare autoresearch experiment has been manually stopped. Use when the user asks to summarize a stopped campaign, report achieved improvements, explain implemented literature-derived ideas and sources, refresh progress plots, capture pasted agent model/effort/cost context when available, or commit the final report and progress plot to the current experiment branch.NVIDIA/NVFlare981
- Nvflare AutoflUse for agent-assisted Auto-FL optimization of an existing NVFLARE job in simulation, POC, or production. Do not use for code conversion, diagnosis-only work, or deployment setup.NVIDIA/NVFlare981
- Nvflare Autofl ReportGenerate a reproducible final report, literature-outcome synthesis, JSON summary, and refreshed progress plot for a stopped or interrupted NVFLARE Auto-FL campaign.NVIDIA/NVFlare981
- Nvflare Convert HuggingfaceConvert existing Hugging Face Transformers Trainer or TRL SFTTrainer training code into an NVFLARE federated job using flare.patch(trainer), local validation, and job export; use when the user names Hugging Face or preliminary source inspection identifies one Hugging Face owner, and not for manual PyTorch loops, Lightning, inference-only pipelines, deployment, or experiment workflows.NVIDIA/NVFlare981
- Nvflare Convert LightningConvert existing PyTorch Lightning training code into an NVFLARE federated job using the Lightning Client API patch, local validation, and job export; use only when the request names federated/NVFLARE conversion or asks multiple sites to train collaboratively while keeping each site's data local, and either names PyTorch Lightning or preliminary source inspection identifies one Lightning owner; do not use for non-federated Lightning work such as DDP, profiling, inference serving, or training-looNVIDIA/NVFlare981
- Nvflare Convert PytorchConvert existing plain or manual PyTorch training code into an NVFLARE federated job using Client API model exchange, local validation, and job export; use when the user names plain PyTorch or preliminary source inspection identifies one plain-PyTorch owner, and not for Lightning, other frameworks, deployment, POC/production lifecycle, or experiment workflows.NVIDIA/NVFlare981
- Nvflare Diagnose JobUse when the user asks why a reported NVFLARE job failure signal occurred: the job failed, stalled, timed out, lost clients, ended with EXECUTION_EXCEPTION, or produced suspicious errors. Diagnose in simulation, POC, or production by collecting bounded evidence and mapping failure patterns to recovery actions.NVIDIA/NVFlare981
- Nvflare Fed StatsCompute federated statistics over tabular data (count, sum, mean, stddev, var, histogram, quantile, noise-protected min/max) and image data (count, failure_count, pixel-intensity histogram) across NVFLARE sites via FedStatsRecipe — automatic and non-interactive from the dataset, feature names (header or supplied), and optionally a README or notes declaring which statistics to compute; do not use for model training conversion, hierarchical statistics, deployment, POC/production lifecycle, or failNVIDIA/NVFlare981
- Nvflare OrientRoute open-ended NVFLARE advice and only conversion requests whose preliminary source inspection reports unresolved or conflicting ownership; never load this skill merely to inspect a concrete conversion request before selecting its detected framework converter.NVIDIA/NVFlare981
- Nvflare SharedInternal NVFLARE conversion references and templates. Use only when another NVFLARE skill directs you to a shared workflow, policy, or asset.NVIDIA/NVFlare981
- Cutedsl Kernel IntegrationUse when integrating a CuTeDSL/CUTE DSL kernel into cuDNN Frontend as a frontend-only Python API, including APIBase wrappers, lazy cudnn exports, optional cutedsl dependencies, FE OSS documentation, and pytest coverage.NVIDIA/cudnn-frontend953
- Tilegym Adding Cutile KernelAdd a new cuTile GPU kernel operator to TileGym. Covers dispatch registration in ops.py, cuTile backend implementation, __init__.py exports, test creation, and benchmark in tests/benchmark. Use when adding, creating, or implementing a new cuTile operator/kernel in TileGym, or when asking how to register a new cuTile op.NVIDIA/TileGym822
- Tilegym Converting Cutile To JuliaConverts cuTile Python GPU kernels (@ct.kernel) to cuTile.jl Julia equivalents. Handles kernel syntax translation, 0-indexed to 1-indexed conversion, broadcasting differences, memory layout (row-major to column-major), type system mapping, and launch API differences. Use when converting, porting, or translating cuTile Python kernels to Julia cuTile.jl, or debugging/optimizing existing Julia cuTile translations.NVIDIA/TileGym822
- Tilegym Converting Cutile To TritonConverts cuTile GPU kernels (@ct.kernel) to Triton (@triton.jit). Handles standard in-repo conversion, debugging (cudaErrorIllegalAddress, shape mismatch, numerical mismatch), and mapping cuTile idioms (ct.load/ct.store, ct.Constant, ct.launch) to Triton equivalents. Covers dual-kernel layout flags (e.g. transpose=True/False + autotune grid via META) per translations/advanced-patterns.md. Use when converting, porting, or translating cuTile kernels to Triton, or debugging existing Triton translatNVIDIA/TileGym822
- Tilegym Converting Cutile Triton To Cutile RsUse this skill to convert, port, or translate Triton-TileIR or cuTile-Python GPU kernels to cutile-rs (Rust). The orchestrator runs scripts/preflight.sh, then drives a bounded Agent A -> B -> D -> E pipeline (Agent C is diagnostic, Agent F optional), delegating all kernel/host/correctness/perf work to sub-agents and routing by each stage's single-line VERDICT.NVIDIA/TileGym822
- Tilegym Cutile AutotuningUse when adding, modifying, optimizing, or debugging CuTile autotuning code. Trigger signals: `exhaustive_search` / `replace_hints` / `hints_fn` / `cuda.tile.tune` in code, `autotune` in filenames, or correctness/performance issues in autotuned CuTile kernels. Covers: tune-once/cache/launch pattern, per-architecture configs (sm80–sm120), parameter space design (tile sizes, occupancy, num_ctas), and 7 common pitfalls with solutions.NVIDIA/TileGym822
- Tilegym Cutile OptimizingQuestion-gated router for the curated cuTile optimization wiki: kernel-family playbooks, measured techniques with caveats, performance patterns, and practical language knowledge for Blackwell and Hopper. Use when an agent has inspected the current kernel and has a concrete question that could change its plan, when measurements point to a specific bottleneck, or when a cuTile language or API detail is blocking implementation. Do not use as mandatory onboarding, as the source of the first design, NVIDIA/TileGym822
- Tilegym Cutile PythonExpert cuTile programming assistant. Write high-performance GPU kernels using cuTile's tile-based programming model with proper validation and optimization. Supports deep agent orchestration for complex multi-kernel tasks.NVIDIA/TileGym822
- Tilegym Improve Cutile Kernel PerfIteratively optimize cuTile kernel performance through systematic profiling, bottleneck analysis, IR comparison, and targeted tuning. Covers tile sizes, occupancy, autotune configs, TMA, latency hints, persistent scheduling, num_ctas, flush_to_zero, and IR-level debugging. Use when asked to "optimize cutile kernel", "improve kernel perf", "tune cutile performance", "make kernel faster", or iteratively benchmark and refine a cuTile GPU kernel in the TileGym project.NVIDIA/TileGym822
- Tilegym Monkey Patch Kernels To TransformersIntegrate TileGym kernels into Hugging Face `transformers` models by replacing the library's submodule(s) and certain class(es)' implementations, and patching certain class(es)' init/forward/load weight methods prior to instantiating models. Used when the user requires integrating TileGym kernels into `transformers` models.NVIDIA/TileGym822
- Disco Kernel DevExpert guide for adding, optimizing, or debugging DISCO convolution kernels in torch-harmonics. Use this skill whenever the user mentions: adding a new kernel variant or GPU arch path, wiring up a kernel through the Python dispatch layer, fixing bf16/fp16/autocast issues in DISCO, profiling or benchmarking the CSR or kpacked forward/backward, propagating kernel changes to the distributed DISCO path, or writing tests that cover all dispatch branches. Also use for debugging correctness issues in tNVIDIA/torch-harmonics708
- Test WritingGuide for writing tests in torch-harmonics. Use this skill whenever the user asks to add tests, check or extend test coverage, write a new test class, test a new layer or kernel, or add distributed tests. Also use when the user asks about tolerance values, how to compare tensors, how to structure distributed test infrastructure, or how to test gradients. Covers serial tests in test_convolution.py and distributed tests in test_distributed_*.py.NVIDIA/torch-harmonics708
- Torch Harmonics SkillsIndex of project-local Claude Code skills for torch-harmonics. Use this to discover which skills are available for this codebase. Individual skills live in the .skills/ directory at the project root.NVIDIA/torch-harmonics708
- Nvshmem Collect Performance DataCollect and package NVSHMEM put/get bandwidth, latency, and other perftest results with system and topology evidence for performance sanity checks.NVIDIA/nvshmem594