- Nvflare Convert PytorchConvert existing PyTorch training code into an NVFLARE federated job using Client API model exchange, local validation, and job export; do not use for other frameworks or deployment-only tasks.NVIDIA/NVFlare944
- Nvflare Diagnose JobDiagnose failed, stalled, or suspicious NVFLARE jobs in simulation, POC, or production by collecting bounded evidence and mapping failure patterns to recovery actions.NVIDIA/NVFlare944
- Nvflare OrientRoute ambiguous NVFLARE requests by inspecting the local project, checking readiness, and recommending the next specific FLARE workflow skill without editing files.NVIDIA/NVFlare944
- 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-frontend861
- 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/TileGym760
- 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/TileGym760
- 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/TileGym760
- 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/TileGym760
- 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/TileGym760
- 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/TileGym760
- 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/TileGym760
- Ncu ReportAnalyze NVIDIA Nsight Compute (ncu) profiling reports (.ncu-rep files). Extract metrics, performance data, SASS/CUDA source, and identify bottlenecks. TRIGGER when: user asks to analyze, profile, or look at an ncu report, .ncu-rep file, Nsight Compute report, kernel performance/profiling data from ncu, or asks to generate/collect an ncu profile for a tilus kernel or example script. DO NOT TRIGGER when: user is writing unrelated profiling code.NVIDIA/tilus488
- Write DocsConvention and format for writing instruction docstrings and RST tutorials in tilus documentation. TRIGGER when: user asks to add, update, or write documentation for tilus instructions, instruction groups, or tutorials.NVIDIA/tilus488
- Cuopt DebuggingTroubleshoot cuOpt LP/MILP problems including errors, wrong results, infeasible solutions, performance issues, and status codes. Use when the user says something isn't working, gets unexpected results, or needs help diagnosing issues.NVIDIA/cuopt-examples460
- Cuopt Model MapperMap interpreted optimization problems into cuOpt-native models for the fast path with minimal clarifying questions.NVIDIA/cuopt-examples460
- Cuopt SandboxRun cuOpt in the NemoClaw sandbox — probe/smoke gates, remote gRPC env, then vendored cuOpt skills.NVIDIA/cuopt-examples460
- Generic Max SupplyMulti-period supply chain planning model: data files, BOM structure, variable/constraint reference for the max-supply base model.NVIDIA/cuopt-examples460
- Optimization From Data OrchestratorCoordinate uploaded data plus a natural-language question into interpretation, clarification, cuOpt solve, and a user-facing answer.NVIDIA/cuopt-examples460
- Optimization Intent RouterClassify whether a data-backed request is LP, MILP, QP, routing, or non-optimization analytics.NVIDIA/cuopt-examples460
- Optimization Mode RouterChoose fast direct-to-cuOpt solve versus replayable or auditable model artifact mode.NVIDIA/cuopt-examples460
- Tabular Optimization IngestionInfer optimization structure from uploaded tables and identify minimal clarifications before cuOpt modeling.NVIDIA/cuopt-examples460
- CuequivarianceDefine custom groups (Irrep subclasses), build segmented tensor products with CG coefficients, create equivariant polynomials and IrDictPolynomials, and use built-in descriptors (linear, tensor products, spherical harmonics). Use when working with cuequivariance group theory, irreps, or segmented polynomials.NVIDIA/cuEquivariance404
- Cuequivariance JaxExecute equivariant polynomials in JAX using segmented_polynomial (naive/uniform_1d), the ir_dict workflow with IrDictPolynomial and dict[Irrep, Array], and Flax NNX layers (IrrepsLinear, SphericalHarmonics, IrrepsIndexedLinear). Use when writing JAX code with cuequivariance.NVIDIA/cuEquivariance404
- Cuequivariance TorchExecute equivariant tensor products in PyTorch using SegmentedPolynomial (naive/uniform_1d/fused_tp/indexed_linear), high-level operations (ChannelWiseTensorProduct, FullyConnectedTensorProduct, Linear, SymmetricContraction, SphericalHarmonics, Rotation), and layers (BatchNorm, FullyConnectedTensorProductConv). Use when writing PyTorch code with cuequivariance.NVIDIA/cuEquivariance404
- Integrate A ModelEnd-to-end workflow for porting an external video diffusion model into a flashdreams integration — scope the architecture, scaffold a workspace-member plugin, reuse an existing recipe, write the checkpoint key-remap, layer model-specific conditioners, wire the runner, and verify with checkpoint weight-equality + upstream parity + a GPU rollout. Use when integrating a new model (e.g. a HuggingFace/research release) into flashdreams or a downstream repo, porting upstream weights, or reproducing anNVIDIA/flashdreams366
- Maintaining Oss StateMaintain FlashDreams's OSS-release state — the LICENSE / NOTICE / THIRD-PARTY-NOTICES / REUSE.toml / LICENSES/ / CONTRIBUTING.md collateral that satisfies OSRB Bug 6107043, the per-file SPDX headers, the third-party dependency manifest in THIRD-PARTY-NOTICES, and the pyproject.toml + uv.lock dependency pins. Use when adding or upgrading a runtime dependency, vendoring third-party source into the repo, adding a new first-party source file (any .py / .pyx / .pyi / .c / .cc / .cpp / .h / .hpp / .cuNVIDIA/flashdreams366
- Python Docstring StyleWrite Python docstrings and inline comments matching the flashdreams house style — SPDX header, one-line module docstring, Google-style function docstrings (Args/Returns/Raises), PEP 257 attribute docstrings on dataclass/class fields *and on module-level constants*, double-backticks for code references, imperative first sentences, and signpost-style inline block comments (kept, not stripped, on a tightening pass). Use when authoring or editing any .py file under flashdreams/, when adding a new mNVIDIA/flashdreams366
- Aicr Analyzing SnapshotsUse when analyzing an AICR snapshot YAML file, reviewing cluster state, comparing provider characteristics, extracting GPU/network topology insights, or generating a cluster assessment report from a snapshot. Triggers on: snapshot analysis, cluster review, provider comparison, GPU topology, node health, snapshot report.NVIDIA/aicr340
- Aicr Auditing DocsUse when reviewing AICR's Markdown documentation for duplication, drift, bloat, and gaps — to keep docs high-value as the project evolves. Triggers on "audit the docs", "review documentation", "docs cleanup", "/aicr-auditing-docs", or any request to find redundant/stale/missing docs across README, docs/, demos/, and the root governance files. Produces a prioritized findings report (research, not edits) grouped by the five audit dimensions, anchored to the project's canonical sources of truth.NVIDIA/aicr340
- Aicr Creating Guided DemosScaffolds an interactive guided demo script (demos/*.sh), live or self-paced, with the Frame → Tell → Show → Close pattern. Triggers on "demo script", "guided walkthrough", "demos/*.sh", "live demo".NVIDIA/aicr340
- Aicr Creating Slide DecksUse when building a self-contained HTML slide deck or visual talking-point for a technical concept or workflow (e.g. a demos/*.html) — shown full-screen or projected and narrated, opening in any browser with no build step or dependencies.NVIDIA/aicr340
- Aicr Managing OpenvexUse when adding, updating, or removing CVE/GHSA suppressions in `.openvex.json` — the OpenVEX document consumed by the daily image vulnerability scan workflow. Triggers on "VEX", "OpenVEX", ".openvex.json", "suppress CVE", "ignore CVE", "vulnerability suppression", "aiperf-bench CVE", or any request to act on findings reported by `Daily Image Vulnerability Scan` for the aiperf-bench image. Keeps the file current: adds reachability-evidenced statements for new HIGH+ findings, drops statements thaNVIDIA/aicr340
- Aicr Release NotesUse when drafting the human-readable GitHub release notes summary for an upcoming AICR release. Triggers on "release notes", "draft release notes", "/aicr-release-notes", or any request to summarize commits since the last tag into a polished release announcement. Runs tools/changelog, groups commits into thematic highlights, mirrors the style of the previous release, and writes a Markdown draft to a temp file for hand-editing before publishing.NVIDIA/aicr340
- Cosmos3 Codebase NavNavigate the Cosmos3 package codebase to find where parameters, configs, defaults, scripts, and documentation live. Use when the user asks "where is X in cosmos3", "how do I find the config for Y", "where are the defaults", "where do I change a parameter", or any question about locating files, modules, or settings. Also use when the user opens or edits files and needs orientation.NVIDIA/cosmos-framework323
- Cosmos3 Codebase NavNavigate the Cosmos3 package codebase to find where parameters, configs, defaults, scripts, and documentation live. Use when the user asks "where is X in cosmos3", "how do I find the config for Y", "where are the defaults", "where do I change a parameter", or any question about locating files, modules, or settings. Also use when the user opens or edits files and needs orientation.NVIDIA/cosmos-framework323
- Cosmos3 Env TroubleshootDiagnose and fix Cosmos3 environment, installation, and runtime errors. Use when the user encounters an ImportError, ModuleNotFoundError, CUDA error, Docker error, checkpoint download failure, or any traceback during setup or inference.NVIDIA/cosmos-framework323
- Cosmos3 Env TroubleshootDiagnose and fix Cosmos3 environment, installation, and runtime errors. Use when the user encounters an ImportError, ModuleNotFoundError, CUDA error, Docker error, checkpoint download failure, or any traceback during setup or inference.NVIDIA/cosmos-framework323
- Cosmos3 InferenceGuide users through running Cosmos3 inference — offline batch generation, online serving with Ray and Gradio, parallelism options, input formats, sampling parameters, and prompt upsampling. Use when the user asks "how do I run inference", "how do I generate a video", "how do I serve the model", "what parameters should I use", or any question about running the model to produce outputs.NVIDIA/cosmos-framework323
- Cosmos3 InferenceGuide users through running Cosmos3 inference — offline batch generation, online serving with Ray and Gradio, parallelism options, input formats, sampling parameters, and prompt upsampling. Use when the user asks "how do I run inference", "how do I generate a video", "how do I serve the model", "what parameters should I use", or any question about running the model to produce outputs.NVIDIA/cosmos-framework323
- Cosmos3 Post TrainingGuide users through Cosmos3 supervised fine-tuning (SFT) post-training: preparing the example dataset and Wan2.2 VAE, converting the base checkpoint to DCP, launching distributed training (paired launch shell recommended, raw `torchrun` as an alternative), running T2V/I2V/V2V inference with the trained DCP checkpoint, and optionally exporting it to Hugging Face safetensors. Use when the user asks how to post-train Cosmos3, fine-tune on a custom video dataset, export a trained checkpoint, or invoNVIDIA/cosmos-framework323
- Cosmos3 Post TrainingGuide users through Cosmos3 supervised fine-tuning (SFT) post-training: preparing the example dataset and Wan2.2 VAE, converting the base checkpoint to DCP, launching distributed training (paired launch shell recommended, raw `torchrun` as an alternative), running T2V/I2V/V2V inference with the trained DCP checkpoint, and optionally exporting it to Hugging Face safetensors. Use when the user asks how to post-train Cosmos3, fine-tune on a custom video dataset, export a trained checkpoint, or invoNVIDIA/cosmos-framework323
- Cosmos3 SetupGuide users through Cosmos3 installation, environment setup, checkpoint downloading, and verification. Use when the user asks "how do I install cosmos3", "how do I set up the environment", "how do I download checkpoints", "how do I use Docker", or any question about getting the package running for the first time.NVIDIA/cosmos-framework323
- Cosmos3 SetupGuide users through Cosmos3 installation, environment setup, checkpoint downloading, and verification. Use when the user asks "how do I install cosmos3", "how do I set up the environment", "how do I download checkpoints", "how do I use Docker", or any question about getting the package running for the first time.NVIDIA/cosmos-framework323
- Fault Injection LoopClosed-loop fault injection and attribution accuracy benchmark. Draws from a prioritized pool of (fault_type, rank, iter, nodes) experiments and submits them 2 at a time via sbatch — waiting for each pair to finish before submitting the next — to bound filesystem load. GPU-related faults are front-loaded in the pool. After all jobs complete, runs /log-analysis and /fr-analysis on every experiment, scores attribution vs. ground truth, aggregates gaps, and iterates on attribution modules to close NVIDIA/nvidia-resiliency-ext305
- Fr AnalysisAnalyze PyTorch NCCL flight-recorder (FR) dumps to identify collective operation hangs and isolate the responsible ranks using CollectiveAnalyzer. Use when a distributed training job hangs due to an NCCL collective timeout and FR dump files are available. Detects the wavefront process group where collectives diverge and returns the root-cause suspect ranks.NVIDIA/nvidia-resiliency-ext305
- Log AnalysisAnalyze a SLURM job log file for failure root-cause attribution and restart decisions using NVRxLogAnalyzer. Use when you have a SLURM training job log and need to determine why the job failed and whether it should be restarted. Performs per-cycle chunking, fast-path pattern matching, and LLM-based classification.NVIDIA/nvidia-resiliency-ext305
- Nvrx AttrOrchestration layer over nvidia_resiliency_ext attribution modules. Provides log-analysis, fr-analysis, and a Megatron-LM-oriented fault-injection feedback loop for benchmarking attribution quality on SLURM workloads.NVIDIA/nvidia-resiliency-ext305
- Rest Core Grpc ProxyBuild or migrate infra-controller REST API endpoints that call on-site NICo Core through the generic Core gRPC proxy. Use when working on REST-to-Core operations, ExecuteCoreGRPC, coreproxy, forge.Forge methods, creating new proxied REST endpoints, or migrating bespoke workflows to the gRPC proxy.NVIDIA/infra-controller216
- Osmo AdminUse only for offline/local OSMO service-config admin requests involving explicit config roots or values files, or to ask for one when a file-specific config request omits it. Do not inspect the workspace to infer a root. Do not use for live workflow support, resource capacity, pod/node diagnostics, or cluster operations, except live service-config paths that must be refused.NVIDIA/OSMO190
- Osmo DeployHow to deploy OSMO to a Kubernetes cluster on Azure (AKS), AWS (EKS), MicroK8s (single-node), or any kubectl-reachable cluster (BYO). Use this skill whenever the user asks to install, deploy, set up, or stand up OSMO; whenever they ask to provision an OSMO cluster; whenever they mention deploy-osmo-minimal.sh, deploy-k8s.sh, or "OSMO helm install"; whenever they ask to wire up workflow storage (MinIO / Azure Blob / S3); or whenever they ask to add a GPU pool to an OSMO cluster, install KAI schedNVIDIA/OSMO190
- Osmo UserDrive the OSMO CLI for cloud-robotics compute on behalf of an end user: check resources, submit/monitor/debug/explain workflows, fetch logs and Grafana/Kubernetes links, inspect direct data storage, manage workflow apps, and set workflow credentials. Use whenever the user asks about OSMO pools, quota, GPUs, or nodes, or about submitting, listing, querying, monitoring, or troubleshooting workflows — including failed, PENDING, queued, stuck, or image-pull-blocked workflows, or when they ask to insNVIDIA/OSMO190
- Documentation StyleNVCF docs style: no bold, no emojis, no em-dash or en-dash, ASCII-only prose. Use when editing markdown, READMEs, AGENTS.md, plans, or PR descriptions.NVIDIA/nvcf172
- Nvcf Explore StackNavigate and explain the NVCF self-hosted stack inside the monorepo. Maps helmfile releases to their charts, image-source subtrees, helm hooks, namespaces, and `needs:` dependency chains. Reads deploy/stacks/self-managed/helmfile.d/*.yaml.gotmpl and deploy/stacks/nvcf-compute-plane/helmfile.d/*.yaml.gotmpl as the source of truth for ordering and versions, with imports.yaml for upstream provenance. Use when a user or developer asks "what deploys X", "what does X depend on", "what hooks run for X"NVIDIA/nvcf172
- Nvcf Self Managed CliInstall, operate, and tear down self-hosted NVIDIA Cloud Functions (NVCF) deployments with nvcf-cli. Use for control-plane or compute-plane install, status checks, cluster registration, function deploy/invoke, task create/list/cancel/delete, API keys, admin tokens, JWKS rotation, failed-install diagnosis, and uninstall or down workflows. Trigger keywords: nvcf, nvcf-cli, self-hosted nvcf, self-managed nvcf, NVCFBackend, NVCA, NCP, ICMS, helmfile, control plane, compute plane, LLM function, OpenANVIDIA/nvcf172
- Nvcf Self Managed InstallationInstall and deploy the nvcf-self-managed-stack helmfile bundle for NVCF self-hosted deployments. Covers clean control-plane installation, teardown, helm values overrides, image pull secrets, and debugging installation failures. Use when deploying, installing, reinstalling, tearing down, or configuring the NVCF self-managed control plane stack, or when the user mentions helmfile, self-managed, self-hosted, control plane installation, or nvcf-self-managed-stack. Do NOT use for local k3d developmenNVIDIA/nvcf172
- Nvcf Self Managed PrerequisiteInstall the prerequisites the NVCA operator / compute plane needs before nvcf-nvca-install can succeed: the operator tool nvcf-cli (required by the compute-plane stack's register-cluster step), KAI Scheduler (for the KAIScheduler feature gate), and the SMB CSI driver (for the sharedStorage Samba sidecar PVCs). The two cluster components are cloud-neutral helm installs at the NVCF-validated version pins; same install on AKS, EKS, GKE, k3d, or bare metal. Use when the user mentions NVCA prereqs, nNVIDIA/nvcf172
- Nim Operator InstallInstall NVIDIA NIM Operator on Kubernetes with prerequisite checks, optional NVIDIA GPU Operator dependency installation, public or local Helm chart selection, optional Dynamo support, and optional KServe compatibility verification. Use when a customer wants to install or upgrade the NIM Operator itself, with or without Dynamo and KServe, but does not want to deploy a NIM inference model yet.NVIDIA/k8s-nim-operator159
- Nim Operator UninstallSafely uninstall NVIDIA NIM Operator from Kubernetes with inventory checks, explicit approval gates for destructive actions, optional custom resource cleanup, optional CRD removal, and post-uninstall validation. Use when a customer wants to remove or clean up the NIM Operator itself, not the GPU Operator or unrelated cluster dependencies.NVIDIA/k8s-nim-operator159
- Tripy CompilationWork with the nvtripy compilation pipeline. Use when: using tp.compile, creating InputInfo or DimensionInputInfo, understanding the Trace → MLIR → TensorRT flow, configuring optimization levels, working with Executable objects, debugging compilation, using dynamic shapes or NamedDimension.NVIDIA/TensorRT-Incubator125
- Tripy ConstraintsAuthor input/output constraints for nvtripy operations using the declarative constraint DSL. Use when: defining input_requirements or output_guarantees, writing @wrappers.interface decorators, auto-casting dtypes, using GetInput/GetReturn/OneOf/If/Equal, debugging constraint validation errors.NVIDIA/TensorRT-Incubator125