NVIDIA

Deploy and configure the NVIDIA DOCA Telemetry Service (DTS) as it is actually run: (A) BlueField (built-in static pod), (B) Host (docker) on x86_64 / aarch64, or (C) DPF (DPUService / helm chart) — plus the shared configuration layer (`dts_config.ini`,…

Use this skill when the operator is driving the DOCA ngauge tool to analyze, visualize, and debug network performance on a single node by probing NIC hardware counters and storing collected data in HDF5 format. Trigger even when the user does not say "DOCA…

Use this skill when the user is running DOCA Perftest (doca_perftest), the RDMA benchmark that measures bandwidth, message rate and latency for RDMA verbs, point-to-point or across a cluster, with an optional DOCA Verbs backend (-r dv) or GPUNetIO engine…

Use this skill when the operator is driving the DOCA Pipeline Language (DPL) Developer Tools to inspect or debug DPL programs on a BlueField DPU — DPL Nspect for runtime state such as tables, keys, and counters, DPL Debugger for packet flow, DPL Admin for…

Use this skill when the operator is driving the DOCA Telemetry Traceback application to monitor telemetry data locally on a BlueField DPU or ConnectX NIC, detect long-term trends and threshold breaches with user-defined rules, and capture pre-event history…

Use this skill when the user is doing hands-on DOCA AES-GCM work on a BlueField DPU or ConnectX NIC — configuring `doca_aes_gcm_task_encrypt` / `_task_decrypt`, querying `doca_aes_gcm_cap_*` for per-key-type (only `DOCA_AES_GCM_KEY_128` / `_256` — AES-192 not…

Use this skill for hands-on DOCA Arg Parser CLI work on a shipped sample or new DOCA-using app — adding / removing / renaming flags; wiring `doca_argp_init` → register params → `doca_argp_start` → `doca_argp_destroy` in order; picking a parameter type from…

Use this skill when the user is deploying or operating the DOCA Argus Service — the packaged BlueField-side runtime-security container that watches the BlueField and attached host for suspicious activity, integrity violations, and operational anomalies, and…

Add SimReady capability docs, requirement indexes, validation stubs, and registrations for new requirement families.

Use for adding SimReady feature adapters that mutate USD assets between exact feature or profile versions.

Use for adding new SimReady feature markdown pages and matching JSON manifests from nv_core/sr_specs/docs/guides/features/feature-template.md, including FET runtime naming, integer feature versions, requirement mapping, validation strategy, index entries, and…

Use for adding SimReady profile versions with feature bundles, docs, indexes, and validation notes.

Use for adding atomic SimReady requirements with stable IDs, docs, examples, indexes, and validator follow-up.

Use for adding or updating SimReady Foundation runtime tests in a tier-owned Benchmark test package, wiring the package through the tier descriptor and wheel, and keeping feature/profile documentation and test registration synchronized. Delegates generic…

Use for adding executable SimReady validators that report requirement IDs with focused pass/fail coverage.

Use for repairing exact FET_000_ISAAC SimReady conformance: Isaac packaging Core (clean folder, thumbnail, physics attribute/schema source layer). Use when a profile, validation report, or user request names FET_000_ISAAC; default to version `0.1.0`.

Mandatory acceptance policy for source-referenced CAD reconstruction; separates geometric fidelity from structural and persistence checks.

Inspect Kubernetes or OpenShift resources and perform only the mutations exposed by the chart-managed SRE API proxy.

Use when inspecting GPU/model capacity, live GPU utilization, GPU memory, power or temperature telemetry, model serving metrics such as queue depth, KV-cache usage or request latency, Prometheus/Thanos metric queries, model Routes or NodePorts, OpenShell…

Keep the memory inside its size limits by compacting rather than truncating, so it stays readable as it ages.

Check the memory against its schema and fix what can be fixed, so the store degrades visibly rather than silently.

Create and update the memory pages that the ranking job gates its top tier on, from evidence the selector collected.

Update a bounded preference policy from the user's repeated corrections, without ever overwriting the user's own choices.

Search the Outlook mailbox via Microsoft Graph to find and read emails that help answer user questions.

Profiles and optimizes TensorRT-LLM host/CPU overhead using line_profiler (with nsys support planned). Runs iterative profile-analyze-optimize-validate rounds. Use when GPU utilization is low or optimizing PyExecutor throughput.

Compile TensorRT-LLM on a compute node inside a Docker container. Use this when already on a compute node with GPUs visible.

Execute a TensorRT-LLM workload locally in Docker. Runs a fully-resolved Docker command in background, monitors completion, reads logs, and reports results. Workflow-agnostic — does not need to know if the workload is pytest, eval, benchmark, or a custom…

Submit and monitor a Slurm job on a local cluster. Supports two modes: (1) Persistent allocation (default) — allocates nodes once via nohup salloc, imports the container once, installs once, and reuses across runs by setting SLURM env vars and running the…

Remote SLURM cluster development via SSH. Use when running jobs, profiling, or developing on a remote SLURM cluster with pyxis/enroot containers. Covers SSH connection management, srun/sbatch/salloc job patterns, tmux-based allocation persistence, file…

Compile TensorRT-LLM on a SLURM cluster. Covers submitting a batch job with a container image, monitoring the job, and verifying the build. Use when the user wants to compile TRT-LLM remotely via SLURM rather than on a local compute node.

Runs model-level and module-level tests for TensorRT-LLM. First classifies the test scope (module test or model test), then dispatches to the appropriate workflow. Model tests are further classified by type (functionality/smoke test, benchmark, or…

Review, design, and refactor TensorRT-LLM PyTorch MoE code for architecture fit, clean code, maintainability, and testability. Always use for any modification, review, refactor, or design planning that touches MoE modules, including…

Review AI agent skills before installation using NVIDIA SkillSpector and source-aware semantic review. Use when asked whether a skill or downloaded skill folder is safe, trustworthy, installable, over-permissioned, or malicious.

Look up and update single orders in an e-commerce backend. Use when the user asks about an order's status or wants one order changed.

Explains compiler and runtime errors. Use when the user pastes an error message and asks what it means.

Look up and update single orders in an e-commerce backend. Use when the user asks about an order's status or wants one order changed.

Look up and update single orders in an e-commerce backend. Use when the user asks about an order's status or wants one order changed.

Explains compiler and runtime errors. Use when the user pastes an error message and asks what it means.

Explains compiler and runtime errors. Use when the user pastes an error message and asks what it means.

A local greeting skill that never accesses the network

Create or update a NemoClaw pull request with its authoring-session report, then follow its checks and automated reviews.

Run a NemoClaw daytime maintainer pass over release-targeted work. Use for the maintainer queue or a requested recurring pass.

Set up, repair, or check a NemoClaw contributor checkout. Also handles requested development CLI exposure, runtime onboarding, and pinned-agent launch.

Validate the staging NemoClaw Brev Launchable through its web journey or a deployed environment. Use for advisory validation outside automated E2E.

Triage, diagnose, debug, or fix failing or flaky NemoClaw E2E tests. Trace every assertion and downstream gate before a repair push or rerun. Excludes status-only and dispatch-only requests.

Classify one failed NemoClaw GitHub Actions job using bounded, redacted logs and optional retained artifacts.

Run or inspect NemoClaw live E2E evidence. Routes requested local execution, trusted GitHub dispatch, and release-evidence inspection.

Find the repository skill for a NemoClaw task or browse the skill catalog. Use when skill selection needs help.

Run the weekly OpenShell release captain checklist. Monday checks the pre-release train, triages failures and the compatibility review, and drafts the coordination post. Tuesday promotes the qualified pre-release after approval, verifies publication, and…

Watch and monitor GitHub Actions workflow runs using the gh CLI. Use when the user wants to check workflow status, watch a running workflow, view CI/CD jobs, or monitor build progress. Trigger keywords - watch pipeline, pipeline status, CI status, check…

Start up, tear down, and configure the local Kubernetes development environment for OpenShell. Uses k3d (Docker-backed k3s) + Skaffold + Helm. Covers cluster lifecycle, optional add-ons (Keycloak OIDC, Envoy Gateway), HA testing, and port mappings. Trigger…

Debug why an OpenShell gateway deployment is unhealthy, unreachable, or unable to create sandboxes. Use for gateway health failures, Docker/Podman runtime issues, Helm failures, Kubernetes scheduling, TLS or auth, gateway interceptors, supervisor middleware…

Prepare structured OpenShell vulnerability reports and create authorized private GitHub security advisory drafts. Use when drafting a GHSA, turning a finding into an advisory, or organizing a report for security review.

Review an authorized security issue or private GitHub advisory for validity, severity, and a remediation plan.

Reconcile contributor skills, AGENTS.md, CONTRIBUTING.md, issue and PR templates, and workflow references after repository workflow changes.

Guide agents through using the OpenShell CLI (openshell) for sandbox management, gateway registration, provider configuration and refresh, profile management, policy iteration, settings, service exposure, BYOC workflows, and attached-provider inference.…

Use this skill by default for any UI-related work or with NVIDIA Elements (nve-*), including creating, editing, reviewing, or debugging HTML, CSS, layout, theming, components, applications, prototypes, Claude Artifacts, Codex Sites pages, and standalone UI…

Audit an Elements project or directory for one concept implemented or owned in more than one place, including duplicate or parallel implementations that should share one internal module, related code scattered across feature directories, and older paths made…

Audit an Elements project, directory, or component for mechanisms that may not need to exist, including redundant state, attribute reflection, forwarding indirection, lifecycle plumbing, per-instance allocations, and custom synchronization that duplicates…

Create a monthly NVIDIA Elements “What’s New” docs page, matching video, and concise, copy-ready summary from local release tags, tagged changelogs, package versions, and commit history. Use for a scheduled release PR, monthly update, release roundup,…

Create the 24–36 second marketing video for a monthly NVIDIA Elements “What’s New” post, rendered to projects/site/public/static/video/releases/MM-YYYY.webm from real Elements components, with feature snapshots inserted into the post. Use after the…

Verify every starter supported by the stable production NVIDIA Elements CLI from a fresh consumer environment, investigate failures, and prepare at most one focused repair pull request. Use for nightly or scheduled production starter verification and starter…

Perform one small, scoped maintenance improvement to the Elements monorepo and open a single reviewable pull request. Use this skill for scheduled or unattended upkeep runs that improve unit test coverage for one file, fix one behavioral bug in one module, or…

Generate a production availability report for NVIDIA Elements packages, skill registry, and documentation.

Perform a NeMo Relay project release-version change across project-owned manifests, dependencies, lockfiles, and generated attribution surfaces while keeping just set-version coverage complete. Do not use for dependency-only updates or adding a package when…

Verify whether a tagged NeMo Relay release reached GitHub Actions, tagged Go module source, crates.io, PyPI, and npm. Use for publication status of a specific tag; not for publishing packages or creating tags.

Create and push a signed, annotated NeMo Relay beta tag from its release branch or validated main. Use when cutting a beta tag; not for RC or stable tags.

Create and push a signed, annotated NeMo Relay release-candidate tag from its validated release branch. Use when cutting an RC tag; not for code freezes or stable tags.

Create and push a signed, annotated NeMo Relay stable tag, then prepare an unpublished GitHub Release draft for review. Use when cutting a stable release; not for beta or release-candidate tags.

Execute a requested NeMo Relay code freeze by cutting a release branch, updating nightly alpha configuration, bumping main, and preparing the required PR. Do not use for an ordinary version bump or release-note draft.

Add or change a public NeMo Relay runtime API across Rust and affected language bindings when no more specific maintainer skill owns the domain. Do not use for middleware additions, internal refactors, binding-local fixes, or docs-only changes.

Add a new NeMo Relay guardrail or intercept type, registration surface, or pipeline stage. Do not use for changing an existing middleware implementation without a new middleware contract.

Use this skill when integrating NVIDIA NeMo Fabric into a consumer application, service, evaluation harness, or platform through the typed Python SDK — translating the consumer's own application, job, or deployment config into an in-memory FabricConfig,…

Add or substantially change a first-party NVIDIA NeMo Fabric adapter, including repository packaging, discovery metadata, catalogs, CI wiring, and validation.

Build, migrate, review, and maintain third-party NVIDIA NeMo Fabric adapters against the public adapter contract. Use when creating adapter or target descriptors, mapping AgentConfig into an agent harness or custom-agent runtime, implementing…

Contribute a new NVIDIA NeMo Fabric public API surface safely, with Rust, CLI, Python, TypeScript, schema, adapter, and documentation parity in mind

Maintain NVIDIA NeMo Fabric Rust, Python, and TypeScript dependencies, package metadata, module paths, native artifacts, lockfiles, license evidence, and release-facing build surfaces

Choose and run the right NeMo Fabric validation matrix for a change instead of using one fixed test list

Verify whether a tagged NeMo Fabric release is deployed to crates.io, PyPI, and npm. Use when checking publication status for a specific release tag; not for publishing packages or creating tags.

Contribute documentation or example changes that stay aligned with NeMo Fabric public behavior

Use when the user wants to run data augmentation on an annotated dataset, configure augmentation parameters, check augmentation status, or understand what each QA augmentation type does (BCQ, MCQ, GQA, DMCQ, DSQA, ENQA, WMCQ)

Use when running by-action VLM evaluation (per-action-clip inference + accuracy metrics) against the BP evaluation-ms HTTP API. Invoked as /sop-by-action-eval <inputs.yaml> [natural language parameter overrides]

Use when running the e2e evaluation pipeline (temporal segmentation + action recognition + accuracy) against the BP evaluation-ms HTTP API. Invoked as /sop-e2e-inference <inputs.yaml> [natural language parameter overrides]

Autonomous end-to-end orchestrator for SOP fine-tuning. Runs the full Import → Augment → DDM Train → VLM Train → Evaluate → RCA loop. Interprets RCA findings across DDM, VLM and augment axes, applies config fixes autonomously, and iterates until success…

Root cause analysis for SOP monitoring pipeline failures. Analyzes end-to-end evaluation logs, DDM temporal segmentation, VLM action recognition, training data, and fine-tuning configs to identify failure patterns and produce an evidence-driven RCA report…

Build a custom VSS SOP blueprint from the VSS 3.1 base, then deploy and test in a loop until fully operational. Use when asked to create the SOP blueprint structure, customize VSS compose for SOP, configure SOP services, set up the VSS agent for SOP, or…

Build the DS-SOP Docker image and deploy the VSS SOP blueprint end-to-end. Use when asked to deploy SOP, build DS SOP, install SOP, set up the SOP pipeline, verify SOP models, start the SOP blueprint, simulate RTSP for SOP, run the SOP API test, or tear down…

Use this skill when building, deploying, evaluating, debugging, or measuring latency for the DeepStream SOP Inference Microservice — a GPU-accelerated FastAPI service that detects whether operators perform assembly-line steps in order via event boundary…

Reference for translating a validated ML request into KumoAI graph configuration, PQL (predictive query language), and Kumo Predict SDK code. Normally loaded by the kumo-data-science skill after its intake checks pass, rather than invoked directly. Triggers:…

Use for any work with KumoAI predictive models on Snowflake data: train a model, score data with a trained one, or answer questions about an existing model or run — explain its predictions, diagnose its training, publish, reload or retain it, train or score…

Inspect and interpret gradient attribution (including requests called radiant attribution), column analysis and sampled explanation subgraphs from a Kumo model trained in a Snowflake Python notebook. Locate the correct run's explanation artifacts, explain…

Publish and reload Kumo models through the Snowflake registry, reconstruct compatible scoring inputs, and retain or retrieve run diagnostics and output files through a Snowflake stage. Use for saved-model scoring and artifact retention without retraining by…

Train with caller-supplied labels, weights and splits or score exact entity/anchor rows through Snowflake source tables using the local Kumo API. Use when generated populations do not represent the requested rows.

Configure a Kumo model plan for a user-defined Snowflake task, including encoders, run mode, bounded experiments, sampling, optimization, architecture, metrics and refits. Preserve target and evaluation constraints and use existing diagnostics for comparisons.

Evaluate an existing Kumo training run in a Snowflake Python notebook using aggregate metrics, trial results, learning curves, refit evidence, and training progress. Use for model-quality questions or training diagnostics; inspect existing results without…

Orchestrate Kumo UI ticket work from GitHub issue analysis through implementation, validation, and optional PR creation.