개발 스킬

Diagnose slow loading, high memory use, and low frame rates in USD 3D scenes. It coordinates measurement, scene validation, and optimization, then produces reports comparing available before-and-after evidence and an optimized scene when changes are performed.

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• Investigate slow loading in USD scenes
• Diagnose low frame rates and high memory use
• Review repeated structures in large CAD scenes

Generates a standalone YOLO26 detection and tracking service scaffold for VSS Search and Alerts. Includes DeepStream configuration, Docker files, and tests, with Kafka integration guidance and clear limits on validation before model assets are available.

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• Creating a standalone YOLO26 service for VSS
• Generating a scaffold before model assets are ready
• Checking Kafka integration with VSS workflows

Use when controlling NVIDIA Broadcast through MCP to apply effects, process local media, select devices, or change camera resolution, or when Broadcast is missing or too old to expose the gateway and the user wants it installed; not for Broadcast app settings outside the MCP gateway.

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Build apps for viewing USD, a 3D scene format, using guidance matched to browser streaming or local execution. The skill selects implementation references for rendering, camera controls, selection, interface layout, and validation.

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• Build a browser viewer for USD scenes
• Create a local workstation viewer
• Add camera controls and object selection

Choose and install the right NeMo Relay option for a command-line tool, a Python, Node.js, or Rust application, or a supported framework integration. Check basic availability and report the installation result before moving to runtime setup.

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• Trying NeMo Relay through a temporary CLI run
• Adding a Relay package to an existing application
• Installing Relay for a supported framework

Guides typed data integration in NeMo Relay by choosing codecs, converters between application values and JSON. Helps preserve middleware edits and caller-visible responses while distinguishing application data conversion from provider-specific LLM request and response handling.

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• Connecting existing domain models to NeMo Relay
• Choosing codecs for Python or Node.js
• Making provider payloads inspectable by middleware

Use when a task needs to search or add documents through NeMo Retriever MCP.

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Migrate existing NeMo Flow code and documentation to NeMo Relay by updating explicit names, imports, paths, and configuration references. A bundled helper previews changes, while language-specific checks cover dependencies, generated files, and remaining legacy identifiers.

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• Migrate imports and dependencies across languages
• Preview identifier and path changes
• Update CLI examples and documentation

Troubleshoot applications where NeMo Relay is installed but runtime behavior is missing or incorrect. It helps isolate loading failures, inactive execution contexts, missing events, and plugin or export configuration issues before changing application settings.

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• Diagnose native extension or shared-library loading failures
• Investigate missing lifecycle events
• Separate execution contexts across concurrent requests

Package reusable NeMo Relay behavior as an embedded component or an independently distributed plugin. Design shared configuration, validate it before activation, and register behavior with rollback handling so failed setup does not leave partial changes active.

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• Share runtime behavior across Relay applications
• Validate plugin configuration before activation
• Package a native Rust plugin or a local gRPC worker

Tune adaptive behavior in an instrumented NeMo Relay application using runtime data. Configure tool parallelism, prompt caching, and state storage, then compare changes against a baseline to check latency, correctness, and failure rates.

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• Evaluate adaptive behavior using runtime telemetry
• Tune tool parallelism against a baseline
• Evaluate prompt-cache planning

Keep NeMo Relay event histories separate for concurrent requests, tasks, and agents. The skill guides independent scope stacks, which track nested operations, and preserves parent-child relationships when execution crosses asynchronous tasks, threads, or workers.

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• Separate event histories for concurrent requests
• Preserve parent-child links across async tasks and threads
• Prevent request-specific middleware from leaking

Integrate NVIDIA NeMo Fabric into an application or evaluation system through its Python SDK. Map application settings into typed configuration, choose an execution lifecycle, validate prerequisites, and handle standardized results, artifacts, and telemetry.

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• Integrate agent execution into an application
• Build configurations for different evaluation conditions
• Manage single calls and conversations with retained state

Wrap existing tool functions and language model calls with NeMo Relay to record execution events, process requests through middleware, and apply guardrails. Guides integration while preserving the original functions’ arguments, results, and business logic.

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• Adding execution events to existing tool calls
• Wrapping model provider calls with NeMo Relay
• Integrating request processing and execution guardrails

Configure how Jetson Orin or Thor assigns shared high-speed connections on a custom carrier board. The skill checks board schematics and platform guides, updates ODMDATA settings, and coordinates controller-specific device tree changes through related skills.

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• Choosing high-speed lane assignments for a custom carrier
• Changing PCIe, MGBE, USB3, or UFS lane allocation
• Investigating controller detection or boot failures after flashing

Split a pull request into smaller drafts based on CODEOWNERS, the file that assigns review ownership. Propose groups of changes that need fewer reviewer teams, keep tests with their code, and create the drafts after approval.

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• Split a large pull request into smaller reviews
• Reduce reviewer groups required for each pull request
• Plan dependencies between split pull requests

Understand the Isaac for Healthcare workflow architecture and discover supported workflows and run modes from the current checkout. Get directed to the appropriate stage skill for setup, data processing, training, or validation.

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• Find where to start with i4h workflows
• Understand the roles of Scene, Task, and Engine
• Find the right skill for data processing or training

Prepare a workspace for customizing Jetson board support software. Sets up Git tracking for changes, arranges kernel and related source files, and locates or extracts the NVIDIA Crosstool-NG compiler tools needed for later builds.

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• Preparing a workspace after BSP image initialization
• Arranging source files from local archives for offline setup
• Using a customer Git fork as the BSP source

Prepare configuration files for a custom Jetson carrier board using a reference development kit. Creates board-specific copies and a device tree overlay template, a file for hardware configuration differences, with build registration and flash configuration wiring.

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• Preparing initial configuration for a custom Jetson carrier board
• Copying reference settings under custom board names
• Creating and registering a device tree overlay template

Linting and formatting for Megatron-LM. Covers running autoformat.sh, tools (ruff, black, isort, pylint, mypy), and code style rules.

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Investigate a failing GitHub Actions run or job and create a GitHub issue for the failure.

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Check Jetson video encoding and decoding capabilities by comparing live API reports, verified NVIDIA samples, and official documentation. Distinguish documented product support from observed behavior for specific formats, bit depths, dimensions, and tested operations.

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• Checking support for video formats and bit depths
• Comparing API reports with official product documentation
• Verifying encoding and decoding under specific conditions

Configure MIPI or GMSL cameras on Jetson Thor and Orin custom carrier boards. Check sensor support and wiring, then generate and validate a device tree overlay, an additional hardware configuration, using existing sensor definitions.

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• Enable a MIPI camera on a custom carrier board
• Configure wiring for a GMSL camera
• Add another sensor to an existing camera setup

Add EAGLE-3 or draft-model speculative decoding to a Jetson vLLM server when TPOT is the bottleneck.

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