개발 스킬

Build an adapter that exposes API, database, or object storage data as files in Mirage. Covers Python or TypeScript implementation, mount configuration, and tests for reading, access restrictions, and supported changes.

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• Connect an API or database to Mirage
• Implement file operations for a custom data source
• Package a reusable Mirage adapter

Run a Unitree G1 navigation demo in Isaac Sim through cloud control. The skill coordinates browser streaming, route checks, operator approvals, mission submission, and monitoring, with a guarded process for releasing the simulated support gantry.

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• Run a cloud-controlled humanoid navigation demo
• Move toward a selected object in the simulated scene
• Start the demo stack for inspection

Build a custom CDF Airflow DAG, a workflow defining task order, from existing processing stages when standard workflows do not fit. Generate configuration and supporting code, or compare model, container, and prompt choices against a specification.

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• Build a workflow containing only detection and captioning
• Add, remove, or reorder existing CDF stages
• Generate workflow code and execution configuration

Develop reusable external Holoscan Modules with the Holoscan CLI, from scaffolding and testing to Python bindings and package creation. Verify that applications can use installed packages in a clean environment, with checks for each requested format.

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• Creating a reusable Holoscan operator module
• Adding and testing Python bindings
• Using an installed module from another application

Diagnose a specific failing, hanging, or incorrect Holoscan CLI command. The skill guides reproducible investigation, isolates the responsible layer, applies a minimal fix, and checks the same command and focused tests to verify the result.

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• Diagnose a reproducible holoscan build failure
• Investigate a command that hangs
• Trace unexpected application output

Find official NVSHMEM and NVSHMEM4Py documentation for installation, APIs, runtime settings, and troubleshooting. Receive concise explanations with direct source links, relevant version details, and explicit uncertainty when the requested documentation or compatibility evidence cannot be verified.

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• Finding installation instructions for a specific release
• Understanding APIs and runtime settings
• Checking NVSHMEM4Py compatibility

Review or prepare an NVSHMEM CUDA kernel for TMA, a GPU data-transfer mechanism. Assess hardware suitability, add shared-memory registration and launch settings, and consider direct transfers from shared memory when an intermediate copy is proven redundant.

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• Assessing TMA suitability for an NVSHMEM kernel
• Preparing a selected kernel and its launch settings for TMA
• Reviewing whether an intermediate memory copy can be removed

Diagnose NVSHMEM launch failures, crashes, hangs, and communication problems using exact commands and complete logs. Separates observations from likely causes, checks version-specific documentation, and prepares a Markdown bug report for the user to submit.

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• Investigate launch failures, crashes, and hangs
• Distinguish NVSHMEM defects from application or environment issues
• Check whether a known issue applies to the installed version

Choose the appropriate NVSHMEM performance workflow for collecting benchmark evidence, selecting communication between nodes, assigning network adapters to processing elements, or assessing TMA transfers between peer GPUs. This skill routes requests to specialists that perform the technical work.

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• Choose a workflow for baseline performance measurement
• Find the specialist for inter-node transport selection
• Route network adapter assignment questions

Assess whether NVSHMEM suits communication between GPUs, then learn its core concepts and plan a first C/C++ or Python program. Guidance covers program structure, compilation, launch commands, and appropriate next steps.

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• Assessing whether NVSHMEM fits a multi-GPU application
• Learning GPU communication and shared-memory concepts
• Designing, compiling, and launching a first C/C++ program

Connect Omniverse scenes to the Newton physics engine through ovnewton. The skill guides scene import, simulation stepping, synchronization of scene edits, and publication of simulation results for rendering, with setup guidance for supported solvers.

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• Importing a populated ovstage into Newton
• Applying scene edits to a running simulation
• Publishing physics results for Omniverse rendering

Guide the creation and maintenance of an agent’s ethos.md, a record of its purpose, boundaries, and success criteria. Locate existing files and route requests to intent exploration or editing, carrying forward information already confirmed.

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• Defining a new agent’s purpose and boundaries
• Locating an existing ethos.md and identifying gaps
• Updating specific rules or revisiting the agent’s intent

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

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

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

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

Build, migrate, and review adapters that connect external agent runtimes to NVIDIA NeMo Fabric. It guides configuration translation, startup and shutdown, discovery metadata, and validation against the public contract, the rules defining how adapters interact with Fabric.

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• Building an adapter for an external agent runtime
• Migrating an adapter to the public contract
• Reviewing configuration mapping and runtime isolation

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

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

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

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

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

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

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