NVIDIA Corporation

Create a Jetson target-platform YAML profile by selecting a reference product and flash configuration, with optional details for a custom carrier board. The workflow also sets the active profile used by subsequent setup and customization skills.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating the first Jetson target-platform profile
• Recording a custom carrier board and its reference platform
• Adding a profile for another Jetson product

Rebuild Jetson hardware configuration files, kernel images, and modules after source changes. Selects a build mode from the changed repositories or an explicit request, then records the outputs in a manifest for a separate deployment step.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Rebuilding hardware configuration after a change
• Rebuilding the kernel and modules
• Resuming an interrupted build

Configure Jetson CPU, GPU, and memory-controller clock frequencies before flashing an image. Edit firmware settings and the boot script to fix or limit frequencies, change frequency-control policies, and enable or disable automatic frequency scaling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing fixed CPU or GPU frequencies for performance tests
• Setting maximum or minimum device clock frequencies
• Enabling or disabling automatic memory-controller frequency scaling

Prepare a Jetson BSP image, the board's system software, from local Jetson Linux and sample root filesystem archives. Apply GPU binaries for the selected target, verify existing images, and record image metadata in its profile.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a BSP image after selecting a Jetson target
• Extract matching Jetson Linux and root filesystem archives
• Check and reuse an existing Linux_for_Tegra directory

Investigate a failed GitHub Actions job and turn its logs into a structured bug issue for Megatron-LM. Collects failing tests, errors, and related pull requests, checks for duplicates, and identifies an assignee from test history.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a bug issue from a failed GitHub Actions job
• Summarize failing tests and relevant log excerpts
• Check for an existing issue covering the same test

Guide checks of a customized Jetson system image, from file consistency to tests on a flashed device over SSH or UART. Device access is defined, while the broader validation procedure remains a skeleton.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check system image files without a connected device
• Run smoke tests after flashing a Jetson device
• Investigate kernel and module version mismatches

Manage nvfancontrol fan profiles for Jetson and Tegra boards. Add, remove, or edit temperature-based fan curves and boot defaults in board support files, while tracking changes and preserving configuration differences between related carrier boards.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• List fan profiles and boot defaults
• Add or remove a fan profile
• Adjust fan curves using measured platform data

Collect a short, read-only report from a running Jetson device, covering its model, L4T release, kernel, OS, power mode, and uptime. Use it to record the environment before performance, regression, or compatibility tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Record the device environment before performance tests
• Check the L4T release after flashing a Jetson
• Capture baseline information for compatibility tests

Measure language model response times and generation speed on Jetson using wrappers for vLLM, llama.cpp, or Ollama. Produces structured JSON results to assess models, power modes, runtime settings, and performance changes after tuning.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Measure response times for a deployed model
• Compare performance before and after tuning
• Evaluate models and power modes under controlled conditions

Install, repair, or inspect NVIDIA Video Codec SDK and PyNvVideoCodec on Jetson. Verify readiness with official samples that encode and independently decode one frame, and report what passed, what failed, and the next action.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Install a video SDK on Jetson
• Inspect installed packages and the selected Python environment
• Verify one-frame encoding and decoding with official samples

Configure speculative decoding on Jetson vLLM servers, letting a smaller model propose tokens for the main model to verify. Adjust memory allocation and benchmark before and after to assess generation speed under low concurrency.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reducing slow token generation on Thor or AGX Orin
• Choosing EAGLE-3 or a small draft model
• Comparing generation speed before and after configuration

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• 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

Register existing Jetson and IGX reference documents in the active platform profile so downstream skills can find them by name. Suggest files based on filenames, confirm selections, and update references for guides, schematics, and pinmux spreadsheets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Register reference documents after setting up a target profile
• Update or remove document references in an existing profile
• Fix missing document references reported by downstream skills

Launch distributed Megatron-LM training on a SLURM cluster with guidance on job scripts, environment variables, GPU settings, and containers. Monitor jobs and investigate failures across worker processes to identify the initial cause.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Write an sbatch script for distributed training
• Configure training across multiple GPU nodes
• Check CUDA settings for the parallelism mode

Reclaim memory on Jetson devices that do not need a local desktop or display. Build a plan from current memory audit data, preview reversible service changes, apply approved changes, and measure the actual memory savings afterward.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimate memory savings from headless operation
• Prepare a Jetson for use as an inference appliance
• Disable unneeded desktop and background services

Switch the active Jetson target to an existing YAML profile before customization, building, or flashing. Review profile names and configuration summaries, choose a target, and update the pointer used by downstream skills without creating new profiles.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Switch the target before building
• Choose an existing profile before flashing
• Select a target when none is active

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Prepare NVIDIA Jetson setup files by checking supported releases and downloading the BSP, sample root filesystem, sources, toolchain, and guides. Files are placed where the following setup skills expect to find them.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing files for Jetson Auto Setup
• Gathering artifacts for a supported Jetson Linux release
• Staging downloads before image and source initialization

Run a warehouse robot demo controlled by Mission Control and displayed in a local Isaac Sim window. Check startup readiness, replay a fixed route, and verify mission completion, robot movement, and system health.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Demonstrating Mission Control with a simulated warehouse robot
• Replaying a fixed robot route
• Investigating startup or mission failures

Convert recorded robot workflows from HDF5 into local LeRobot datasets for training or browser inspection. Preserves actions, state, camera recordings, and task labels, then checks episode counts, metadata, videos, and data dimensions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert successful robot episodes into a training dataset
• Prepare recorded workflows for browser inspection
• Check converted videos, metadata, and data dimensions

Assess whether a DOCA application needs low-level communication controls beyond its higher-level APIs. For those cases, guide queue configuration, libibverbs migration, and error diagnosis using installed headers, version checks, and device capability queries.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Decide whether higher-level DOCA APIs are sufficient
• Configure a queue attribute unavailable through the task API
• Port libibverbs code into DOCA Core

Build Earth2Studio wrappers for transformations at a single time point, from calculating wind speed to using pretrained or diffusion models. The workflow covers input and output coordinates, model loading, tests, and supporting documentation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate wind speed from u10m and v10m
• Add a pretrained precipitation estimator
• Wrap a diffusion model for higher-resolution output

Check whether a Dynamo deployment is ready for disaggregated serving, where input processing and token generation run separately. Read-only probes inspect transport settings, GPU networking capabilities, and NIXL transfer-test readiness, then report gaps and next steps.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check communication readiness after deployment
• Inspect transport setup before benchmarking
• Investigate suspected communication bottlenecks

Configure how Dynamo routes requests to workers, then check model discovery and a chat response. Supports local and Kubernetes setups, including routing based on cached model data, with clear commands, results, and limitations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Start a local Dynamo frontend
• Change routing settings on Kubernetes
• Check model discovery and a chat response