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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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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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.
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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.
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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.
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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.
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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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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.
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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.
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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.
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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.
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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.
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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.
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Integrate a machine learning weather model into Earth2Studio for forecasts that advance through time. The skill guides wrapper implementation, coordinate handling, model loading, tests, dependency declarations, and documentation updates using a reference inference script.
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