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Find the right code for Roo Code evaluations, which test AI coding tasks. This skill explains the execution system, management interface, and public results page, helping agents identify where changes and debugging work belong.

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• Locate code for evaluation infrastructure fixes
• Find guidance for adding exercises or languages
• Identify files for the evaluation management interface

Coordinate specialized AI agents to develop research hypotheses, plan methods, run computational experiments, search literature, and draft journal-formatted LaTeX papers. Start from a dataset description or supply existing ideas, methods, and results at individual stages.

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• Generate research hypotheses from dataset descriptions
• Develop methods for an existing research question
• Run computational experiments and create figures

Build and run genomics analyses on DNAnexus, a cloud platform for biomedical data. Covers analysis apps, file organization, multi-step workflows, job monitoring, and automation with the dxpy Python library.

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• Build a genomics analysis app
• Upload and organize sequencing data
• Run analyses across multiple samples

Helps prepare video and image datasets with Cosmos Curator through splitting, filtering, deduplication, captioning, and embeddings, numerical representations for matching. Covers pipeline configuration, execution checks, Cosmos Dataset Search ingestion and search, and compatible handoffs to TAO data mining.

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• Splitting and filtering videos into training datasets
• Deduplicating data and packaging it into archives
• Adding captions and embeddings to images

Create synthetic defect images for detector training using clean images and masks that mark defect regions. Guide mask placement, Cosmos fine-tuning, image generation, evaluation, quality refinement, and automatic assignment of provisional labels.

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• Generating synthetic PCB defect images
• Placing defect masks on clean images
• Fine-tuning Cosmos for a defect dataset

Create and validate Cosmos Data Factory YAML configurations for changing video scenes, editing images, or generating videos. Run generation through remote model APIs, configure captions and quality checks, and retry candidates within a defined limit.

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• Changing weather or lighting in source videos
• Editing selected attributes in still images
• Generating videos from an image or text

Evaluate object detection predictions against labeled ground truth with TAO Data Services. Produce class-level precision, recall, accuracy, and AP, a detection quality measure, with CSV results and precision–recall plots for reviewing model performance.

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• Score object detection predictions against ground truth
• Review precision and recall by class
• Generate metrics CSV files and precision–recall plots

Replace the object detector in VSS Alerts Blueprint verification mode with an ONNX model. This guide covers model placement, interpreting detection outputs, DeepStream configuration, TensorRT engine builds, redeployment, and common loading or mount failures.

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• Replace the verification detector with a YOLO11 ONNX model
• Investigate DeepStream custom parser loading failures
• Fix ONNX mounts and TensorRT engine rebuild settings

Investigate failures in NVIDIA TAO Visual ChangeNet classification experiments using prediction scores and actual images. Produces an illustrated root cause report covering data, reference images, training settings, and simulated changes to assess possible fixes.

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• Investigate missed defects and poor recall
• Analyze FAR regressions and false positives
• Audit reference images and inspection data

Switch the vision-language model, which handles images and text, in VSS Alerts Blueprint. Configure alert generation, verification, and agent queries separately, choose an external endpoint or bundled vLLM deployment, and verify an actual inference response.

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• Switch all three VSS services to a different model endpoint
• Run standalone rtvi-vlm with an external model server
• Serve a model with vLLM inside the RTVI container

Find the right NVIDIA NuRec skills for turning sensor recordings into renderable 3D scenes. This routing guide organizes dataset downloads, conversion, training, rendering, and frame cleanup into workflows handled by separate specialist skills.

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• Choose the right NuRec skill
• Plan 3D reconstruction from sensor recordings
• Render a pretrained NuRec scene

Coordinate conversion of CAD and other source assets into simulation-ready USD, a 3D scene format. Organizes material and physics assignment, SimReady conformance checks, validation, and optional preview images and deliverable packages, with consolidated reports.

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• Convert CAD assets for simulation
• Assign materials and physics properties
• Validate existing USD assets against SimReady requirements

Plan and troubleshoot Nemotron retrieval recipes for embeddings, which turn text into searchable vectors, and reranking, which reorders search results. Review configurations, prepare training and evaluation commands, and check export or deployment requirements.

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• Choosing training steps for missing relevant documents
• Improving the ordering of retrieved passages
• Reviewing training commands with a dry run

Build or repair NVIDIA voice agents with Pipecat or LiveKit for real-time spoken conversations. Set up speech recognition, language models, and speech synthesis, customize pronunciation or multilingual routing, and deploy locally or in the cloud.

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• Build a new real-time voice agent
• Repair an existing voice conversation pipeline
• Customize speech recognition and pronunciation

Prepare the runtime for Isaac for Healthcare workflows by checking host requirements and setting up dependencies. Verify that workflows can be listed and their definitions pass checks, and identify the first setup failure requiring attention.

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• Set up the workflow runtime on a Linux machine
• Check host requirements before installation
• Investigate missing environments or third-party setup failures

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

Set up a NVIDIA Holoscan Sensor Bridge demo for the selected development kit. The workflow configures the host, builds and launches the demo container, checks board connectivity and FPGA version, and reports issues encountered.

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• Setting up an HSB demo on a supported development kit
• Building and launching the demo container
• Checking board connectivity and FPGA version

Set up or repair the FoundationPose perception pipeline and build FoundationStereo TensorRT engines for depth estimation. It checks system requirements, resolves dependency and CUDA library issues, and verifies engines against the intended scene dimensions.

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• Install the pipeline on a supported GPU machine
• Resolve SAM3 and TAO dependency conflicts
• Diagnose missing CUDA runtime libraries

Find and run Holoscan Sensor Bridge example applications that match a connected development kit, board, software version, and sensors. Monitor timed runs, investigate failures, suggest code or environment changes, and summarize the session.

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• Find example apps compatible with connected sensors
• Run a sample application for 60 seconds
• Analyze failures and retry an application

Build a first DOCA application by adapting an installed sample. Get guidance on build configuration, object setup and shutdown order, and common errors, plus how other languages access DOCA through its public C interface.

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• Adapting an installed sample into a first DOCA application
• Configuring a build with pkg-config and meson
• Checking object lifecycle order and common errors

Build packet steering rules, which direct network traffic by matching its properties, with DOCA Flow on NVIDIA NICs or DPUs. Adapt shipped samples, validate rules against installed APIs and hardware capabilities, and investigate counters or runtime errors.

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• Route traffic matching address and port conditions
• Bring up Flow ports
• Validate rules before programming hardware

Extend an existing grover_base model with a new decoder that reconstructs molecular structure strings, then continue pretraining on your corpus. The workflow validates inputs, rebuilds vocabulary components, and records the conversion and training outputs.

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• Extend an existing grover_base model with cMIM
• Continue pretraining on a molecular corpus
• Trace checkpoint conversion and training records

Customize NVIDIA Nemotron Voice Agent’s Generic Pipecat example for appointment scheduling, five-field patient intake, or a custom healthcare conversation. Build prompts and tool handlers without a separate backend, then validate conversation behavior, services, and voice interaction.

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• Build an appointment-scheduling voice agent
• Collect five patient-intake fields through conversation
• Design a custom healthcare conversation with tool calls

Turn molecules listed in a SMILES CSV into numerical feature vectors using a KERMT model. The workflow checks the model and input, cleans molecular notation, and saves four sets of vectors with validity information.

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• Extract molecular feature vectors from a SMILES CSV
• Reuse a local KERMT checkpoint
• Extract features with a released pretrained model