open-edge-platform/skills

metro-ai-app-recipe

Stand up a complete, ready-to-run computer-vision analytics stack on Intel hardware with one Docker Compose command — point it at your video sources and an OpenVINO/ONNX model to get live annotated WebRTC video plus real-time detection dashboards and alerts…

physicalai-train-adding-a-policy

Adds or modifies a Physical AI Studio policy under library/src/physicalai/policies. Use when creating a new policy family with the config/model/policy split, registering it in the get_policy factory and package exports, or keeping a policy compatible with…

uav-mission-compute-sdk

Operate and troubleshoot the UAV Mission Compute SDK for PX4 telemetry, camera streaming, missions, computer vision, and edge AI demonstrations. USE FOR: SDK setup, infrastructure validation, camera profile switching (sim/USB/RealSense), mission execution,…

metro-ai-app-builder

Conversational orchestrator that turns an objective into a working Intel Edge AI application through one questionnaire — the user states an outcome and may optionally specify packaging, API recipe, hardware, and models (or defer each with `auto`) — then…

dlstreamer-coding-agent

Build new DL Streamer video-analytics applications (Python, C, C++ or GStreamer command line). Use when: user describes a vision AI pipeline, wants to create a new sample app, combine elements from existing samples, add…

scenescape-setup

Deploy a working Intel® SceneScape installation from scratch (outside the repo). Gathers user-provided streams, camera IDs, scene name, and mapping choice, then runs bootstrap through tracking verification via scripts/deploy_scenescape.sh. Also handles…

physicalai-train-training-a-policy

Trains, validates, tests, and runs prediction for Physical AI Studio policies via the library Lightning stack. Use when running physicalai fit/validate/test/predict, calling physicalai.train.Trainer and Policy APIs from Python, writing or editing YAML configs…

physicalai-train-working-with-datasets

Works with Physical AI Studio datasets and Lightning datamodules built on the LeRobot format. Use when wiring physicalai.data.lerobot.LeRobotDataModule into a training config, choosing a repo_id, converting between the physicalai and lerobot data layouts,…

physicalai-train-benchmarking-a-policy

Benchmarks a trained Physical AI Studio policy in a simulation gym and reports success metrics. Use when running physicalai benchmark, editing configs under library/configs/benchmark, adding or changing a Benchmark class in physicalai.benchmark, tuning…

physicalai-train-exporting-and-validating

Exports and validates Physical AI Studio policies for Runtime deployment. Use when working on policy.export(...), the physicalai export CLI, the ONNX/OpenVINO/Torch/ExecuTorch backends, export metadata, numerical parity checks, or the Studio side of the…

skill-name

Replace with a description of what this skill does and when it should be used. Include trigger keywords so the agent reliably activates this skill.

physicalai-runtime-adding-a-robot-integration

Adds or modifies robot hardware integrations under physicalai.robot. Use when implementing the Robot protocol, SO101 or Trossen WidowX drivers, robot connect helpers, verify.py checks, optional extras so101 or trossen, or tests in tests/unit/robot.

physicalai-runtime-running-policy-on-robot

Runs exported policies on hardware with PolicyRuntime, execution modes, and physicalai run. Use when wiring PolicyRuntime, SyncExecution or RTC execution, runtime YAML configs, action queues, runtime callbacks, or docs/how-to/runtime run-policy-on-robot and…