open-edge-platform

Generates or updates CHANGELOG.md by analyzing git commit history between two branches, tags, or revisions in ANY git repository or folder. Use this skill whenever the user asks to create, update, generate changelog, draft release notes from git history, or…

Deploy and operate DL Streamer Pipeline Server — a microservice that wraps DL Streamer pipelines behind a REST API for containerized, no-code operation. Use this skill whenever a user wants to: deploy the pipeline server via Docker Compose or Helm; start,…

Extend, test, debug, or integrate the Model Download microservice codebase. Use this skill when a developer wants to: add a new plugin to the microservice; write tests for a plugin (including mocking subprocess calls, async methods, or the Ollama server);…

Download and convert AI models using the Model Download microservice. Use this skill whenever a user wants to: download a model from HuggingFace, Ollama, Ultralytics, Geti, or Pipeline Zoo; convert a model to OpenVINO IR format for OVMS; download healthcare…

Deploys and manages VSS through setup.sh and its Docker Compose overlays. Use this skill for local lifecycle tasks such as configuration, startup, mode changes, inspection, shutdown, data cleanup, and health checks. It supports summary, search, dual, and…

Install, start, stop, or remove Open Edge Platform components and profiles on an edge system with the openedge-cli installer. Use for requests like "install OpenVINO", "set up DL Streamer", "run the smart parking sample", "deploy the…

Build a new time-series analytics use case on top of the deployed Time Series Analytics microservice — bring it up with Docker Compose (from a repo clone, or by fetching the compose files from GitHub when no clone exists) using the prebuilt…

Develop the Time Series Analytics microservice itself (FastAPI + Kapacitor) — build and deploy it locally via Docker Compose or Helm, run the mocked unit test suite (tests/run_tests.sh) and the slower Docker/Helm end-to-end functional suite…

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…

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…

Build an end-to-end UAV object detection and telemetry overlay application on Intel hardware using DL Streamer Pipeline Server with MAVLink telemetry. USE FOR: creating UAV/drone vision analytics stacks that detect objects from aerial video (file, RealSense…

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,…

MANDATORY for creating, previewing, refining, registering, or deleting any Smart Community video analytics use case, and for creating, rebinding, or updating any monitor. Use-case creation requires two cross-turn gates: explicit Q1/Q2 answers, then explicit…

Upload a file to the Content Search backend and poll the ingestion task until the file is fully indexed (status COMPLETED). Handles duplicate detection (code 40901), cleanup-and-retry, and task timeout. Supported file types: pdf, txt, docx, doc, pptx, ppt,…

Generic RAG query skill - Retrieve any information from the local knowledge base and generate structured reports, summaries, or Q&A responses. Use for: product comparisons, technical analysis, documentation generation, competitive analysis, benchmark reports,…

Tune or debug this Smart Building Digital Twin application on another machine. Use when results differ across systems, when setup works locally but not on a target host, or when analytics behavior changes due to GPU/CPU mode, CPU or platform performance…

Adds a new dataset/datamodule to anomalib under src/anomalib/data/. Use when wiring a new data source into the AnomalibDataset/AnomalibDataModule base classes, exporting it so anomalib.data.<Name> and the CLI/config (jsonargparse) can discover it, and adding…

Reviews anomalib model, data, callback, metric, and CLI integration conventions

Use when writing or updating Anomalib Studio UI component or hook tests that need the shared render/renderHook helpers, React Router paths or parameters, React Query, theme, URL-query, stream, suspense, or toast providers.

Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches AI agents running in CI/CD…

Adds a new anomaly-detection model to anomalib under src/anomalib/models/. Use when implementing a new model architecture (image or video), wiring it into the AnomalibModule base class, registering it so get_model() and the CLI/config (jsonargparse) can…

Runs the anomalib benchmarking pipeline to train/evaluate a grid of model + dataset (+ category) combinations and collect metrics into a results CSV. Use when comparing multiple models/datasets/categories in one sweep, or authoring/editing a benchmark config…

Runs and configures the anomalib tiled-ensemble pipeline, which trains/evaluates one model per image tile and merges results (with optional seam smoothing) for high-resolution anomaly detection. Use when the user wants to train with image tiling, mentions…

Trains an anomalib model on a dataset via the Python API or CLI, including training on a custom folder-structured dataset with the Folder datamodule. Use when writing or debugging an anomalib training script/command, choosing Engine/Trainer arguments, or…

Create projects, manage labels, and annotate media in the Geti application via its REST API. Use when a user wants to create a project with a task type and label set, add/edit/remove labels, upload images or videos, draw or set annotations (classification…

Develop and validate changes in `application/backend/` for the FastAPI `geti` service. Use when changing backend source or tests as well as packaging and configuration. This includes API routers and schemas; services and repositories; database code; UI-facing…

Update documentation (READMEs, application docs, or inline docstrings) to reflect changes, fixes, or new features. Use when a PR modifies behavior that should be documented for users or developers.

Import and export datasets in the Geti application via its REST API. Use when a user wants to bring an existing dataset into Geti (COCO/YOLO/VOC/Datumaro), migrate data from another tool, export a project's dataset or a dataset revision, remap labels or…

Regenerate and validate the OpenAPI contract between `application/backend/` and `application/ui/`. Use when backend endpoints, schemas, request or response models, or API surface change, or when `application/ui/src/api/openapi-spec.json` or…

Configure and validate the Geti runtime inference pipeline in application mode. Use when a user needs to set up or troubleshoot source → model → sink configuration, tune pipeline parameters, diagnose bad predictions or throughput issues, verify deployment…

Run and operate live inference in the Geti application pipeline. Use when a user wants to start, monitor, stop, or recover source -> model -> sink runtime execution, verify production readiness, or troubleshoot live pipeline behavior such as stalls, dropped…

Develop and validate changes in `application/ui/` for the React and TypeScript frontend. Use when touching `application/ui/src/**`, frontend tests, RSBuild or Vitest config, Playwright setup, package scripts, or generated API typings under `src/api`. Helps…

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…

Procedures for updating Scenescape documentation — where to make changes and what to update for each type of modification.

Create Scenescape pytest cases (unit, functional, integration, UI, BAT) with SCENESCAPE_SPEC, Zephyr IDs, and positive/negative coverage. Use when adding or modifying tests under tests/, writing pytest, or choosing unit vs functional vs UI.

Makefile standards for Scenescape — build targets, conventions, and patterns.

Write or review Scenescape external-source converter/adapter scripts that map native telemetry (MAVLink, ROS 2, NMEA, CAN, UWB/RTLS, proprietary) into the Scene Controller external_source MQTT contract. Use when the task involves external sources, publishers,…

JavaScript coding standards for Scenescape — code style, conventions, and frontend patterns.

Python coding standards for Scenescape — imports, indentation, patterns, and conventions.

On-demand security review skill for Scenescape — code and configuration security guidance.

Build a host OS image using the Image Composer Tool (ICT) from a source template with customizable user credentials.

Package bootable USB installation artifacts (HookOS, host image, deployment scripts) from a standard Docker build, an ICT image, or a previously built image.

Create controlled CPU and integrated-GPU load on an Intel host to see how the platform behaves when it is busy. Choose how many CPU workers to run, how hard each one pushes (per-CPU load %), how many GPU workers to run, and how long the load lasts — then…

Update Ubuntu package configuration files for package add and delete operations.

Orchestrate a full platform profiling session on an Intel host — apply a power envelope and thermal policy, start the power/thermal monitor, drive a bounded stress load, then summarize the result as a single enclosure report. Chains set-power-profile →…

Validate whether a provisioned platform is correctly configured when run locally on that host, including k3s pod health, binary paths, cloud-init state, network and proxy setup, and device readiness.

Generate sustained AI inference load on CPU, GPU, or NPU using OpenVINO benchmark_app in a container (K3s pod or Docker). Produces real neural-network inference stress for power/thermal profiling — unlike stress-ng synthetic load. Auto-detects whether K3s or…

Run a live power and thermal monitor locally on an Intel host using tools/power-tuning/pt_mon.sh (turbostat), sampling package temperature and the RAPL power domains (PkgWatt, CorWatt, GFXWatt, RAMWatt, SysWatt) at a fixed interval and logging to pt_mon.txt.…

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…

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…

Creates or modifies an external Physical AI robot plugin for Studio. Use when implementing a Runtime Robot driver outside Studio, adding studio_catalog.py, RobotCatalogDefinition, RobotProbe, RobotAsset, the physicalai.studio.catalog_plugins entry point, or a…

Adds a new interactive robot form UI field for plugin payload schemas. Use when introducing a new `robot_payload_ui` item kind, wiring renderer support under application/ui/src/features/robots/robot-form/robot-schema/components, updating plugin SDK UI-schema…

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,…

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…

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…

Report Intel Boot Guard provisioning state, profile (0, 3, 4, or 5), and Verified/Measured Boot modes. Use when asked about Boot Guard, BtG profiles, or firmware root of trust.

Report hardware crypto acceleration available on the CPU - AES-NI, SHA-NI, RDRAND, RDSEED. Use when asked which crypto instructions the platform supports.

Report disk encryption configuration - LUKS/dm-crypt volumes, cipher and key size, swap encryption, and unencrypted volumes. Use when asked whether disks or swap are encrypted.

Report UEFI Secure Boot configuration on an Intel edge platform - enabled state, Setup Mode, and enrolled PK/KEK/db keys. Use when asked about Secure Boot status or boot chain key enrollment.

Report Intel Total Memory Encryption (TME/MKTME) support, activation state, and encryption algorithm. Use when asked about memory encryption on an Intel platform.

Install or uninstall the Intel Trusted Compute stack on bare-metal Ubuntu 24.04, via K3s or Docker. Use when asked to install, deploy, verify, or remove Trusted Compute packages.

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…

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…

Adds a new dataset/datamodule to anomalib under src/anomalib/data/. Use when wiring a new data source into the AnomalibDataset/AnomalibDataModule base classes, exporting it so anomalib.data.<Name> and the CLI/config (jsonargparse) can discover it, and adding…

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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.

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…

Manage and configure post-orch.env settings

Manage and configure pre-orch.env settings

Install pre-orchestrator infrastructure (OpenEBS, MetalLB, cluster setup)

Install post-orchestrator EMF application stack

Uninstall post-orchestrator EMF application components

Uninstall pre-orchestrator infrastructure components

Customize image-composer templates with extra packages and repos, saving results to user-templates/ so canonical templates stay pristine. Use when users ask for package/repo injection without editing image-templates/.

Use when building OS disk images using the image-composer-tool and YAML template definitions for any supported OS/imageType.

List all buildable OS targets and template details from image-templates/. Use when users ask what OSes, distros, arches, or image types are available.

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…