open-edge-platform

Use this skill whenever a developer needs to deploy VSS to Kubernetes, helm install VSS, configure values.yaml for VSS, or run VSS on k8s with GPU/vLLM for the video-search-and-summarization sample app. This skill is especially useful when translating Docker…

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…

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,…

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…

Deploy and consume the Multimodal Embedding Serving microservice — bring it up with setup.sh + docker compose (from a repo clone, or by fetching those same files from GitHub when no clone exists) using the prebuilt intel/multimodal-embedding-serving image,…

Deploy and consume Intel Multimodal DataPrep from prebuilt images or a repository checkout. Use for configuring VDMS or Milvus vector storage, MinIO or local media storage, checking service dependencies, and ingesting, listing, streaming, or deleting videos…

Deploy Chat Question-and-Answer Core with Docker Compose (OpenVINO CPU, OpenVINO GPU, or Ollama CPU), including env setup, profile selection, startup verification, health checks, and teardown. Use this skill when the user says "deploy chatqna core", "start…

Deploy Chat Question-and-Answer Core to Kubernetes using Helm (OpenVINO CPU, OpenVINO GPU, or Ollama), including values.yaml configuration, helm install/upgrade, deployment verification, uninstall, and translation from Docker Compose setup_env.sh variables…

Build an end-to-end, vertical-agnostic computer-vision analytics stack on Intel hardware from a single streamlined Docker Compose deployment: DL Streamer Pipeline Server plus MediaMTX/WebRTC, Coturn, Mosquitto, Node-RED, Grafana, and Nginx. It streams live…

Conversational orchestrator that turns a plain business objective into a working Intel Edge AI application. It OWNS the conversation: it asks only business questions (what outcome you want, your inputs, where it runs, your hardware) — never which framework,…

Configure real-time Smart Community monitor alerts for a specified OpenClaw agent. Use when a user asks to bind, route, subscribe, or push one monitor's alerts to an agent session. Requires an agent ID and monitor ID, and always targets session…

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,…

生成竞品分析中文HTML和PDF报告 - 自动调研产品信息并生成专业中文对比报告

Generic, use-case-agnostic guide to the smart-community MCP server and its smart_community_* video tool set. Read this before touching any smart_community_* tool. IMPORTANT: this toolkit must not create/register new use cases directly; for any new use case…

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…

Diagnose Content Search backend availability by probing the health endpoint, then surface connectivity issues between Flutter and backend when unhealthy. Use when the user says "is the backend up", "check health", "backend unreachable", "debug backend", or…

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…

Designs and reviews REST APIs for FastAPI services using consistent resource naming, HTTP semantics, validation, security, and error handling patterns. Use for backend API tasks, endpoint design/refactors, or API review requests in FastAPI/Python projects.

Run or continue model benchmarks, collect measured results, and refresh README/docs benchmark sections from generated artifacts. Use when benchmark tables in model docs need to be created, updated, or corrected.

Reviews anomalib docstrings, documentation updates, and changelog expectations

Keep anomalib model READMEs, docs pages, image assets, and benchmark/result references in sync

Export, validate, and publish model sample-result images into docs/source/images and reference them from README/docs pages. Use when model sample images are missing, outdated, or suspected to be invalid.

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

Reviews anomalib contributor workflow, PR title, branch naming, and quality gate expectations

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…

Develop and validate changes in `application/backend/` for the FastAPI `geti` service. Use when touching `application/backend/app/**`, backend tests, backend packaging, backend configuration, API routers, schemas, services, repositories, or database code, or…

Update documentation (README, CHANGELOG, Sphinx 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.

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…

Use the Geti application end to end through its REST API — the project → dataset → annotate → train → deploy pipeline served by the FastAPI backend in `application/backend/`. Use when a user (not a contributor) wants to create a project, upload media, add…

Develop and validate changes in `library/` for the `getitune` Python package (the Geti training library). Use when touching `library/src/**`, `library/tests/**`, `library/pyproject.toml`, recipes, model manifests, or any Python API, CLI, model, training,…

Discover which models, recipes, and tasks the getitune library (the Geti training library) supports before training. Use when a user asks what models are available, how to list recipes, how to filter by task or name pattern, how `list_models(...)` and…

Export a trained getitune model (the Geti training library) to a deployable format. Use when a user wants to run `engine.export(...)` or `getitune export`, choose between OpenVINO IR and ONNX, set FP32 vs FP16 precision with `ExportFormat` / `Precision`, or…

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

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,…

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

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.

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.

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

Validate whether a provisioned platform is correctly configured over SSH, including k3s pod health, binary paths, cloud-init state, network and proxy setup, and device readiness.

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 →…

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…

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…

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.…

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.

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

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…

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…

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…

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,…

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…

Use this skill whenever a developer needs to deploy VSS to Kubernetes, helm install VSS, configure values.yaml for VSS, or run VSS on k8s with GPU/vLLM for the video-search-and-summarization sample app. This skill is especially useful when translating Docker…

Deploy and consume the VDMS DataPrep video-ingestion stack (dataprep + VDMS vector DB + MinIO) — bring it up with setup.sh + docker compose (from a repo clone, or by fetching those same files from GitHub when no clone exists) using the prebuilt…

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

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/.

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…