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open-edge-platform

@open-edge-platform source repo

114 published skills · page 2 of 2

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    Chatqna Troubleshoot · open-edge-platform bundle
    Troubleshoot Chat Question-and-Answer Core end-to-end across Docker Compose and Helm deployments, including startup failures, health/API errors, runtime mismatches (OpenVINO vs Ollama), model/config issues, UI access problems, and log-driven root-cause isolation with concrete fix steps. Use this skill whenever the user mentions "troubleshoot", "debug", "not working", "health check failed", "chat endpoint error", "container crash", "helm pod failing", "docs page unavailable", or similar symptoms, even if they do not explicitly ask for a troubleshooting workflow.
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    Vss Add Nest Module · open-edge-platform bundle
    Scaffolds and wires a new NestJS service/module for the Video Search & Summarization sample app's pipeline-manager using the repo's real conventions. Use when asked to add a new service/module to pipeline-manager, extend the VSS orchestrator, add a new endpoint/queue/event to pipeline-manager, or scaffold a NestJS module the repo way for the video-search-and-summarization sample app.
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    Vss MCP Integration · open-edge-platform bundle
    Helps developers configure and extend the Video Search and Summarization sample app's spec-driven FastMCP proxy for VSS search. Use when the user wants to connect an AI agent to VSS search, add an MCP tool to VSS, configure the VSS MCP server / filters, run the FastMCP proxy, or debug why isn't my MCP tool showing up.
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    Vss Pipeline Config · open-edge-platform bundle
    Use this skill for the video-search-and-summarization sample app whenever a developer wants to tune the summarization pipeline, improve summary quality, or diagnose why a video is too slow to process. Trigger especially for requests to change frames per chunk, adjust chunk duration, turn audio transcript on/off, tune frame sampling or multi-frame settings, or explain latency/compute/quality trade-offs in VSS.
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    Vss Summarize Video · open-edge-platform bundle
    Summarize a video through the VSS Pipeline Manager - start a summary pipeline with POST /summary (full required body), poll GET /summary/{stateId} until complete, then return the summary via GET /summary/{stateId}/raw. Use when the user says "summarize this video", "create a summary", "what happens in this video" (on an ingested video), or wants to run/inspect the summarization pipeline. Requires a summary-capable deployment (--summary, --dual, or --unified).
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    Chatqna Docker Deploy · open-edge-platform bundle
    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 chatqna container", "run compose", "openvino gpu deploy", or "ollama deploy". Canonical deploy sources are docker/compose.yaml (services and image names) and scripts/setup_env.sh (runtime profile export); Makefile is not the source of truth.
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    Chatqna API Smoke Test · open-edge-platform bundle
    Validate ChatQnA Core REST APIs from docs/user-guide/api-reference.md using repeatable curl-based smoke tests, runtime-specific endpoint checks (OpenVINO or Ollama), and concise pass/fail evidence. Use this skill when the user says "test APIs", "verify endpoint health", "check /chat", "validate docs endpoint", or "smoke test deployment".
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    Chatqna Run Unit Tests · open-edge-platform bundle
    Run ChatQnA Core unit tests for backend (pytest via uv) and frontend UI (vitest), including runtime selection (openvino or ollama), coverage options, and concise pass/fail evidence. Use this skill when the user says "run unit tests", "run backend tests", "run UI tests", "pytest", or "vitest".
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    Vss Dlstreamer Pipeline · open-edge-platform bundle
    Helps developers understand and safely modify the DLStreamer/GStreamer Pipeline Server (EVAM) video ingestion pipelines in the video-search-and-summarization sample app. Use when the user wants to change the DLStreamer/GStreamer pipeline, extract frames differently, modify the EVAM pipeline, add a detection model to video ingestion, or tune chunk/frame extraction in the pipeline server.
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    Multimodal Embedding Serving Dev · open-edge-platform bundle
    Develop the Multimodal Embedding Serving microservice itself — Poetry install, run the existing tests, navigate the wrapper/registry/handler architecture, add a new model family, and build the image from source. Use when modifying, testing, or debugging this service's code. Not for merely deploying or calling the API — that is multimodal-embedding-serving-user.
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    Multimodal Embedding Serving User · open-edge-platform bundle
    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, embed text/images/videos over REST on port 9777, choose among 19 models (CLIP/SigLIP/MobileCLIP/CN-CLIP/Blip2/ QwenText), or integrate in-process via the Python SDK wheel. Use when an app needs embeddings for similarity search or retrieval. Not for modifying the service's source — that is multimodal-embedding-serving-dev.
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    Multimodal Dataprep Dev · open-edge-platform bundle
    Develop and debug the Multimodal DataPrep microservice: its FastAPI media endpoints, in-process embedding pipeline, batch jobs, object detection, telemetry and Metrics Manager publishing, and pluggable VDMS/Milvus vector stores plus MinIO/local storage. Use when changing source, adding a backend, running pytest/coverage/format checks, or building the service image. Use multimodal-dataprep-user for deployment and API-consumer workflows.
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    Multimodal Dataprep User · open-edge-platform bundle
    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 and images; submitting batch jobs; adding text-summary embeddings; and inspecting telemetry. This service prepares retrieval data but does not execute semantic search. Use multimodal-dataprep-dev for source changes, tests, or image builds.
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    Dlstreamer Coding Agent · open-edge-platform bundle
    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 detection/classification/VLM/tracking/alerts/recording to a video pipeline, or create custom GStreamer elements in Python or C++. Translates natural-language pipeline descriptions into working DL Streamer code using established design patterns.
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