vss-search-archive
Use this skill to run top-level VSS fusion search on archived video, or to ingest video files / RTSP streams for search. Do NOT use for ad-hoc visual Q&A (use vss-ask-video), live captioning (use vss-deploy-dense-captioning), or video summarization and reports (use vss-summarize-video).
npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-search-archive --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
## Purpose Run the top-level VSS fusion search across archived video, ingest new clips / RTSP streams for search, and delete search-ingested sources. ## Prerequisites - Active VSS deployment reachable on `$HOST_IP` (see `vss-deploy-profile` and `references/`). - `vss-manage-video-io-storage` skill installed (used to list and manage video sources before search). - NGC credentials in `$NGC_CLI_API_KEY` and `$NVIDIA_API_KEY` for any image pulls. - `curl`, `jq`, and Docker available on the caller. ## Instructions Follow the routing tables and step-by-step workflows below. Each section that ends in *workflow*, *quick start*, or *flow* is intended to be executed top-to-bottom. Detailed reference material lives in `references/`. ## Examples Worked end-to-end examples are kept under `evals/` (each `*.json` manifest contains a runnable scenario) and inline in the per-workflow `curl` blocks below. Run a Tier-3 evaluation with `nv-base validate <this-skill-dir> --agent-eval` to replay them. ## Limitations - Requires the matching VSS profile / microservice to be deployed and reachable from the caller. - NGC-hosted models and NIMs may be subject to rate-limits, GPU memory requirements, and lice
- Purpose
- Prerequisites
- Instructions
- Examples
- Limitations
- Troubleshooting
- When to Use
- Deployment prerequisite
- Ingestion prerequisite (required before any /generate)
- File upload — universal three-step flow
- RTSP stream — single endpoint
- Delete source — agent-backed cleanup
- How Search Works
- Mandatory workflow
curl -sf --max-time 5 "http://${HOST_IP}:8000/docs" >/dev/null \
The final-chunk response carries sensorId.
printf '%s' "${UPLOAD_RESPONSE}" \
curl -s -X POST "http://${HOST_IP}:8000/api/v1/rtsp-streams/add" \
For video files: video_id is the VIOS sensor/video UUID
curl -s -X DELETE "http://${HOST_IP}:8000/api/v1/videos/<video_id>" | jq .
For RTSP streams: name is the registered source name
curl -s -X DELETE "http://${HOST_IP}:8000/api/v1/rtsp-streams/delete/<name>" | jq .
Consider only ingested video file sources by default
curl -s -X POST http://${HOST_IP}:8000/generate \What does the vss-search-archive skill do?
Use this skill to run top-level VSS fusion search on archived video, or to ingest video files / RTSP streams for search. Do NOT use for ad-hoc visual Q&A (use vss-ask-video), live captioning (use vss-deploy-dense-captioning), or video summarization and reports (use vss-summarize-video).
How do I install it?
Run `npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-search-archive --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From NVIDIA-AI-Blueprints/video-search-and-summarization, a repository with 1,773 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.