name: vss-ask-video description: Use this skill to ask the VSS agent's video_understanding tool a fresh visual question about a recorded clip. Not for prior tool output, search hits, or metadata-answerable questions. license: Apache-2.0 metadata: version: "3.2.0" github-url: "https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization" tags: "nvidia blueprint operational"

Video QnA using VLM through VSS Agent

Use this skill when you need details about the video which requires VLM to look at the video frames — for example the agent has no usable prior answer and needs a fresh look at the pixels for a specific clip.

When to Use

• The user asks what happens in the video, what objects / people / actions appear, colors, timing, safety, or other visual facts that require watching the clip.
• The user asks for details that cannot be answered from existing messages, summaries, Elasticsearch/MCP results, or filenames alone—you need model inference on the video.
• Follow-up questions about content details after a coarse summary or after report generation.

Do not use this skill when a database / MCP / prior tool output already answers the question, unless the user explicitly wants verification against the video.

Deployment prerequisite

This skill requires a VSS profile that serves the video_understanding tool — typically base (recommended) or lvs. Before any request:

1. Probe the VSS agent:

   curl -sf --max-time 5 "http://${HOST_IP}:8000/docs" >/dev/null
   

2. If the probe fails, ask the user:

   "No VSS profile is running on $HOST_IP. Shall I deploy base (recommended for per-clip VLM QnA) using the /vss-deploy-profile skill? If you prefer lvs, say so."

   • If yes → hand off to /vss-deploy-profile -p base (or -p lvs if the user prefers). Return here once it succeeds.
   • If no → stop.

3. If the probe passes, proceed.

Sensor prerequisite

You MUST list VST sensors before any /generate call. This is required even when the user names the sensor explicitly, even when the user asserts the video is already uploaded, and even when a previous turn appeared to use the same video. Do not skip this step.

1. List sensors:

   curl -sf --max-time 5 "http://${HOST_IP}:30888/vst/api/v1/sensor/list" | jq '.[].name'
   

2. Compare the returned name values against the user-supplied <sensor-id> (or filename stem, e.g. warehouse_safety_0001).

3. If a matching sensor is present → proceed to the Agent workflow below.

4. If no matching sensor is present — upload the video first, then re-list to confirm the new sensor appears:

   # filename: must not contain whitespace
   # timestamp: ISO 8601 UTC — default 2025-01-01T00:00:00.000Z if user did not specify
   curl -s -X PUT "http://${HOST_IP}:30888/vst/api/v1/storage/file/<filename>?timestamp=<timestamp>" \
     -H "Content-Type: application/octet-stream" \
     -H "Content-Length: <file_size_in_bytes>" \
     --upload-file /path/to/<filename> | jq .
   

   See /vss-manage-video-io-storage for full upload semantics (v1 vs v2, conflict handling, delete flow). In interactive runs, confirm with the user before uploading. Never issue an unconditional PUT without first running the sensor-list check above — that is exactly the failure mode this prerequisite exists to prevent.

Agent workflow

The Sensor prerequisite above must have already confirmed (or made) the sensor exist on VST. Then:

1. Clip — Identify sensor id, filename, or URL for one video segment. If ambiguous, ask the user.
2. Call vss agent with the sensor id and ask for it to call video_understanding tool to answer the user's question.
3. Return the vss agent's answer back to the user.

Query VSS agent (/generate)

# Set from deployment (compose / .env / host where vss-agent listens)
export VSS_AGENT_BASE_URL="http://localhost:8000"

curl -s -X POST "${VSS_AGENT_BASE_URL}/generate" \
  -H "Content-Type: application/json" \
  -d '{"input_message": "Call video_understanding tool to answer the following question about <sensor-id>: <user query>"}' | jq .

Response contract and extraction

/generate returns a JSON object with the assistant output in value, for example:

{"value":"<agent-think><agent-think-step ...>...</agent-think-step></agent-think>\n\n<final answer>\n\n"}

There is no separate clean-answer field. The consumable answer is the text in .value after removing any <agent-think>...</agent-think> block.

Required handling for this skill (and any downstream caller):

1. Read .value from the JSON response.
2. Strip <agent-think>...</agent-think> sections wherever they appear.
3. Return only the remaining final-answer text to the user.

Example extraction:

curl -s -X POST "${VSS_AGENT_BASE_URL}/generate" \
  -H "Content-Type: application/json" \
  -d '{"input_message":"Call video_understanding tool to answer the following question about <sensor-id>: <user query>"}' \
| jq -r '.value' \
| python3 -c 'import re,sys; t=sys.stdin.read(); t=re.sub(r"<agent-think>.*?</agent-think>\s*", "", t, flags=re.S); print(t.strip())'

Cross-Reference

• vss-manage-video-io-storage — VST storage/replay URLs so VIDEO_URL is valid for the VLM.
• vss-generate-video-report — timestamped reports via Mode A (direct VLM) or Mode B (video-analytics incidents); this skill is VSS-agent /generate for ad-hoc video Q&A.