vss-ask-video: Research & Knowledge for AI Agents | OpenAgentSkill

Overview

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.

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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:

Probe the VSS agent:

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

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.
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.

List sensors:

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

Compare the returned name values against the user-supplied <sensor-id> (or filename stem, e.g. warehouse_safety_0001).
If a matching sensor is present → proceed to the Agent workflow below.
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:

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

Read .value from the JSON response.
Strip <agent-think>...</agent-think> sections wherever they appear.
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.

View source ↗

File metadata

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"

View original text

---
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:
   ```bash
   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:
   ```bash
   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:
   ```bash
   # 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`)

```bash
# 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:

```json
{"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:

```bash
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**.

Review the source

Skill source recorded

Skill instructions are recorded. This is not a runtime test, safety guarantee or compatibility certification.

Review before install: Avoid automatic install

Dependency or permission surface needs review
Permission surface may require sandboxing
No explicit authentication or authorization model for the VSS/VST endpoints is documented; the skill assumes trusted local access.
The SKILL.md excerpt cuts off during the /generate extraction example, which may leave implementers without the complete response-handling instructions.

Open full audit

Listed tools are metadata hints, not tested compatibility. Agent prompts are suggested handoffs.

Start with one small task

1Read the source. Confirm the input, expected output, dependencies and permissions.
2Ask your agent for a plan. Approve setup and any costs before running a small isolated test.
3Check the output and changed files. Report only what actually ran; keep the source revision for reproduction.

Check the source for dependencies, API keys and third-party costs. A public repository does not mean every service is free.

Source & trust

Indexed

Repository metadata and review signals are advisory. Popularity, source discovery and successful execution are different facts.

License: Apache-2.0
Version: 1.0.0
Last GitHub push: Sep 3, 2026
Registry updated: Sep 3, 2026
Instruction path: skills/vss-ask-video/SKILL.md @ b5cf39f32f28

Version reported in registry metadata; check source releases before relying on it.

Quality

79/100

Strong

Trust

61/100

Sandbox only

Audit

79/100

Needs review

Agent access

This page exposes the same decision, trust, audit, use-case, and install signals through the Registry API, so agents can rank this skill without scraping the UI.

Open install API LLM text format Open manifest Auto-resolve Plan

More details

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      "agentOutcomes": "No agent outcome data yet"
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    "Dependency or permission surface needs review",
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    "Quality score needs review"
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    "minimum_review_before_use": [
      "Trust: 69/100 Manual review",
      "Audit: 79/100 Needs review",
      "Safety: 35/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
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    "expected_agent_output": {
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    "api": "https://www.openagentskill.com/api/agent/skills/nvidia-ai-blueprints-vss-ask-video",
    "audit": "https://www.openagentskill.com/skills/nvidia-ai-blueprints-vss-ask-video/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=nvidia-ai-blueprints-vss-ask-video&task=Use%20vss-ask-video%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20vss-ask-video%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20vss-ask-video%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/nvidia-ai-blueprints-vss-ask-video/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/nvidia-ai-blueprints-vss-ask-video"
  }
}

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Creator: NVIDIA-AI-Blueprints
Source: NVIDIA-AI-Blueprints/video-search-and-summarization
Indexed by: OpenAgentSkill community index

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