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vss-ask-video

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.

Examiner la source Voir sur GitHub

Prix non confirmé★ 1,843 Stars GitHubRegistre mis à jour · 3 sept. 2026agent-skill

Vue d’ensemble

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.

Lire la documentation complète

Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.

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.

Voir la source ↗

Métadonnées du fichier

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"

Voir le texte original

---
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**.

Examiner la source

Prix et coûts d’utilisation

Obtenir le skill: Prix non confirmé
L’utiliser: Prérequis non confirmés. Consultez les frais d’agent, d’API et de services à la source.
Licence: Apache-2.0
Prix non confirmé: Le prix n’est pas confirmé. Les liens existants vers les sources et l’installation restent disponibles.

Gratuit à obtenir ne signifie pas gratuit à utiliser. Le prix ne constitue pas une évaluation de sécurité. Soumettre un prix →

Source du skill enregistrée

Un chemin vers les instructions est enregistré. Cela ne constitue pas un test, une garantie de sécurité ou de compatibilité.

Réviser avant installation: Éviter l’installation automatique

Licence: Apache-2.0

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.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Ouvrir l’audit complet

Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: NVIDIA-AI-Blueprints/video-search-and-summarization
Licence: Apache-2.0
Version: 1.0.0
Dernier push GitHub: 3 sept. 2026
Registre mis à jour: 3 sept. 2026
Chemin des instructions: skills/vss-ask-video/SKILL.md @ b5cf39f32f28

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

76/100

Solide

Confiance

60/100

Sandbox uniquement

Audit

76/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

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.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

Accès agent

L’API Registry fournit les signaux de décision, confiance, audit, cas d’usage et installation sans analyser l’interface.

Ouvrir l’API d’installation Format texte LLM Ouvrir Manifest Plan de résolution auto

Plus de détails

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  "alternative_skills": [
    {
      "slug": "latent-spaces-brag-slim",
      "name": "brag-slim",
      "url": "https://www.openagentskill.com/skills/latent-spaces-brag-slim",
      "stars": 13807,
      "install_command": "npx skills add latent-spaces/brag --skill brag-slim",
      "trust_score": 81,
      "audit_score": 84
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "production agents without a repository review",
    "No explicit authentication or authorization model for the VSS/VST endpoints is documented; the skill assumes trusted local access.",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "The SKILL.md excerpt cuts off during the /generate extraction example, which may leave implementers without the complete response-handling instructions.",
    "Quality score needs review"
  ],
  "agent_contract": {
    "task_input": "Use vss-ask-video in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 68/100 Manual review",
      "Audit: 76/100 Needs review",
      "Safety: 32/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "nvidia-ai-blueprints-vss-ask-video (vss-ask-video)",
      "install_command": "npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-ask-video",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "nvidia-ai-blueprints-vss-ask-video",
      "task": "Use vss-ask-video in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/nvidia-ai-blueprints-vss-ask-video",
    "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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Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur: NVIDIA-AI-Blueprints
Source: NVIDIA-AI-Blueprints/video-search-and-summarization
Indexé par: Index communautaire OpenAgentSkill

L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.

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