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vss-setup-behavior-analytics

Use to deploy the vss-behavior-analytics service standalone (entrypoint, config-source, optional calibration). Not for the full warehouse deploy.

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Ringkasan

Use to deploy the vss-behavior-analytics service standalone (entrypoint, config-source, optional calibration). Not for the full warehouse deploy.

Baca dokumentasi lengkap

Dokumentasi sumber, bukan instruksi untuk situs ini. Periksa izin sebelum menjalankan perintah.

Purpose

Deploy the behavior-analytics service standalone with the user's chosen entrypoint, config, and calibration.

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). Run a Tier-3 evaluation to replay them:

nv-base validate skills/vss-setup-behavior-analytics --agent-eval

A minimal standalone bring-up looks like:

cd $REPO/deploy/docker
export VSS_APPS_DIR=$(pwd)
docker compose -f services/analytics/behavior-analytics/compose.yml up -d vss-behavior-analytics-base

Follow references/deploy-behavior-analytics-service.md for the full workflow (entrypoint pick, config source, dynamic updates).

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 license restrictions.
  • Concurrency, GPU memory, and storage limits depend on the host hardware and the profile's compose file.

Troubleshooting

  • Error: REST call returns connection refused. Cause: target microservice not running. Solution: probe /docs or /health; redeploy via vss-deploy-profile or the matching vss-deploy-* skill.
  • Error: HTTP 401/403 from NGC pulls. Cause: missing/expired NGC_CLI_API_KEY. Solution: docker login nvcr.io and re-export the key before retrying.
  • Error: container OOM or model fails to load. Cause: insufficient GPU memory for the selected profile. Solution: switch to a smaller variant or free GPUs via docker compose down.

VSS Setup Behavior Analytics — Standalone

Deploy just the vss-behavior-analytics container (the spatial-AI analytics pipeline from the upstream behavior-analytics repo), not as part of the full warehouse blueprint stack.

The full operational walkthrough — entrypoint table, config-source options, calibration types, dynamic-update wire contract, troubleshooting — is references/deploy-behavior-analytics-service.md. This SKILL.md only handles routing and prerequisites.

When to use

  • "Deploy behavior analytics" / "run behavior-analytics standalone"
  • "I just want to run analytics, not the full stack"
  • "Change the entrypoint to fusion_search / dev_example / analytics 3D / mv3dt"
  • "Use my own behavior-analytics config / calibration JSON"
  • "Point behavior-analytics at the warehouse-3d (or mv3dt) config without spinning up the rest of the warehouse profile"
  • "Dynamic config / dynamic calibration into a running behavior-analytics"

Prerequisites

  1. Repo checkout with $VSS_APPS_DIR pointing at <repo>/deploy/docker/. Required by the service compose's volume binds.
  2. NGC credentials — $NGC_CLI_API_KEY set so docker can pull the image. See references/ngc-api-key-registry-login.md.
  3. Docker runtime — Docker Engine 28.3.3 with Docker Compose plugin v2.39.1+. Verify with docker --version and docker compose version.
  4. Optional broker (Kafka / Redis Streams / MQTT). The container starts fine without one — the Kafka client retries a bounded number of times, then the app exits and restart: always cycles the container. Status will show Restarting (N) in docker ps until a broker is reachable. With a broker, dynamic config / dynamic calibration over mdx-notification become available.
  5. Optional config / calibration files on disk if the user is bringing their own.

If any required prerequisite fails, surface the gap before going further.

Workflow

Hand the user references/deploy-behavior-analytics-service.md and walk them through its steps in order:

  1. Pick an entrypoint (analytics 2D / 3D / mv3dt, dev_example, fusion_search).
  2. Choose a config — profile-shipped or custom.
  3. Choose a calibration — optional; profile-shipped or custom; otherwise the app waits for a dynamic-calibration notification.
  4. Decide whether a broker is reachable; if yes, point them at the dynamic-update flows.

The compose-file edits, YAML diffs, deploy + verify commands, and troubleshooting table all live in that reference — don't duplicate them here.

Dynamic updates (runtime, no restart)

Once the container is up and a broker is reachable, two runtime-update flows are available — neither requires redeploying:

Dynamic config

Publish an upsert (per-key patch) or upsert-all (full snapshot) message to the mdx-notification topic with Kafka key behavior-analytics-config and headers:

  • event.type: upsert | upsert-all | request-config | ack
  • reference-id: video-analytics-api-<uuid> (web-api originated), behavior-analytics-<uuid> (bootstrap reply), or the source-type literal (kafka / redis / mqtt) for direct-publisher upserts.

Body: {"status": ..., "config": <patch>, "error": ...}.

The listener validates each message at the envelope layer (rejects unknown keys, missing config, malformed status/error) and at the per-payload layer (rejects forbidden sections, bad item shapes). Successful upserts are persisted to disk, applied to every worker, and ACK'd back over the topic.

Full wire contract + ack semantics: references/dynamic-config.md.

Dynamic calibration

Publish to the same topic with Kafka key calibration and headers:

  • event.type: upsert-all (full snapshot) | upsert (per-sensor merge) | delete (per-sensor removal)
  • timestamp: ISO-8601 UTC (YYYY-MM-DDTHH:MM:SS.fffZ).

Body: JSON sensor list (and ROIs / tripwires / homographies for upsert-all).

The listener validates against the vendored AJV schema before persisting. Schema violations log a calibration schema violation warning and are dropped — the previously-good calibration stays loaded.

Full wire contract + per-action validation policy: references/dynamic-calibration.md.

Both flows live entirely on the broker — the producer can be video-analytics-api, your own script, or any Kafka client that mirrors the wire shape. They're the recommended way to change configuration after the container is running, so the operator doesn't have to redeploy.

Routing rules

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Lihat sumber ↗
Metadata berkas
name: vss-setup-behavior-analytics
description: Use to deploy the vss-behavior-analytics service standalone (entrypoint, config-source, optional calibration). Not for the full warehouse deploy.
license: Apache-2.0

metadata:
  author: "NVIDIA Video Search and Summarization team"
  version: "3.2.0"
  github-url: "https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization"
  tags: "nvidia blueprint operational deployment behavior-analytics"
Lihat teks asli
---
name: vss-setup-behavior-analytics
description: Use to deploy the vss-behavior-analytics service standalone (entrypoint, config-source, optional calibration). Not for the full warehouse deploy.
license: Apache-2.0

metadata:
  author: "NVIDIA Video Search and Summarization team"
  version: "3.2.0"
  github-url: "https://github.com/NVIDIA-AI-Blueprints/video-search-and-summarization"
  tags: "nvidia blueprint operational deployment behavior-analytics"
---
## Purpose

Deploy the behavior-analytics service standalone with the user's chosen entrypoint, config, and calibration.

## 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). Run a Tier-3 evaluation to replay them:

```bash
nv-base validate skills/vss-setup-behavior-analytics --agent-eval
```

A minimal standalone bring-up looks like:

```bash
cd $REPO/deploy/docker
export VSS_APPS_DIR=$(pwd)
docker compose -f services/analytics/behavior-analytics/compose.yml up -d vss-behavior-analytics-base
```

Follow `references/deploy-behavior-analytics-service.md` for the full
workflow (entrypoint pick, config source, dynamic updates).

## 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 license restrictions.
- Concurrency, GPU memory, and storage limits depend on the host hardware and the profile's compose file.

## Troubleshooting

- **Error**: REST call returns connection refused. **Cause**: target microservice not running. **Solution**: probe `/docs` or `/health`; redeploy via `vss-deploy-profile` or the matching `vss-deploy-*` skill.
- **Error**: HTTP 401/403 from NGC pulls. **Cause**: missing/expired `NGC_CLI_API_KEY`. **Solution**: `docker login nvcr.io` and re-export the key before retrying.
- **Error**: container OOM or model fails to load. **Cause**: insufficient GPU memory for the selected profile. **Solution**: switch to a smaller variant or free GPUs via `docker compose down`.

# VSS Setup Behavior Analytics — Standalone

Deploy **just** the `vss-behavior-analytics` container (the spatial-AI analytics pipeline from the upstream `behavior-analytics` repo), not as part of the full warehouse blueprint stack.

The full operational walkthrough — entrypoint table, config-source options, calibration types, dynamic-update wire contract, troubleshooting — is [`references/deploy-behavior-analytics-service.md`](references/deploy-behavior-analytics-service.md). This SKILL.md only handles routing and prerequisites.

## When to use

- "Deploy behavior analytics" / "run behavior-analytics standalone"
- "I just want to run analytics, not the full stack"
- "Change the entrypoint to fusion_search / dev_example / analytics 3D / mv3dt"
- "Use my own behavior-analytics config / calibration JSON"
- "Point behavior-analytics at the warehouse-3d (or mv3dt) config without spinning up the rest of the warehouse profile"
- "Dynamic config / dynamic calibration into a running behavior-analytics"

## Prerequisites

1. **Repo checkout** with `$VSS_APPS_DIR` pointing at `<repo>/deploy/docker/`. Required by the service compose's volume binds.
2. **NGC credentials** — `$NGC_CLI_API_KEY` set so docker can pull the image. See [`references/ngc-api-key-registry-login.md`](references/ngc-api-key-registry-login.md).
3. **Docker runtime** — Docker Engine **28.3.3** with Docker Compose plugin **v2.39.1+**. Verify with `docker --version` and `docker compose version`.
4. **Optional broker** (Kafka / Redis Streams / MQTT). The container starts fine **without** one — the Kafka client retries a bounded number of times, then the app exits and `restart: always` cycles the container. Status will show `Restarting (N)` in `docker ps` until a broker is reachable. With a broker, dynamic config / dynamic calibration over `mdx-notification` become available.
5. **Optional config / calibration files on disk** if the user is bringing their own.

If any required prerequisite fails, surface the gap before going further.

## Workflow

Hand the user [`references/deploy-behavior-analytics-service.md`](references/deploy-behavior-analytics-service.md) and walk them through its steps in order:

1. Pick an entrypoint (analytics 2D / 3D / mv3dt, dev_example, fusion_search).
2. Choose a config — profile-shipped or custom.
3. Choose a calibration — optional; profile-shipped or custom; otherwise the app waits for a dynamic-calibration notification.
4. Decide whether a broker is reachable; if yes, point them at the dynamic-update flows.

The compose-file edits, YAML diffs, deploy + verify commands, and troubleshooting table all live in that reference — don't duplicate them here.

## Dynamic updates (runtime, no restart)

Once the container is up **and a broker is reachable**, two runtime-update flows are available — neither requires redeploying:

### Dynamic config

Publish an `upsert` (per-key patch) or `upsert-all` (full snapshot) message to the `mdx-notification` topic with Kafka key `behavior-analytics-config` and headers:

- `event.type`: `upsert` | `upsert-all` | `request-config` | `ack`
- `reference-id`: `video-analytics-api-<uuid>` (web-api originated), `behavior-analytics-<uuid>` (bootstrap reply), or the source-type literal (`kafka` / `redis` / `mqtt`) for direct-publisher upserts.

Body: `{"status": ..., "config": <patch>, "error": ...}`.

The listener validates each message at the envelope layer (rejects unknown keys, missing config, malformed status/error) and at the per-payload layer (rejects forbidden sections, bad item shapes). Successful upserts are persisted to disk, applied to every worker, and ACK'd back over the topic.

Full wire contract + ack semantics: [`references/dynamic-config.md`](references/dynamic-config.md).

### Dynamic calibration

Publish to the same topic with Kafka key `calibration` and headers:

- `event.type`: `upsert-all` (full snapshot) | `upsert` (per-sensor merge) | `delete` (per-sensor removal)
- `timestamp`: ISO-8601 UTC (`YYYY-MM-DDTHH:MM:SS.fffZ`).

Body: JSON sensor list (and ROIs / tripwires / homographies for `upsert-all`).

The listener validates against the vendored AJV schema before persisting. Schema violations log a `calibration schema violation` warning and are dropped — the previously-good calibration stays loaded.

Full wire contract + per-action validation policy: [`references/dynamic-calibration.md`](references/dynamic-calibration.md).

Both flows live entirely on the broker — the producer can be `video-analytics-api`, your own script, or any Kafka client that mirrors the wire shape. They're the recommended way to change configuration after the container is running, so the operator doesn't have to redeploy.

## Routing rules

- If the user wants "the full stack" (UI / agent / perception): hand off to [`vss-deploy-profile`](../vss-deploy-profile/SKILL.md) with profile `warehouse` (or `alerts`). Don't run this skill in parallel.
- If the user wants to publish a runtime config / calibration update to an already-running container: walk the [Dynamic updates](#dynamic-updates-runtime-no-restart) section. Both flows need a reachable broker.
- If the user describes a behavior-analytics behavior change they want to validate (new incident type, new ROI rule, new sensor): point them at [`references/configuration.md`](references/configuration.md), [`references/dynamic-config.md`](references/dynamic-config.md), or [`references/dynamic-calibration.md`](references/dynamic-calibration.md) before editing the JSON.

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Mulai dengan tugas kecil

  1. 1Baca sumber dan pastikan masukan, keluaran, dependensi, serta izin.
  2. 2Minta rencana dari agent. Setujui pengaturan dan biaya sebelum uji terisolasi.
  3. 3Periksa hasil dan berkas yang berubah. Laporkan hanya yang dijalankan dan simpan revisi sumber.

Periksa dependensi, kunci API, dan biaya layanan pihak ketiga pada sumber. Repositori publik tidak berarti semua layanan gratis.

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Lisensi
Apache-2.0
Versi
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Push GitHub terakhir
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Jalur instruksi
skills/vss-setup-behavior-analytics/SKILL.md @ b5cf39f32f28

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  • Quality score needs review
  • Permission surface needs review: secrets or environment access, shell or command execution
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Detail lainnya
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    "best_for": [
      "research",
      "agent-skill"
    ],
    "known_risks": [
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "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"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 79,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "Dependency or permission surface needs review",
      "Permission surface may require sandboxing",
      "Financial research output is not financial advice; require human review before any live investment decision",
      "Financial research output is not financial advice; require human review before any live investment decision.",
      "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"
    ]
  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 76,
    "label": "Strong"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "Financial research output is not financial advice; require human review before any live investment decision."
  ],
  "agent_contract": {
    "task_input": "Use vss-setup-behavior-analytics 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: 75/100 Strong shortlist",
      "Audit: 79/100 Needs review",
      "Safety: 35/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "nvidia-ai-blueprints-vss-setup-behavior-analytics (vss-setup-behavior-analytics)",
      "install_command": "npx skills add NVIDIA-AI-Blueprints/video-search-and-summarization --skill vss-setup-behavior-analytics",
      "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-setup-behavior-analytics",
      "task": "Use vss-setup-behavior-analytics 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-setup-behavior-analytics",
    "api": "https://www.openagentskill.com/api/agent/skills/nvidia-ai-blueprints-vss-setup-behavior-analytics",
    "audit": "https://www.openagentskill.com/skills/nvidia-ai-blueprints-vss-setup-behavior-analytics/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=nvidia-ai-blueprints-vss-setup-behavior-analytics&task=Use%20vss-setup-behavior-analytics%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20vss-setup-behavior-analytics%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20vss-setup-behavior-analytics%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/nvidia-ai-blueprints-vss-setup-behavior-analytics/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/nvidia-ai-blueprints-vss-setup-behavior-analytics"
  }
}

Untuk kreator

Sumber listing

Diindeks Registry

Dapat diklaim

Listing ini diindeks dari sumber publik dan belum ditandai resmi hingga klaim pemelihara disetujui.

Diindeks oleh
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Atribusi menautkan ke repositori publik atau profil kreator. Kreator dapat mengklaim listing untuk memperbarui sinyal kepemilikan.

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Klaim pemilik

Klaim listing skill ini

Listing Diindeks Registry ini dikaitkan dengan NVIDIA-AI-Blueprints, tetapi belum ditandai resmi. Klaim untuk menambahkan sinyal pemilik terverifikasi dan membuat pembaruan peluncuran, pemasangan, serta audit berikutnya lebih tepercaya.

Kit berbagi

Kit backlink kreator

Tambahkan badge bukti ke README Anda

Tampilkan listing kanonis, sinyal kepercayaan dan audit saat ini, serta bukti Agent-Proven nyata di tempat pengembang mengevaluasi repositori.

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[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/nvidia-ai-blueprints-vss-setup-behavior-analytics?metric=audit&label=Audit)](https://www.openagentskill.com/skills/nvidia-ai-blueprints-vss-setup-behavior-analytics/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/nvidia-ai-blueprints-vss-setup-behavior-analytics?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/nvidia-ai-blueprints-vss-setup-behavior-analytics?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
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