Tao Generate Video Reasoning Annotations
NVIDIAOfficialCreated by NVIDIA Corporation
- Skill
- Workflow
- Data and AI
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
The tao-generate-video-reasoning-annotations skill provides a structured pipeline to transform raw video files into annotated datasets suitable for training video understanding models. It utilizes Vision-Language Models (VLMs) for captioning and Large Language Models (LLMs) for synthesizing descriptions and generating various QA pairs, including MCQ, binary, and open-ended questions with reasoning traces. The pipeline is designed to be domain-agnostic, supporting custom prompt modules for specialized use cases like traffic or warehouse monitoring, and includes features for anomaly detection and resume-capable processing.
What it does
- Filters and classifies videos as normal or anomalous.
- Generates global, dense, and chunked video captions using VLM distillation.
- Synthesizes captions into structured narratives using LLMs.
- Produces diverse QA pairs with step-by-step reasoning traces.
- Parses outputs into standardized JSON files following the tao-vl-reason-v1.0 format.
- Supports resume-on-failure to skip already-processed videos.
When to use it
- When you need to create training data for video understanding models.
- When you want to generate Chain-of-Thought reasoning traces from video content.
- When you need to auto-label large collections of video files.
- When you are working with specific domains like traffic or industrial warehouse monitoring.
Advantages
- Modular pipeline allows for selecting specific processing steps.
- Built-in support for domain adaptation through custom prompt modules.
- Resume capability ensures efficiency during long-running annotation tasks.
- Supports both Gemini and OpenAI-compatible VLM/LLM backends.
- Provides structured output compatible with downstream training workflows.
Usage examples
- “Run the video annotation pipeline on my traffic footage.”
- The agent will guide you through selecting the traffic prompt module, configuring your VLM/LLM endpoints, and executing the pipeline on your video directory.
- “How do I create a custom dataset for my warehouse security videos?”
- The agent will initiate the domain adaptation workshop to help you define your annotation goals and customize the prompt templates for warehouse-specific events.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/nvidia/tao-generate-video-reasoning-annotations/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip tao-generate-video-reasoning-annotations.zip -d .agents/skills
Documentation by NVIDIA Corporation
Video Reasoning Annotation Pipeline
Standalone install? If this session was not initialized by the TAO skill bank plugin, run the
tao-setupskill first (host preflight, credentials, cross-skill discovery).
Generate Chain-of-Thought training datasets from videos by producing multi-level captions, structured descriptions, and QA pairs (MCQ, binary, open-ended) with step-by-step reasoning traces. Domain-agnostic by default — customize prompts for any video domain.
Purpose
Transform raw videos into CoT Q&A training data for video understanding models. VLMs (e.g., Gemini, Qwen) act as "teacher" annotators: Steps 0–1 require the model to see the video (VLM calls); Steps 2–3 are text-to-text (cheaper LLM calls).
Pipeline architecture
Step 0: [Optional] Filter & classify videos → Keep domain-relevant, classify anomaly vs normal
Step 1a: Global + dense captions → VLM: narrative summary + timestamped events
Step 1b: Chunk captions → VLM: fixed-duration segment micro-captions
Step 1c: [Optional, anomaly only] Highlight → LLM extracts anomaly timestamp, VLM captions clip
Step 2: Description synthesis → LLM: synthesize captions into structured narrative
Step 3: QA generation → LLM: MCQ, binary, open-ended with reasoning
Step 4: Parse outputs → Per-task `tao-vl-reason-v1.0` JSON filesSteps are individually selectable via workflow.steps. The pipeline has built-in resume — each step skips already-processed videos, so re-running after a prompt tweak is safe.
Initial consultation
When the user invokes this skill, walk through these questions in order. Don't skip — getting domain and VLM access right up front prevents wasted runs.
1. Videos
Path to the video directory and/or a JSONL with
{"video_path": "..."}per line.Confirm format (
.mp4preferred;.avi,.mov,.mkvalso walked).
2. Domain — drives prompt selection
Ask the user: "What domain are these videos from?" Choose one of the following branches:
| Domain | What to do |
|---|---|
| general | Use the default prompts. Set prompts_module: "" (or omit). The built-in nvidia_tao_ds.auto_label.video_reasoning_annotation.prompts covers domain-agnostic content. |
| traffic (CCTV intersections, highways; dashcam excluded) | Use the reference module. Set prompts_module: "nvidia_tao_ds.auto_label.video_reasoning_annotation.prompts_traffic", or copy references/prompts_traffic.py into the user's project and tune for their specific camera angles, then point prompts_module at the copy. |
| warehouse (industrial site CCTV — safety, operations, security) | Same pattern. Set prompts_module: "nvidia_tao_ds.auto_label.video_reasoning_annotation.prompts_warehouse", or copy references/prompts_warehouse.py and tune. |
| custom (any other domain) | Run the workshop in references/domain_adaptation.md. It walks through: Phase 1 — question types the user wants the model to answer; Phase 2 — caption-requirements checklist; Phase 3 — fill the [PLACEHOLDER] markers in nvidia_tao_ds.auto_label.video_reasoning_annotation.prompt_template. The two reference modules above are working examples to model after. Do this before any pipeline runs. |
3. Anomaly / normal / mixed
Mixed dataset →
workflow.mode: "auto"(Step 0 classifies each video).Pre-split anomaly only →
workflow.mode: "anomaly", drop Step 0.Pre-split normal only →
workflow.mode: "normal", drop Steps 0 and 1c.
4. VLM / LLM endpoint — confirm access before running
Gemini (default for both
vlm.backendandllm.backend): user needsGOOGLE_API_KEYset, or to put the key in the YAML.OpenAI-compatible (Qwen via vLLM, NIM endpoint, etc.): user provides
base_url,model_name, andapi_key.Steps 2–3 are text-only — a smaller/cheaper LLM is fine for
llm.backendeven whenvlm.backendis a frontier video model.
If the user has no endpoint at all and wants to self-host, point them at the skills/applications/tao-run-inference-service skill — a workflow that stands up a network-specific TAO inference microservice locally and exposes an OpenAI-compatible endpoint. Should support Cosmos, Qwen, and Gemma. Check skills/applications/tao-run-inference-service/references/service.yaml for the current valid_network_arch_config_basenames list before relying on a specific model.
If the user doesn't have endpoint access ready and isn't ready to set one up, stop here and help them figure it out first.
5. Pilot vs full run
Recommend a 5–10 video pilot when domain is
custom, when any prompt was edited, or when this is the user's first run.Full-run is fine for
general/traffic/warehouseonce the user has previously verified output quality on the same data type.The pipeline has built-in resume, so a pilot followed by a full run does not re-process the pilot videos.
Quick start
The pipeline runs inside the TAO Toolkit container via the auto_label CLI:
auto_label generate -e /path/to/spec.yaml \
results_dir=/results \
video_reasoning_annotation.data.video_root=/videos \
video_reasoning_annotation.vlm.gemini.api_key=$GOOGLE_API_KEY \
video_reasoning_annotation.workflow.mode=autoGenerate a default spec to start from:
auto_label default_specs results_dir=/results module_name=auto_label
# then set: autolabel_type: "video_reasoning_annotation"All fields support Hydra dot-notation overrides on the command line. For the full YAML reference (every field, model/endpoint setup, error patterns), see references/configuration.md.
Pilot workflow
Use this when running a 5–10 video pilot:
Run the pipeline on the pilot subset with the chosen
prompts_moduleandworkflow.mode.Inspect
results_dir/step_1a_caption/captions.jsonl— captions accurate, capturing the right level of detail?Inspect
results_dir/step_3_qa/qa_output.jsonl— questions meaningful, answers correct, reasoning logical?If quality is insufficient: adjust the prompts (in
prompts_moduleif domain-customized, or fall back togeneralif a domain module is over-tuned), and re-run. The pipeline auto-skips already-processed videos.Once satisfied, scale to the full dataset by pointing
data.video_root(ordata.input_jsonl_files) at the full set and re-running with the sameresults_dir(resume) or a fresh one (full re-run).
Quality compounds downstream — bad captions produce bad descriptions which produce bad QA. Focus iteration on Step 1a/1b output first; descriptions and QA usually improve once captions are right.
Configuration summary
Key fields (full reference in references/configuration.md):
| Field | Default | Description |
|---|---|---|
workflow.steps | ["0","1a","1b","1c","2","3","4"] | Which pipeline steps to execute |
workflow.mode | "auto" | "auto", "anomaly", or "normal" |
vlm.backend | "gemini" | "gemini" or "openai" (OpenAI-compatible) |
llm.backend | "gemini" | Same options; text-only, cheaper model works |
workflow.max_workers | 4 | Parallel threads per step (watch API rate limits) |
license | "" | Optional: written to metadata.license in step 4 outputs (e.g. "CC-BY-4.0") |
description_extra | "" | Optional: extra text appended to per-task descriptions in step 4 metadata |
prompts_module | "" | Dotted import path to custom prompts module |
Prompts
Built-in (general):
nvidia_tao_ds.auto_label.video_reasoning_annotation.prompts— domain-agnostic, used by default.Template:
nvidia_tao_ds.auto_label.video_reasoning_annotation.prompt_template— same 26 keys with[PLACEHOLDER]markers for domain customization.Reference modules (working examples for the consultation's
traffic/warehousebranches): references/prompts_traffic.py, references/prompts_warehouse.py.Custom domains: see references/domain_adaptation.md for the full workshop and placeholder reference.
Inputs
video_root: Directory of videos (walked recursively for.mp4,.avi,.mov,.mkv).input_jsonl_files: List of JSONL files with{"video_path": "..."}per line. Thevideokey is also accepted; extra fields are allowed.filter_field: Optional boolean field to filter JSONL entries.
Provide video_root, input_jsonl_files, or both (lists merge).
Outputs
All outputs go to results_dir/ with per-step subdirectories (step_0_filter/, step_1a_caption/, …, step_4_output/):
Steps 0–3: JSONL — one JSON object per video per line.
Step 4: One
<task>.jsonper non-empty task type, in thetao-vl-reason-v1.0envelope. Up to 10 files:mcq.json,mcq_openended.json,bcq.json,bcq_openended.json,open_qa.json,causal_linkage.json,temporal_localization.json,temporal_description.json,scene_description.json,video_summarization.json.
Each step 4 file looks like:
{
"format": "tao-vl-reason-v1.0",
"metadata": {"type": "annotation", "task": "<task>", "date": "YYYY-MM-DD",
"description": "<per-task + description_extra>", "license": "<from config>"},
"media_root": "<data.video_root>" | null,
"items": [{"video_id": "...", "question": "...", "answer": "...", "reasoning": "..."}, ...]
}media_root mirrors data.video_root (or null when unset); each item's video_id is the entry's video path with the video_root prefix stripped. Set license and description_extra in the spec to populate the metadata.
Prerequisites
Container:
nvcr.io/nvidia/tao/tao-toolkit:7.3.0-pyt. <!-- versions-key: images.tao_toolkit.pyt -->ffmpeg / ffprobe: required for chunk captioning (Step 1b) and highlight extraction (Step 1c).
VLM endpoint: at least one — Gemini API key or OpenAI-compatible endpoint.
Files
- ATTRIBUTION.mdAdded by AutoGineer990 B
- BENCHMARK.md7.3 KiB
- LICENSELicence file added by AutoGineer11.2 KiB
- SKILL.md11.0 KiB
- config/skillspector-baseline.yaml834 B
- evals/evals.json879 B
- references/configuration.md8.5 KiB
- references/domain_adaptation.md8.1 KiB
- references/prompts_traffic.pyScript78.7 KiB
- references/prompts_warehouse.pyScript85.0 KiB
- references/skill_info.yaml1.5 KiB
- skill-card.md5.2 KiB
- skill.oms.sig5.9 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Declares allowed tools, Needs an API key, Needs Python
The author says: Requires docker + nvidia-container-toolkit + at least one VLM endpoint (Gemini API key or OpenAI-compatible).
Security
What our scanner noticed· Medium risk
- Large base64-encoded blobs
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This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Created by
- NVIDIA Corporation
- Publisher
- NVIDIA (Official)
- Source
- NVIDIA/skills · skills/tao-generate-video-reasoning-annotations · commit fce3cca
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE-APACHE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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