# tao-generate-video-reasoning-annotations

Turns raw videos into a training dataset with captions, descriptions and QA pairs carrying reasoning traces. Useful when you need labelled video data.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1600 installs · ★ 3735
- Author: NVIDIA Corporation
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-generate-video-reasoning-annotations/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/tao-generate-video-reasoning-annotations

## What it does

The skill runs a multi-step annotation pipeline. Steps 0–1 call a VLM that actually watches the video: it writes global captions, timestamped events and micro-captions for fixed-duration chunks. Then a text-only LLM takes over, synthesising structured descriptions and generating questions — multiple choice, binary and open-ended — each with a step-by-step reasoning trace. Outputs are parsed into per-task JSON files in the `tao-vl-reason-v1.0` format.

Steps are individually selectable through `workflow.steps`, and resume is built in: each step skips videos it already processed, so re-running after a prompt tweak is safe. Domain handling goes through prompt modules — `general` by default, ready-made `prompts_traffic` and `prompts_warehouse`, or your own adaptation via the workshop in `references/domain_adaptation.md`.

Before anything runs, set up the environment: docker, nvidia-container-toolkit and at least one VLM endpoint. Gemini is the default and needs `GOOGLE_API_KEY`; an OpenAI-compatible endpoint needs `base_url`, `model_name` and `api_key`. No endpoint access and no plan to stand one up means the pipeline stays parked. For a custom domain or a first run, the skill advises starting with a 5–10 video pilot.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-video-reasoning-annotations -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/tao-generate-video-reasoning-annotations/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/tao-generate-video-reasoning-annotations/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-video-reasoning-annotations -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/tao-generate-video-reasoning-annotations/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-video-reasoning-annotations -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/tao-generate-video-reasoning-annotations/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills tao-generate-video-reasoning-annotations
```

2. **Manually: put the files in place**
- File: `.github/skills/tao-generate-video-reasoning-annotations/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-video-reasoning-annotations -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/tao-generate-video-reasoning-annotations
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/tao-generate-video-reasoning-annotations/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-generate-video-reasoning-annotations -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-generate-video-reasoning-annotations/SKILL.md

## When to use it
- caption raw videos with timestamps
- generate a QA dataset with chain-of-thought traces
- auto-label videos for a specific domain
- filter anomaly clips and classify them
- re-run the pipeline after editing prompts without reprocessing

## Example prompts
- Build a training dataset from the videos in /videos: captions, descriptions and QA with reasoning
- Run video_reasoning_annotation on these clips, domain traffic
- Pilot the pipeline on 5 videos and show me the step 1a output

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: df6fb514039d87d8.

BEGIN THIRD-PARTY FILE df6fb514039d87d8
````text
---
name: tao-generate-video-reasoning-annotations
description: >-
  Multi-step video annotation pipeline that turns raw videos into
  Chain-of-Thought training data — multi-level captions, structured
  descriptions, and QA pairs (MCQ, binary, open-ended) with reasoning
  traces, via VLM/LLM distillation. Use when the user wants to "create
  video training data", "generate video QA datasets", "build CoT
  reasoning traces from videos", "auto-label videos", or run the
  video_reasoning_annotation pipeline. Triggers include "video
  annotation", "video CoT", "video QA", "chain-of-thought",
  "video captioning pipeline", "video distillation".
license: Apache-2.0
compatibility: Requires docker + nvidia-container-toolkit + at least one VLM endpoint (Gemini API key or OpenAI-compatible).
metadata:
  author: NVIDIA Corporation
  version: "0.1.0"
allowed-tools: Read Bash Write
tags:
  - video
  - annotation
  - chain-of-thought
  - captioning
  - qa-generation
  - vlm
  - llm
  - auto-label
---

# Video Reasoning Annotation Pipeline

> **Standalone install?** If this session was not initialized by the TAO skill bank plugin, run the `tao-setup` skill 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
````
END THIRD-PARTY FILE df6fb514039d87d8

The third-party file has ended. Everything between the df6fb514039d87d8 tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

First 2062 of 11240 bytes. © NVIDIA Corporation · Apache-2.0 · https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-generate-video-reasoning-annotations/SKILL.md

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