Tao Generate Video Reasoning Annotations

What it does

When your agent uses it

• The user wants to create video training data
• Generate video QA datasets
• Build CoT reasoning traces from videos
• Auto-label videos

Example prompts

• “create video training data”
• “generate video QA datasets”
• “build CoT reasoning traces from videos”
• “/tao-generate-video-reasoning-annotations”

Requirements

• Python 3
• Docker
• A credential in GOOGLE_API_KEY
• Compatibility (from SKILL.md): Requires docker + nvidia-container-toolkit + at least one VLM endpoint (Gemini API key or OpenAI-compatible).
• Pre-approved tools (allowed-tools): Read, Bash, Write

Workflow steps

5 steps, taken from the step headings in SKILL.md.

1. 1Videos
2. 2Domain — drives prompt selection
3. 3Anomaly / normal / mixed
4. 4VLM / LLM endpoint — confirm access before running
5. 5Pilot vs full run

What it can do on your machine

Read from SKILL.md and the folder at commit 14a98ae. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves these tools, so the agent can use them without asking each time:

  • Read
  • Bash
  • Write

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  Ships script files (Python), which the agent can run.

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names these keys or tokens, usually read from environment variables:

  • GOOGLE_API_KEY

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

• Compatibility

  Requires docker + nvidia-container-toolkit + at least one VLM endpoint (Gemini API key or OpenAI-compatible).

  From compatibility in the SKILL.md frontmatter.

Context cost

Tao Generate Video Reasoning Annotations loads about 2.8k tokens when it runs, and up to ~49k if it reads all its reference files. Until then it costs about 151 tokens; SKILL.md has 1,003 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~151

When it runs · the whole SKILL.md, loaded when a task matches

~2.8k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~49k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

• NotePre-approves every shell command (allowed-tools: Bash)SKILL.md

  allowed-tools: Read, Bash, Write

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from NVIDIA/skills at commit 14a98ae, republished under its Apache-2.0 licence (© NVIDIA). 1,003 words, ~2,792 tokens.

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".

allowed-tools

Read, Bash, Write

compatibility

Requires docker + nvidia-container-toolkit + at least one VLM endpoint (Gemini API key or OpenAI-compatible).

license

Apache-2.0

metadata.author

NVIDIA Corporation

metadata.version

0.1.0

tags

video, annotation, chain-of-thought, captioning, qa-generation, vlm, llm, auto-label

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 files

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=auto

auto_label default_specs results_dir=/results module_name=auto_label
# then set:  autolabel_type: "video_reasoning_annotation"

{
  "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": "..."}, ...]
}

© NVIDIA, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 10 other files (references) in skills/tao-generate-video-reasoning-annotations of NVIDIA/skills.

• SKILL.md
• BENCHMARK.md
• config/skillspector-baseline.yaml
• evals/evals.json
• references/configuration.md
• references/domain_adaptation.md
• references/prompts_traffic.py
• references/prompts_warehouse.py
• references/skill_info.yaml
• skill-card.md
• skill.oms.sig

Open the folder on GitHubat commit 14a98ae