Individual skill
tao-generate-video-reasoning-annotations
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".
skills/tao-generate-video-reasoning-annotations/SKILL.md
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- Nov 6, 2026
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- Apache-2.0
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- agent-skills Repository topic
- agentic-ai Repository topic
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- coding-agents Repository topic
- cuda Repository topic
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- video Declared skill tag
- annotation Declared skill tag
- chain-of-thought Declared skill tag
- captioning Declared skill tag
- qa-generation Declared skill tag
- vlm Declared skill tag
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BENCHMARK.mddoc · 7,489 bytesSKILL.mdcore · 11,240 bytesSKILL.mdcore · 11,240 bytesSKILL.mdcore · 11,240 bytesconfig/skillspector-baseline.yamlasset · 834 bytesevals/evals.jsonasset · 879 bytesreferences/configuration.mddoc · 8,678 bytesreferences/domain_adaptation.mddoc · 8,317 bytesreferences/prompts_traffic.pyruns · 80,578 bytesreferences/prompts_warehouse.pyruns · 87,002 bytesreferences/skill_info.yamlasset · 1,554 bytesskill-card.mddoc · 5,277 bytesskill.oms.sigasset · 6,081 bytes
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# 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