An AI-powered utility that automatically parses video URLs to generate comprehensive manuscript, outline, and graphic notes.
The fastest way to install a skill directly from the registry.
npx clawhub@latest install ai-notes-video
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
Copy this prompt to OpenClaw to install it automatically.
Help me install ai-notes-video using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Get the raw skill files in a ZIP archive.
AI Video Notes is a sophisticated tool designed for Openclaw Skills that automates the process of extracting knowledge from video content. By simply providing a video download address, the tool utilizes Baidu's AI capabilities to download, analyze, and synthesize video data into three distinct, high-quality note formats. This skill is essential for developers and researchers who need to turn long-form video content into searchable, structured text and visual mind maps without manual transcription.
Whether you are processing educational content or corporate meetings, this skill streamlines the workflow by handling complex asynchronous tasks and providing granular status updates until the final notes are ready for consumption.
To integrate this tool with your Openclaw Skills environment, ensure you have the necessary credentials and runtime configured.
export BAIDU_API_KEY='your_api_key_here'
python3 scripts/ai_notes_task_create.py 'https://example.com/video.mp4'
python3 scripts/ai_notes_task_query.py 'your-unique-task-id'
The skill returns structured data categorized by template numbers (tpl_no). Each note type is accessible within the task result detail object:
| Template ID (tpl_no) | Note Type | Content Details |
|---|---|---|
| 1 | Manuscript | Full document-style transcript and notes |
| 2 | Outline | Structured hierarchy; includes a Mind Map marked by 'Mind' tags |
| 3 | Graphic & Text | Visual-rich notes combining text with key video frames |
Status tracking is handled via integer codes: 10002 (Success), 10000 (Processing), and other codes for specific failure states.
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