Analyze text or video content into structured teardown reports across four disciplines.
npx skills add https://github.com/alextangson/AutoCrew --skill teardown-alextangson
Please help me install this Agent Skill. Skill: teardown Source: https://github.com/alextangson/AutoCrew/tree/main/skills/teardown Command: npx skills add https://github.com/alextangson/AutoCrew --skill teardown-alextangson
This skill provides automated teardown analyses for text and video content, delivering structured insights across marketing, psychology, and communication to inform creative decisions.
Provide a text or video for teardown and I will generate a structured analysis report.
High-intent search queries and answers about installing and using this skill.
A content teardown analyzes text or video content to produce a structured report across four disciplines: communication, psychology, content structure, and audiovisual language. You simply provide textual copy or a video link to generate detailed, fielded insights.
A text teardown analysis outputs structured insights including HKRR, hook analysis, and rhythm/clock mappings. This helps break down textual copy to inform creative decisions and content strategy.
Video teardown requires an Omni analysis module and a configured services.json file for video crawling and model access. It performs multi-modal analysis and saves comprehensive reports directly to the intel repository.
Teardown reports are designed for knowledge-base ingestion and creative reuse. The skill stores all generated fielded reports in the intel system, surfacing structured insights to inform future content creation and marketing decisions.
Using a multi-modal video teardown is the best way to analyze audiovisual language and psychology. It evaluates content across four specific disciplines, outputting a detailed report suitable for informing creative marketing decisions.
Generated teardown reports are stored in the intel repository located at ~/.autocrew/data/pipeline/intel/_teardowns. This storage mechanism ensures reports are saved for future knowledge-base ingestion and reuse.
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