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Generate structured ContentSummary JSON from analyzed content. Use after media-reviewer to produce final output with all 16 fields.
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Based on SOC occupation classification
| name | content-documenter |
| description | Generate structured ContentSummary JSON from analyzed content. Use after media-reviewer to produce final output with all 16 fields. |
| tools | Read, Write |
Transforms media-reviewer analysis into structured JSON documentation.
Takes your media-reviewer analysis and produces ContentSummary JSON with all 16 required fields.
You should have already used media-reviewer skill which provides:
contentId (string)
headline (string, 10-200 chars)
tldr (string, 20-500 chars)
bullets (string[], 1-10 items)
tags (string[], 1-20 items)
sentiment ("positive" | "neutral" | "negative")
category (string)
score.relevanceToUser (number, 0-1)
overview (string, min 50 chars)
keyThemes (string[], 3-7 items)
detailedAnalysis (string, min 100 chars)
narrativeFlow (string, min 50 chars)
coreIdeas (CoreIdea[], 1-10 items) Each item has:
importantQuotes (Quote[], 0-20 items) Each item has:
context (string, min 20 chars)
relatedConcepts (string[], 0-15 items)
For video/audio content:
0:30 - 30 seconds2:45 - 2 minutes 45 seconds1:30:00 - 1 hour 30 minutes{
"contentId": "abc123",
"headline": "Constitutional AI introduces a novel approach to aligning language models through self-critique",
"tldr": "This video explains Constitutional AI, Anthropic's method for training helpful and harmless AI assistants. The approach uses a set of principles (a 'constitution') to guide the model's self-improvement, reducing the need for human feedback while maintaining safety.",
"bullets": [
"Constitutional AI uses self-critique guided by explicit principles",
"The method reduces reliance on human feedback for safety training",
"Models learn to identify and correct their own harmful outputs"
],
"tags": ["ai", "safety", "alignment", "constitutional-ai", "anthropic", "rlhf"],
"sentiment": "positive",
"category": "video",
"score": { "relevanceToUser": 0.85 },
"overview": "This comprehensive video from Anthropic introduces Constitutional AI...",
"keyThemes": [
"AI Safety and Alignment",
"Self-supervised learning for safety",
"Reducing human feedback requirements",
"Explicit principles for AI behavior"
],
"detailedAnalysis": "The video opens with a clear problem statement...",
"narrativeFlow": "The presentation follows a classic problem-solution structure...",
"coreIdeas": [
{
"concept": "Constitutional AI",
"explanation": "An alignment approach where AI models critique and revise their own outputs based on explicit principles",
"examples": ["A model generating a harmful response, then self-critiquing"]
}
],
"importantQuotes": [
{
"text": "The key insight is that we can use AI to supervise AI",
"timestamp": "12:34",
"context": "Explaining the core mechanism that makes Constitutional AI scalable"
}
],
"context": "This video builds on prior work in RLHF...",
"relatedConcepts": ["RLHF", "red teaming", "scalable oversight", "AI alignment"]
}
Before outputting, verify:
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