memorysaver/looplia-core/content-documenter

Content Documenter Skill

Transforms media-reviewer analysis into structured JSON documentation.

What This Skill Does

Takes your media-reviewer analysis and produces ContentSummary JSON with all 16 required fields.

Input

You should have already used media-reviewer skill which provides:

• Deep understanding of content structure
• Core ideas and their connections
• Narrative flow analysis
• Key moments and quotes with timestamps
• Thematic patterns

Output Fields

Core Fields (7)

1. contentId (string)

• Copy from input file frontmatter id field

1. headline (string, 10-200 chars)

• One compelling sentence capturing the essence
• Should make someone want to read more
• Focus on the unique value or insight

1. tldr (string, 20-500 chars)

• 3-5 sentence summary
• Cover the main points concisely
• Answer: "What is this about and why does it matter?"

1. bullets (string[], 1-10 items)

• Key points as bullet list
• Each bullet = one distinct insight
• Ordered by importance or logical flow

1. tags (string[], 1-20 items)

• Topic tags for categorization
• Include broad tags (e.g., "AI") and specific (e.g., "Constitutional AI")
• Lowercase, no special characters

1. sentiment ("positive" | "neutral" | "negative")

• Overall tone of content
• Based on language and framing, not topic

1. category (string)

• Content type: article, video, podcast, tutorial, interview, etc.

1. score.relevanceToUser (number, 0-1)

• Based on user-profile.json topics
• 1.0 = perfectly matches user interests
• 0.0 = no relevance to user interests
• Calculate: sum(matched topic weights) / sum(all topic weights)

Documentary Fields (8)

1. overview (string, min 50 chars)

• 2-3 rich paragraphs introducing the material
• Write for someone unfamiliar with the content
• Cover: what it is, who it's for, why it matters
• Set the stage for detailed analysis

1. keyThemes (string[], 3-7 items)

• Main topics/themes identified
• Broader than tags, more conceptual
• Examples: "The tension between safety and capability"

1. detailedAnalysis (string, min 100 chars)

• Documentary-style breakdown
• Follow the content's structure
• Explain each major section/segment
• Include specific details and examples

1. narrativeFlow (string, min 50 chars)

• How the content progresses
• Explain the arc and transitions
• Show how ideas build on each other
• Note pacing and structure choices

1. coreIdeas (CoreIdea[], 1-10 items)

Each item has:

• concept: string - the idea name
• explanation: string (min 10 chars) - what it means in this context
• examples?: string[] - specific examples from content

1. importantQuotes (Quote[], 0-20 items)

Each item has:

• text: string - EXACT verbatim quote (never paraphrase!)
• timestamp?: string - HH:MM:SS or MM:SS format (for video/audio)
• context?: string - what was being discussed when this was said

1. context (string, min 20 chars)

• Background needed to understand the content
• Prerequisites or prior knowledge assumed
• Historical or situational context
• Related work or references mentioned

1. relatedConcepts (string[], 0-15 items)

• Related topics mentioned or implied
• Connections to other ideas or fields
• What someone might want to learn next

Timestamp Format

For video/audio content:

• 0:30 - 30 seconds
• 2:45 - 2 minutes 45 seconds
• 1:30:00 - 1 hour 30 minutes
• Always use the format from the source (don't normalize)

JSON Output Example

{
  "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"]
}

Quality Checklist

Before outputting, verify:

• All 16 fields are present
• All minimum lengths are met
• Quotes are exact (verbatim)
• Timestamps are included for video/audio
• contentId matches input file
• relevanceToUser is calculated from user profile
• JSON is valid

Important Rules

• Include ALL 16 fields - never omit any
• Use exact verbatim quotes - never paraphrase
• Include timestamps when source has them
• Meet all minimum character requirements
• Follow the JSON schema exactly
• Base relevanceToUser on actual user profile
• Never paraphrase quotes
• Never add interpretation beyond source
• Never omit required fields
• Never guess timestamps - only include if known