Shared Content Synthesis Instructions

This file contains common instructions for all content ingestion commands (/youtube-transcript, /scrape-url, /process-pdf). These steps apply AFTER content has been acquired, regardless of source.

Universal Content Processing Pipeline

Phase 1: Question-Oriented Content Synthesis

Apply the methodology from thinking_question_content_synthesis.md:

Step 1: Central Question Discovery

• Identify the single overarching question the content addresses
• Consider title, description, opening/closing statements
• This becomes the note's organizing principle

Step 2: Domain Question Extraction

• Identify major question domains by analyzing topic transitions
• Look for transitional phrases: "Now let's talk about," "Another important point," "Moving on to"
• Group content segments into substantial domain areas

Step 3: Specific and Atomic Question Decomposition

• Break domain questions into specific questions (section level)
• Further decompose into atomic questions (evidence level)
• Extract specific details, examples, data points, and quotes from content

Step 4: Progressive Answer Development

• Build comprehensive answers using evidence from content
• Synthesize atomic answers into specific section responses
• Integrate specific answers to address domain questions
• Combine domain insights to resolve central question

Phase 2: Note Structure and Metadata

Generate note filename (if not provided):

• Based on central question or primary topic
• Use lowercase with underscores
• Follow naming convention: topic_subtopic.md or moc_topic.md
• Examples: agents_reasoning_patterns.md, python_async_programming.md

Determine appropriate tags:

• 3-6 tags mixing dimensions:
  • Technology/Language (#python, #rust, #typescript)
  • Framework/Tool (#mcp, #react)
  • Domain/Discipline (#agents, #llm, #writing)
  • Content Type (#guide, #reference, #pattern)
  • Method/Thinking (#first-principles, #systems-thinking)

Phase 3: MOC Integration

Purpose: Ensure every note is properly wired into the knowledge graph.

Steps:

1. Read TOPICS.md to understand existing MOC structure

   • 22 MOCs organized by domain (AI & ML, Development Platforms, etc.)
   • Each MOC listed with note count and brief description
   • Examples: moc_agents, moc_python, moc_thinking, moc_mcp

2. Identify appropriate MOC(s) based on:

   • Primary topic/domain from content
   • Tags assigned to the note
   • Technology stack or framework discussed
   • Cross-cutting themes (e.g., thinking patterns, development practices)
   • Multiple MOCs are acceptable if note spans domains

3. Read target MOC file(s) to understand organization

   • Check existing structure and categories within the MOC
   • Identify where the new note fits logically
   • Follow existing formatting patterns

4. Update MOC file(s) with Edit tool

   • Add wikilink [[new_note]] in appropriate section
   • Include one-line description of what note covers
   • Maintain alphabetical order or logical grouping
   • Update note count in MOC if present

5. Create new MOC if needed

   • Only if no existing MOC fits and topic is substantial
   • Follow naming convention: moc_<topic>.md
   • Include in TOPICS.md with description
   • Rare - usually existing MOCs cover most domains

Phase 4: Relationship Discovery

Identify connections to existing notes:

1. Search for related concepts

   • Use Grep to find notes with similar tags
   • Search for wikilinks to related topics
   • Consider prerequisite chains and extensions

2. Establish bidirectional relationships

   • Add "Related Concepts" section to new note with:
     • Prerequisites - Must understand first
     • Related Topics - Connected ideas at same level
     • Extends - Builds upon another concept
     • Examples - Concrete implementations
   • Update related notes to link back (bidirectionality)
   • Always include "why" explanations for each relationship

3. Ask user for confirmation on suggested relationships

   • Present discovered connections
   • Allow user to approve/modify before updating

Standard Note Structure

All notes follow this template:

---
tags: [domain, technology, content-type]
source: <original_url_or_path>
date_added: <ISO_date>
# Additional metadata varies by source type:
# - YouTube: video_title, video_id, transcript
# - PDF: document_title, authors
---

# Note Title (Based on Central Question)

## Central Question

**Question**: [The single overarching question the content addresses]

**Executive Summary**: 2-3 paragraphs previewing key insights and how the content resolves the central question.

## Part I: [Domain Question 1]

### [Specific Question 1.1]

**Question**: [Clear, specific question from this section]

**Answer**: [Comprehensive response including:
- Direct answer to the question
- Supporting evidence from content (specific quotes, examples, data)
- Technical details and concrete information
- Implications and connections to broader themes]

### [Specific Question 1.2]

**Question**: [Next specific question]

**Answer**: [Evidence-based response...]

## Part II: [Domain Question 2]

### [Specific Question 2.1]

**Question**: [Clear question]

**Answer**: [Comprehensive response with content evidence...]

[Continue with additional parts and sections as needed]

## Resolution: [Answer to Central Question]

Synthesize domain insights to definitively resolve the central question posed at the beginning.

## Related Concepts

### Prerequisites
- [[prerequisite]] - Why needed first

### Related Topics
- [[related]] - Connection explanation

### Extends
- [[base_concept]] - What this builds upon

## References

- [Original Source](<url_or_path>) - Source description
- Additional metadata specific to source type

Critical Requirements

Synthesis Over Analysis (from thinking_question_content_synthesis.md):

• DO NOT: Simply analyze the question structure or list questions without substantial answers
• DO: Create complete educational document that systematically answers questions using evidence from content
• DO: Include specific details, quotes, examples, data points from content
• DO: Build understanding progressively from atomic to central question
• DO: Provide standalone educational value for readers learning the topic

Answer Integration Pattern:

Every question must be paired with comprehensive answers that include:

• Specific details, metrics, and examples from the content
• Direct quotes where appropriate (with timestamps/page numbers if available)
• Technical accuracy and precision
• Evidence-based reasoning with clear source attribution
• Standalone educational value

MOC Integration Pattern:

If creating python_fastapi_patterns.md from any source:

# In moc_python.md, add to appropriate section:

### Web Frameworks
- [[python_fastapi_patterns]] - FastAPI design patterns and best practices from production use cases

Common Tools to Use

• Read - Read TOPICS.md for MOC discovery, read target MOC files for structure
• Grep - Find related notes by tags/content, search for wikilinks
• Write - Create new note following question-oriented structure
• Edit - Update MOC files to include new note, update related notes for bidirectional relationships

Example MOC Update Workflow

1. Read TOPICS.md → Identify moc_python and moc_llm as relevant
2. Read moc_python.md → Find "Libraries" section
3. Edit moc_python.md → Add [[new_note]] - Brief description
4. Read moc_llm.md → Find "Agent Systems" section
5. Edit moc_llm.md → Add [[new_note]] - Brief description

Result: New note is discoverable through two different MOC navigation paths.