Perfect! Now I have comprehensive findings. Let me compile the report:

## Genesis System - Research Automation & Knowledge Ingestion Report

I've identified extensive web scraping, research automation, and knowledge ingestion systems throughout E:\genesis-system. Here are the key findings:

### 1. WEB SCRAPING & RESEARCH AUTOMATION SYSTEMS

**Core Scraping Infrastructure:**
- **E:\genesis-system\core\apify_scraper.py** - ViralSiphonScraper class using Apify API to scrape YouTube channels for inactivity detection and lead qualification. Processes lead files (CSV) filtering for YouTube creators and checks if they've posted YouTube Shorts in the last 14 days.

**YouTube Content Processing:**
- **E:\genesis-system\core\youtube_transcripts.py** - Multi-level YouTube transcript extraction system with 6-tier failsafe architecture:
  - Level 1: MCP Server (direct API integration)
  - Level 2: youtube-transcript-api (Python library)
  - Level 3: yt-dlp subtitles (download & parse)
  - Level 4: Browser extraction (Playwright/Selenium)
  - Level 5: Whisper transcription (audio extraction + STT)
  - Level 6: Manual captions cache
  - Supports batch processing, research indexing, pattern detection, and knowledge extraction/summarization

- **E:\genesis-system\core\intelligence\youtube.py** - YouTubeIntelligencePipeline that extracts skills and insights from YouTube video transcripts. Monitors specific YouTube channels/videos, performs daily monitoring with 24-hour lookback, extracts skills (programming, data analysis, design, leadership, finance, quantum computing) and insights using keyword matching.

### 2. KNOWLEDGE INGESTION SYSTEMS

**Main Ingestion Pipelines:**
- **E:\genesis-system\AIVA\knowledge_ingest.py** - AIVA Knowledge Ingestion Pipeline (PM-029) that extracts entities, axioms, and relationships from documents. Uses EntityExtractor (regex-based pattern matching for SYSTEMS, APIs, FILES, CONFIG, PERSON, DOMAIN), AxiomExtractor (extracts "X is Y", "X uses Y", "X can Y" statements), and RelationshipMapper to map entity co-occurrences. Stores results in RLM (relationship layer memory).

- **E:\genesis-system\AIVA\queen_enhanced\ingestion_production.py** - Patent Ingestion Pipeline supporting multiprocessing for batch PDF processing. Pipeline stages: extract text from PDFs → clean text → chunk into 2000-char segments → embed with SentenceTransformers → store in vector DB → update knowledge graph. Includes duplicate detection via MD5 hashing and error recovery.

- **E:\genesis-system\core\ingest_knowledge.py** - Knowledge cortex ingestion system for patents and markdown documents. Processes patent visualization boards, extracts unique patents, stores as knowledge items in Genesis Memory Cortex with metadata tagging.

### 3. RALPH WIGGUM LOOP (RWL) - AUTONOMOUS EXECUTION SYSTEM

**Ralph Wiggum Loop Core:**
- **E:\genesis-system\loop\ralph_runner.py** - Main autonomous execution engine implementing the Ralph Wiggum methodology. Features:
  - Pulls one story at a time (passes: false)
  - Executes with Gemini 2.0 Flash API in fresh context
  - Self-verifies against acceptance criteria
  - Commits results to Git with atomic transactions
  - Tracks progress in progress.txt (short-term memory)
  - Uses $300 Gemini API budget for autonomous execution
  - Implements exponential backoff retry logic
  - Direct file verification shims for verification patterns
  - Supports status checking, looping, and budget management

**Task Definitions:**
- **E:\genesis-system\loop\tasks.json** - Master task board
- **E:\genesis-system\loop\openwork_integration_tasks.json** - Tasks dispatched to OpenWork for browser automation
- **E:\genesis-system\loop\pitch_suite_tasks.json** - Pitch-related tasks
- **E:\genesis-system\loop\viralsiphon_tasks.json** - ViralSiphon lead validation tasks
- **E:\genesis-system\loop\youtube_insight_tasks.json** - YouTube content analysis tasks
- **E:\genesis-system\loop\zero_stall_protocol_tasks.json** - Zero-stall autonomous execution tasks

**Task Dispatcher:**
- **E:\genesis-system\loop\openwork_task_dispatcher.py** - Converts RWL stories to OpenWork action sequences for browser automation, file operations, document generation, data collection, screenshots, form filling, API interactions, and code execution.

### 4. YOUTUBE & VIDEO PROCESSING

**YouTube Tools:**
- **E:\genesis-system\core\youtube_insight_dispatcher.py** - Dispatches YouTube analysis tasks
- **E:\genesis-system\core\youtube_transcript_rwl.py** - Ralph Wiggum loop integration for transcript processing
- **E:\genesis-system\scripts\batch_youtube_ingest.py** - Batch processing for YouTube content ingestion
- **E:\genesis-system\skills\youtube_scout_skill.py** - Skill-based YouTube scouting
- **E:\genesis-system\tools\youtube_learner.py** - Learning tool for extracting insights from YouTube
- **E:\genesis-system\tools\mcp_youtube_bridge.py** - MCP bridge for YouTube integration
- **E:\genesis-system\data\youtube_scout_channels.json** - Tracked YouTube channels database
- **E:\genesis-system\config\n8n\youtube-scout-nightly.json** - n8n automation for nightly YouTube scouting
- **E:\genesis-system\logs\youtube_scout.jsonl** - Logging of YouTube scouting activities

### 5. COMPETITIVE INTELLIGENCE & RESEARCH

- **E:\genesis-system\core\intelligence\competitors.py** - Competitive intelligence gathering
- **E:\genesis-system\docs\research\competitive_intelligence_raw.json** - Raw competitive data
- **E:\genesis-system\KNOWLEDGE_GRAPH\youtube_*.json** - Multiple YouTube research insight files (combined, jan13 snapshots)
- **E:\genesis-system\sprint-checkpoints\phase-3-intelligence.json** - Phase 3 intelligence checkpoint

### 6. SOCIAL & FORUM INTEGRATION

- **E:\genesis-system\core\instantly_client.py** - Client for "Instantly" (likely LinkedIn/social automation)
- **E:\genesis-system\scripts\tradie_lead_processor.py** - Processes tradesperson/contractor leads (likely from social platforms)
- **E:\genesis-system\scripts\seed_tradie_tasks.py** - Seeds tradie-related tasks
- **E:\genesis-system\.venv/lib/python3.12/site-packages/yt_dlp/extractor/reddit.py** - Reddit extraction capability installed in environment

### 7. "RALPH WIGGUM" SPECIFIC FILES & REFERENCES

Ralph Wiggum Loop appears throughout the system as the autonomous execution methodology:
- Named after a comedic "I'm in danger" character (metaphor for decisive action without hesitation)
- Documents found in: `.cache-redirect/gemini/antigravity/brain/` directories with multiple walkthroughs and implementation plans
- Protocol documented in: **PROBING_VS_EXECUTING_PROTOCOL.md** - defines job modes where Ralph operates autonomously without asking for permission
- References in task boards indicate RWL is the primary autonomous execution framework
- Specific references to "Zero-Stall Ralph Wiggum Loop" eliminating permission request stalls

### 8. RESEARCH DIRECTORIES

Multiple research output directories identified:
- E:\genesis-system\research
- E:\genesis-system\AIVA\queen_outputs\research
- E:\genesis-system\core\cost_tracker\research
- E:\genesis-system\core\rwl\research
- E:\genesis-system\data\knowledge-bases\research
- E:\genesis-system\docs\research
- E:\genesis-system\Sunaiva\research

### Summary

The Genesis System implements a sophisticated autonomous research and knowledge ingestion pipeline centered around the Ralph Wiggum Loop (RWL) execution framework. It combines:
- **Web scraping** via Apify API and yt-dlp
- **YouTube intelligence** with multi-level transcript extraction
- **Knowledge ingestion** across patents, documents, and web content
- **Autonomous execution** via Gemini API with self-verification
- **Task orchestration** across multiple execution backends (OpenWork, n8n)
- **Competitive research** and social media monitoring

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