research("prompt engineering techniques", sources=["youtube", "papers"])

research("RAG architecture", mode="expert", sources=["youtube", "papers"])

research("prompt engineering")
         |
    +----+--------------------+
    v                         v
[YouTube API]           [OpenAlex API]          [Podcast Index]
 25 videos               20 papers               20 episodes
    |                         |                       |
    v                         v                       v
[Transcript              [Paper                  [Podcast
 Analyzer]                Scorer]                  Scorer]
 - substance              - citations             - episode count
 - hedging                - open access           - regularity
 - failures               - journal tier          - description
 - specificity            - recency               - relevance
    |                         |                       |
    +----+--------------------+-----------------------+
         v
[Credibility Scorer]
 - clickbait detection (45%)
 - upload consistency (25%)
 - description substance (20%)
 - channel age (10%)
         |
         v
[Contrarian Search]
 - auto-searches "topic + criticism/problems/risks"
 - reserves 1 slot for the best opposing viewpoint
         |
         v
[Top N Sources Selected]
         |
    +----+----+
    v         v
[NotebookLM]  [Knowledge Graph]
 notebook       entities
 created        edges
    |            |
    +----+-------+
         v
[Feedback Loop]          [Outcome Tracker]
 learns which             tracks which research
 channels work            produced real results

suggest_research()
  -> "You've researched 'RAG' 3 times — go deeper"
  -> "'vector databases' appears across 4 notebooks but was never researched directly"
  -> "'embeddings' bridges NLP and computer vision research — cross-domain opportunity"
  -> "Research on 'LLM fine-tuning' is 18 days old — URGENT: refresh"

Research -> Implement -> Results -> Better Research

track_edge(notebook_id="research-rag-architecture", result="edge", notes="New chunking strategy improved retrieval accuracy 23%")

edge_report()
  -> "3Blue1Brown: 3/4 sessions produced results (75%)"
  -> "AI topic success rate: 42% (above average)"
  -> "YouTube delivers 2x more actionable content than papers for applied topics"

git clone https://github.com/itsjwill/RECON.git
cd RECON
python -m venv .venv && source .venv/bin/activate
pip install -e .

cp .env.example .env
# Edit .env:
YOUTUBE_API_KEY=your_key_here  # https://console.cloud.google.com/apis/credentials

{
  "mcpServers": {
    "auto-research": {
      "command": "/path/to/RECON/.venv/bin/python",
      "args": ["-m", "src.server"],
      "cwd": "/path/to/RECON"
    }
  }
}

research("prompt engineering techniques", sources=["youtube", "papers"])

RECON/
├── src/
│   ├── server.py            # MCP server — 9 tools
│   ├── config.py            # Environment + settings
│   ├── youtube_search.py    # YouTube API + 8-factor scoring + expert mode
│   ├── transcript.py        # Transcript extraction + substance detection
│   ├── credibility.py       # Channel credibility (clickbait, consistency)
│   ├── paper_search.py      # OpenAlex academic paper search
│   ├── podcast_search.py    # Podcast Index search
│   ├── notebook_manager.py  # NotebookLM browser automation
│   ├── library_sync.py      # Shared library.json management
│   ├── feedback.py          # Usage tracking + stale detection
│   ├── knowledge_graph.py   # Cross-notebook entity graph
│   └── edge_tracker.py      # Research outcome tracking
├── data/
│   ├── knowledge_graph.json # Entity graph (auto-created)
│   └── feedback.json        # Usage data (auto-created)
├── pyproject.toml
├── requirements.txt
├── .env.example             # API key template
└── .env                     # Your API keys (not committed)