One command. YouTube, papers, podcasts — scored, filtered, and loaded into a queryable NotebookLM notebook.
One command:
research("prompt engineering techniques", sources=["youtube", "papers"])
What happens:
- Searches YouTube (25 candidates), OpenAlex (474M papers), Podcast Index
- Grabs transcripts and scores them for substance — hedging language, failure discussion, specific data
- Scores channel credibility — clickbait detection, upload consistency, description depth
- Auto-searches for contrarian viewpoints (criticism, limitations, risks)
- Selects top sources across all types, creates a NotebookLM notebook
- Updates your knowledge graph — connects concepts across sessions
- Registers in the feedback loop — learns which channels actually produce results
Next research session? The system already knows what worked last time.
| Feature | Perplexity Pro | Elicit | RECON |
|---|---|---|---|
| Source scoring | No — shows top results | Citation count only | 8-factor scoring (substance, credibility, engagement, recency, velocity, relevance, transcript, feedback) |
| Transcript analysis | No | No | Analyzes what the speaker actually said — hedging, failures, specificity |
| Clickbait detection | No | N/A | Penalizes ALL CAPS titles, sensational language, empty descriptions |
| Knowledge graph | No | No | Connects concepts across sessions, finds gaps, suggests next research |
| Feedback loop | No | No | Tracks which sources produced real results — boosts them in future searches |
| Expert mode | No | No | Inverts popularity scoring to find practitioners over influencers |
| NotebookLM integration | No | No | Creates queryable notebooks you can have conversations with |
| Contrarian search | No | No | Auto-searches for criticism and opposing viewpoints |
| Cost | $20/month | $10/month | $0/month |
Most research tools find you popular content. Popular = mainstream consensus. RECON has an expert mode that inverts popularity scoring to find practitioners instead of influencers:
| Signal | General Mode | Expert Mode |
|---|---|---|
| Views | High views = good | 5K-50K sweet spot (practitioners, not mainstream) |
| Velocity | Trending = surface first | Removed entirely (trending = consensus) |
| Transcript | 14% weight | 24% weight (substance over hype) |
| Credibility | 9% weight | 20% weight (practitioners over influencers) |
| Engagement | Like ratio | Like ratio (same — honest signal either way) |
The thesis: a 12K-view video from someone who discusses what went wrong and cites specific data is worth more than a 500K-view video that says "this technique is GUARANTEED to work."
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
| Tool | What It Does |
|---|---|
research | Full pipeline: multi-source search, notebook creation, graph update |
search_videos | YouTube search with transcript + credibility scoring |
search_papers | OpenAlex academic paper search (free, 474M+ papers) |
list_research_notebooks | List all auto-created NotebookLM notebooks |
rate_research | Rate a notebook 1-5 — feeds back into future scoring |
suggest_research | AI-suggested topics from knowledge graph gaps |
knowledge_map | Visualize concept connections across all research |
track_edge | Record whether research produced real results |
edge_report | ROI report — which sources/channels actually deliver |
Most YouTube scoring looks at views and likes. RECON looks at what the person actually said.
Three signals that are hard to fake in a 20-minute video:
Nuanced thinkers acknowledge complexity:
"on the other hand", "it depends", "the tradeoff", "there are exceptions", "context matters"
Practitioners talk about what went wrong:
"doesn't work when", "the risk is", "I was wrong", "lesson learned", "the hard way"
Grounded speakers cite data:
Years, percentages, dollar amounts, "according to", "research shows", "study found"
A video can have 1M views and perfect engagement but still score low on substance if the speaker never hedges, never discusses failures, and never cites specific data.
What got dropped: Self-reported credentials. Anyone can type "10 years experience" in their channel description. Meaningless.
What replaced it:
| Factor | Weight | Why |
|---|---|---|
| Clickbait Detection | 45% | Most honest signal. "GUARANTEED RESULTS" is unfakeable garbage. |
| Upload Consistency | 25% | Regular uploads over years = committed to the craft |
| Description Substance | 20% | Length + specificity (URLs, dates, contact info) |
| Channel Age | 10% | Weak signal — punishes early adopters of new topics |
Red flags that trigger penalties:
- Channel under 3 months old
- Fewer than 10 videos
- 30%+ clickbait titles
- 50%+ ALL CAPS titles
- Empty channel description
Every research session extracts entities (topics, concepts, channels, authors) and links them.
After 5+ sessions, the graph reveals:
- Knowledge Gaps — concepts that keep appearing but haven't been researched directly
- Cross-Domain Insights — entities that bridge different research areas (e.g., "embeddings" appears in both NLP AND image generation research)
- Bridge Concepts — central nodes that connect many topics in your expertise
- Stale Notebooks — time-sensitive research decays in 14 days, general in 30
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"
The feedback loop most research tools are missing:
Research -> Implement -> Results -> Better Research
After you implement something from a research notebook:
track_edge(notebook_id="research-rag-architecture", result="edge", notes="New chunking strategy improved retrieval accuracy 23%")
The system learns:
- Which channels produce real results (boosted in future searches)
- Which topics have high success rates (surfaced first in suggestions)
- Which source types deliver (YouTube vs papers vs podcasts)
- Overall research ROI — what % of sessions produced actionable insights
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/credentialsYouTube API key is the only requirement. OpenAlex (papers) needs no key. Podcast Index is optional.
{
"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"])
| Component | Cost |
|---|---|
| YouTube Data API | Free (10,000 units/day) |
| OpenAlex Papers | Free (no key, 100K req/day) |
| Transcripts | Free (youtube-transcript-api) |
| NotebookLM | Free (Google account) |
| Podcast Index | Free (optional, needs registration) |
| Total | $0/month |
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)
Built by the team at The Agentic Advantage
Get help setting up RECON, share research workflows, and connect with other builders using AI tools that actually work.
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