Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.
Extract structured, quality-scored domain knowledge from any AI model — in-session from closed models (no API key) or locally from open-source models via Ollama.
bdistill turns your AI subscription sessions into a compounding knowledge base. The agent answers targeted domain questions, bdistill structures and quality-scores the responses, and the output accumulates into a searchable, exportable reference dataset.
Adversarial mode challenges the agent's claims — forcing evidence, corrections, and acknowledged limitations — producing validated knowledge entries.
pip install bdistill
claude mcp add bdistill -- bdistill-mcp # Claude Code
/distill medical cardiology # Preset domain
/distill --custom kubernetes docker helm # Custom terms
/distill --adversarial medical # With adversarial validation
bdistill kb list # Show all domains
bdistill kb search "atrial fibrillation" # Keyword search
bdistill kb export -d medical -f csv # Export as spreadsheet
bdistill kb export -d medical -f markdown # Readable knowledge document
Structured reference JSONL — not training data:
{
"question": "What causes myocardial infarction?",
"answer": "Myocardial infarction results from acute coronary artery occlusion...",
"domain": "medical",
"category": "cardiology",
"tags": ["mechanistic", "evidence-based"],
"quality_score": 0.73,
"confidence": 1.08,
"validated": true,
"source_model": "Claude Sonnet 4"
}
Generate structured training data for traditional ML models:
/schema sepsis | hr:float, bp:float, temp:float, wbc:float | risk:category[low,moderate,high,critical]
Exports as CSV ready for pandas/sklearn. Each row tracks source_model for cross-model analysis.
For open-source models running locally:
# Install Ollama from https://ollama.com
ollama serve
ollama pull qwen3:4b
bdistill extract --domain medical --model qwen3:4b
@bdistill-behavioral-xray - X-ray a model's behavioral patterns