bdistill-knowledge-extraction — Автоматизированное извлечение знаний из AI моделей

--- name: bdistill-knowledge-extraction description: "Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed." category: ai-research risk: safe source: community date_added: "2026-03-20" author: FrancyJGLisboa tags: [ai, knowledge-extraction, domain-specific, data-moat, mcp, reference-data] tools: [claude, cursor, codex, copilot] --- # Knowledge Extraction 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. ## Overview 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. ## When to Use This Skill - Use when you need structured reference data on any domain (medical, legal, finance, cybersecurity) - Use when building lookup tables, Q&A datasets, or research corpora - Use when generating training data for traditional ML models (regression, classification — NOT competing LLMs) - Use when you want cross-model comparison on domain knowledge ## How It Works ### Step 1: Install ```bash pip install bdistill claude mcp add bdistill -- bdistill-mcp # Claude Code ``` ### Step 2: Extract knowledge in-session ``` /distill medical cardiology # Preset domain /distill --custom kubernetes docker helm # Custom terms /distill --adversarial medical # With adversarial validation ``` ### Step 3: Search, export, compound ```bash 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 ``` ## Output Format Structured reference JSONL — not training data: ```json { "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" } ``` ## Tabular ML Data Generation 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. ## Local Model Extraction (Ollama) For open-source models running locally: ```bash # Install Ollama from https://ollama.com ollama serve ollama pull qwen3:4b bdistill extract --domain medical --model qwen3:4b ``` ## Security & Safety Notes - In-session extraction uses your existing subscription — no additional API keys - Local extraction runs entirely on your machine via Ollama - No data is sent to external services - Output is reference data, not LLM training format ## Related Skills - `@bdistill-behavioral-xray` - X-ray a model's behavioral patterns