bdistill-knowledge-extraction – Coding skill for AI agents

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 model-generated candidates for independent source review; adversarial self-correction is not external validation.

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

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

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:

{
  "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": 0.73,
  "validated": false,
  "source_model": "<actual host model recorded at run time>"
}

The numeric scores are illustrative, uncalibrated metadata; independent source verification is absent in this example.

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:

# 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
• In-session extraction uses the host provider and its data policy; it is not offline. A genuinely local Ollama endpoint keeps inference local, but downloading models/installing packages uses the network. Verify endpoints and approved data processing before use
• Output is reference data, not LLM training format

Related Skills

• @bdistill-behavioral-xray - X-ray a model's behavioral patterns

Limitations

• Generated medical, legal, financial or security content needs dated independent authoritative evidence and domain review; quality_score/confidence are not clinical/legal accuracy or calibrated probabilities. Do not put personal records into generated corpora or use synthetic labels as real patient outcomes.
• Installing MCP or downloading models is separate from extraction; use a reviewed environment and respect host/configuration authorization.
• Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
• Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.