About

Imported from sickn33/agentic-awesome-skills (skills/bdistill-knowledge-extraction/SKILL.md). Install upstream with npx skills add sickn33/agentic-awesome-skills --skill bdistill-knowledge-extraction. Copyright stays with the author.

Knowledge Extraction

Name: bdistill-knowledge-extraction
Author: sickn33

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

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": 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:

# 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

Limitations

Use this skill only when the task clearly matches the scope described above.
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.

Use it

Copy one of these into your project. Installing also returns the manifest and these snippets.

yaml

targets:
  - https://api.opensmartroute.ai/api/v1/registry/sickn33-agentic-awesome-skills-bdistill-knowledge-extraction/manifest   # or paste the manifest below

Manifest

An Open Capability Manifest: the router reads it to know what this does, what it costs and when to pick it.

sickn33-agentic-awesome-skills-bdistill-knowledge-extraction.ocm.jsonjson

{
  "ocm": "1",
  "id": "sickn33-agentic-awesome-skills-bdistill-knowledge-extraction",
  "kind": "skill",
  "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.",
  "publisher": "sickn33",
  "version": "1.0.0",
  "capabilities": {
    "domains": [
      "coding"
    ],
    "tags": [
      "skill-md",
      "ai",
      "knowledge-extraction",
      "domain-specific",
      "data-moat",
      "mcp",
      "reference-data",
      "skills-sh"
    ],
    "languages": [
      "en"
    ]
  },
  "quality_prior": 0.6,
  "examples": [
    "Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed."
  ],
  "primary": false,
  "metadata": {
    "source": {
      "provider": "skills.sh",
      "repository": "https://github.com/sickn33/agentic-awesome-skills",
      "path": "skills/bdistill-knowledge-extraction/SKILL.md",
      "ref": "HEAD",
      "url": "https://github.com/sickn33/agentic-awesome-skills/blob/HEAD/skills/bdistill-knowledge-extraction/SKILL.md",
      "key": "sickn33/agentic-awesome-skills/skills/bdistill-knowledge-extraction/SKILL.md"
    }
  },
  "instructions": "# Knowledge Extraction\n\nExtract 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.\n\n## Overview\n\nbdistill 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.\n\nAdversarial mode challenges the agent's claims — forcing evidence, corrections, and acknowledged limitations — producing validated knowledge ent",
  "cost": {
    "context_tokens": 824
  }
}

Fetch it by URL: GET /api/v1/registry/sickn33-agentic-awesome-skills-bdistill-knowledge-extraction/manifest?version=1.0.0

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