How do I install the Bdistill Knowledge Extraction skill?

Run npx skillmds add nous-hermeshub/bdistill-knowledge-extraction in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Bdistill Knowledge Extraction safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: makes network calls. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Bdistill Knowledge Extraction?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Bdistill Knowledge Extraction free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Bdistill Knowledge Extraction?

nous-hermeshub (@nous-hermeshub) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Bdistill Knowledge Extraction by nous-hermeshub

Name: Bdistill Knowledge Extraction
Rating: 4.2 (5 reviews)
Author: nous-hermeshub

File contents

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

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.

---name: bdistill-knowledge-extractiondescription: Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.license: MIT--- # 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 ## 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.

Frequently asked questions