pinkpixel-dev/bdistill-knowledge-extraction
SKILLS/bdistill-knowledge-extraction/SKILL.md
Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.
$ install --global
skillsauthnpx skillsauth add pinkpixel-dev/skills-collection-1 bdistill-knowledge-extractionInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
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TrivyContainer and dependency vulnerability scanner
95%
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SemgrepStatic code analysis for vulnerabilities
95%
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mcp-scan (Snyk)Model Context Protocol security validation
95%
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Snyk (dep)Open source security scanning
50%
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Socket.devSupply chain security analysis
50%
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VirusTotalMulti-engine malware detection
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CrowdStrikeAdvanced threat intelligence
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OWASP Dep-Check
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Last scanned: Apr 13, 2026, 5:06 AM80.0s1 file scanned
SKILL.md
- 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
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
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