bdistill-knowledge-extraction
Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.
Best use case
bdistill-knowledge-extraction is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.
Teams using bdistill-knowledge-extraction should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/bdistill-knowledge-extraction/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How bdistill-knowledge-extraction Compares
| Feature / Agent | bdistill-knowledge-extraction | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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.
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