Extracts structured, quality-scored domain knowledge from in-session AI models or local Ollama. Builds searchable/exportable datasets for domains like medical, finance, or cybersecurity.
From antigravity-awesome-skillsnpx claudepluginhub sickn33/antigravity-awesome-skills --plugin antigravity-awesome-skillsThis skill uses the workspace's default tool permissions.
Designs and optimizes AI agent action spaces, tool definitions, observation formats, error recovery, and context for higher task completion rates.
Enables AI agents to execute x402 payments with per-task budgets, spending controls, and non-custodial wallets via MCP tools. Use when agents pay for APIs, services, or other agents.
Compares coding agents like Claude Code and Aider on custom YAML-defined codebase tasks using git worktrees, measuring pass rate, cost, time, and consistency.
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.
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.
pip install bdistill
claude mcp add bdistill -- bdistill-mcp # Claude Code
/distill medical cardiology # Preset domain
/distill --custom kubernetes docker helm # Custom terms
/distill --adversarial medical # With adversarial validation
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
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"
}
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.
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
@bdistill-behavioral-xray - X-ray a model's behavioral patterns