distill

Extract and distill patterns from Oracle brain memory into structured summaries.

9|9|Updated Jan 16, 2026

Search Tags:#logging #patterns #memory #distill #autonomy #data-analysis #topic-detection

Ready to run

npx skills add https://github.com/Soul-Brews-Studio/opensource-nat-brain-oracle --skill distill-soul-brews-studio

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: distill
Source: https://github.com/Soul-Brews-Studio/opensource-nat-brain-oracle/tree/main/.claude/skills/distill
Command: npx skills add https://github.com/Soul-Brews-Studio/opensource-nat-brain-oracle --skill distill-soul-brews-studio

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill enables autonomous extraction and distillation of patterns from an Oracle brain, compressing vast memory into actionable insights while preserving the original signal and provenance.

Core Features & Use Cases

Autonomous pattern distillation: scans retrospectives, learnings, resonance, seeds, and logs to produce concise, structured distillations without human input.
Topic auto-detection & multi-topic support: automatically identifies topics with new signals and can run per-topic or swarm-mode operations for breadth.
End-to-end writing with hierarchy: employs Haiku for gathering, Sonnet/Opus for writing, and supports L1-L4 distillation levels with provenance and logging.

Quick Start

Run /distill to start an autonomous distillation cycle on the current brain data.

Frequently Asked Questions about distill

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract patterns from logs and memory without manual review?▼

You can automate pattern extraction from logs and memory by running a distillation cycle that reads previous outputs, auto-detects topics, and produces structured summaries autonomously. It compresses retrospectives and learnings into concise outputs without human input.

How does topic detection work during data distillation?▼

Topic detection during data distillation works by automatically scanning memory logs to identify new signals within retrospectives and learnings. It then generates structured distillations per topic or uses swarm-mode for broader parallel operations.

Can I run parallel agents for memory distillation across multiple topics?▼

Yes, you can run parallel agents for memory distillation by using swarm-mode operations. This allows multiple agents to process different topics simultaneously, extracting patterns from the Oracle brain and logging structured outputs without human intervention.

What is the best way to compress vast memory into actionable summaries while keeping provenance?▼

The best way to compress vast memory into actionable summaries while keeping provenance is using an autonomous distillation workflow. It applies L1-L4 distillation levels to logs and learnings, writing structured outputs that preserve the original signal.

Do I need any dependencies to run autonomous pattern extraction on my data?▼

No dependencies are required to run autonomous pattern extraction on your data. The skill operates independently to read previous distillations, auto-detect topics, and log structured outputs to the Oracle MCP using Sonnet for writing.

Why use different models like Haiku and Sonnet for logging and pattern distillation?▼

Using different models like Haiku and Sonnet for logging and pattern distillation optimizes the workflow: Haiku efficiently gathers data from logs, while Sonnet handles the complex writing of structured distillations, ensuring high-quality autonomous memory compression.