crystallization

What problem does it solve?

Crystallization converts finished, purposeful work into a durable digest and reusable lessons so the same conclusions do not need to be rediscovered every session.

Core Features & Use Cases

• Distill completed work into a durable digest: extract a clear question, findings, entities, lessons, and open questions from a completed work chain.
• Extract and route reusable knowledge: send digest pages and semantic facts to agentic_kg_memory, and route reusable skill-contract deltas to skill-wiki.
• Manage lesson lifecycle with confidence and deduping: save lessons with initial confidence (0.6) and reinforce via a compounding rule, using fingerprint deduplication to avoid duplicates.
• Avoid crystallizing noise: gate on verified completeness and stable outcomes to prevent canonizing speculation.

Quick Start

Use crystallization to distill a completed research or debugging work chain into a question-driven digest and extracted lessons, then route the resulting artifacts to agentic_kg_memory and skill-wiki as appropriate.