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.