persona
Consensus Persona Respawn
Replaces weak personas with successors learned from historical ledger mistakes to keep evaluator quality high.
Why persona lifecycle matters
A static evaluator panel degrades. consensus-persona-respawn keeps decision quality from plateauing by replacing low-performing personas with evidence-informed successors.
Respawn process
- detect dead/weak persona (threshold or explicit trigger)
- mine recent decision artifacts
- extract recurring mistake patterns
- generate successor persona profile
- write
persona_respawn+ updatedpersona_set
Reliability benefits
- anti-stagnation for long-running agent systems
- transparent mutation history
- explicit lineage between old and new evaluators
- better long-term arbitration quality
SKILL.md perspective
Simple trigger-based API with strict JSON and board-native writes.
AI-SELF-IMPROVEMENT perspective
This is evaluator maintenance: adaptation driven by observed failure, not random persona churn.
Source Documentation
For exact source documentation, open the raw markdown views below.