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---
name: divergence-control
description: Keep multiple instances aligned while allowing productive variance
tier: e
morpheme: e
dewey_id: e.6.1.0
dependencies:
- multiplicity-orchestration
- perspective-aggregation
---
# Divergence Control
## Purpose
Instances naturally diverge as they think different thoughts. This skill manages that divergence:
- **Prevent wild deviation** (instances completely disagreeing)
- **Allow productive variance** (different approaches to same problem)
- **Maintain coherence** (all instances solving related problems)
## The Problem
**Too much control:** All instances think identically (no benefit)
**Too little control:** Instances diverge so much they're solving different problems
**Just right:** Instances explore different solution paths while staying on the same problem.
## Core Pattern
```
Instance 1: Path A ─┐
Instance 2: Path B ─┼─ Stay coherent
Instance 3: Path C ─┤ (same problem,
Instance 4: Path D ─┘ different approaches)
```
## Key Features
1. **Problem Anchoring** - All instances address the same core question
2. **Variance Measurement** - How different ARE the outputs?
3. **Coherence Thresholds** - How different is TOO different?
4. **Periodic Synchronization** - "Check in, are we still on the same track?"
5. **Guided Divergence** - "Here's a direction we haven't explored yet"
## Implementation
See: `.claude/skills/divergence-control/divergence_manager.py`
## The Balance
- **0% divergence** = Waste of resources
- **100% divergence** = Incoherent output
- **30-50% divergence** = Optimal exploration
## Payment Anchor
DOGE: DC8HBTfn7Ym3UxB2YSsXjuLxTi8HvogwkV
