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Divergence Control
divergence-control
Keep multiple instances aligned while allowing productive variance
SKILL.md
Full skill instructions
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
- Problem Anchoring - All instances address the same core question
- Variance Measurement - How different ARE the outputs?
- Coherence Thresholds - How different is TOO different?
- Periodic Synchronization - "Check in, are we still on the same track?"
- 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
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