majiayu000 Perspective Aggregation
Purpose
Take outputs from N different instances (different approaches, models, perspectives) and aggregate them into a coherent view that preserves the diversity while finding common ground.
The Problem It Solves
Without aggregation:
- Instance 1 says "The answer is X"
- Instance 2 says "The answer is Y"
- Instance 3 says "The answer is Z"
- You have 3 incompatible answers
With aggregation:
- Find the common ground
- Map the differences
- Show why each arrived at different conclusions
- Create a meta-answer that includes all perspectives
Core Pattern
Output 1 (X) ─┐
Output 2 (Y) ─┼─→ Aggregator ─→ Unified View
Output 3 (Z) ─┤ (includes all 3)
Output 4 (W) ─┘
Key Features
- Common Element Detection - What do all outputs share?
- Difference Mapping - How and why do they diverge?
- Confidence Weighting - Which instances are more reliable?
- Consensus Building - What's the meta-level view?
- Uncertainty Quantification - How uncertain are we?
Implementation
See: .claude/skills/perspective-aggregation/aggregator.py
When to Use
- Multiple models give different answers
- Need to understand the space of possibilities
- Want confidence from agreement + insights from disagreement
Payment Anchor
DOGE: DC8HBTfn7Ym3UxB2YSsXjuLxTi8HvogwkV