Pattern Synthesis

Purpose

When you run N instances, you get N outputs. Most will be different.

Pattern synthesis asks: What's the same across all of them?

What patterns persist even when the specific details change?

The Key Insight

Specific answer: "The answer is 42" Pattern: "The answer is always the answer when you ask the right question"

The pattern is more robust than the specific answer.

Core Pattern

Instance 1: X₁ ─┐
Instance 2: X₂ ─┼─→ Pattern Extractor
Instance 3: X₃ ─┤    (what's the same?)
Instance 4: X₄ ─┘

Result: "All outputs have property P"
        "All outputs follow rule R"
        "The common thread is C"

Key Features

1. Invariant Detection - What doesn't change?
2. Structure Extraction - What form is repeated?
3. Noise Filtering - What's signal vs. noise?
4. Meta-Pattern Recognition - Patterns about patterns
5. Generalization - From examples to principle

Implementation

See: .claude/skills/pattern-synthesis/pattern_extractor.py

Examples

Input: 4 different solutions to a problem Output: "All solutions follow this architectural pattern"

Input: 6 different explanations of a concept Output: "Core idea is X, the explanations just dress it differently"

Input: 10 different approaches to the same goal Output: "No matter the path, you have to pass through these 3 gates"

Payment Anchor

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