---
id: gh-meta-pattern-recognition
name: "Meta-Pattern Recognition"
url: https://skills.yangsir.net/skill/gh-meta-pattern-recognition
author: obra
domain: ai-software-architecture-engineering
tags: ["meta-patterns", "pattern-recognition", "abstraction", "problem-solving", "architecture-design"]
install_count: 520
rating: 4.20 (120 reviews)
github: https://github.com/obra/superpowers-skills/tree/main/skills/problem-solving/meta-pattern-recognition
---

# Meta-Pattern Recognition

> 此技能通过识别跨越至少三个不同领域的重复模式，帮助用户提炼出普遍适用的核心原则。它使开发者、架构师或任何问题解决者能够将看似独立的挑战抽象化，从而发现并应用已验证的解决方案，避免重复劳动。该 Skill 显著提升了系统设计的优雅性、问题解决的效率及创新能力，促进了跨领域知识的复用与最佳实践的推广。

**Stats**: 520 installs · 4.2/5 (120 reviews)

## Before / After 对比

### 新问题解决方案设计时间

**Before**:

在没有这个技能之前，用户在面对新问题时，往往将其视为独特挑战，耗费大量时间从零开始设计解决方案，导致不同团队或项目间重复发明轮子，效率低下且难以推广通用最佳实践。

**After**:

该 Skill 赋能用户识别跨领域重复出现的模式，将其抽象为通用原则，从而快速应用成熟解决方案或设计更优雅的系统架构，显著缩短新问题解决时间，并促进知识复用与创新。

| Metric | Before | After | Change |
|---|---|---|---|
| 新问题解决方案设计时间 | 120分钟 | 30分钟 | -75% |

## Readme

# Meta-Pattern Recognition

## Overview

When the same pattern appears in 3+ domains, it's probably a universal principle worth extracting.

**Core principle:** Find patterns in how patterns emerge.

## Quick Reference

| Pattern Appears In | Abstract Form | Where Else? |
|-------------------|---------------|-------------|
| CPU/DB/HTTP/DNS caching | Store frequently-accessed data closer | LLM prompt caching, CDN |
| Layering (network/storage/compute) | Separate concerns into abstraction levels | Architecture, organization |
| Queuing (message/task/request) | Decouple producer from consumer with buffer | Event systems, async processing |
| Pooling (connection/thread/object) | Reuse expensive resources | Memory management, resource governance |

## Process

1. **Spot repetition** - See same shape in 3+ places
2. **Extract abstract form** - Describe independent of any domain
3. **Identify variations** - How does it adapt per domain?
4. **Check applicability** - Where else might this help?

## Example

**Pattern spotted:** Rate limiting in API throttling, traffic shaping, circuit breakers, admission control

**Abstract form:** Bound resource consumption to prevent exhaustion

**Variation points:** What resource, what limit, what happens when exceeded

**New application:** LLM token budgets (same pattern - prevent context window exhaustion)

## Red Flags You're Missing Meta-Patterns

- "This problem is unique" (probably not)
- Multiple teams independently solving "different" problems identically
- Reinventing wheels across domains
- "Haven't we done something like this?" (yes, find it)

## Remember

- 3+ domains = likely universal
- Abstract form reveals new applications
- Variations show adaptation points
- Universal patterns are battle-tested


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*Source: https://skills.yangsir.net/skill/gh-meta-pattern-recognition*
*Markdown mirror: https://skills.yangsir.net/api/skill/gh-meta-pattern-recognition/markdown*