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Meta-Pattern Recognition

by @obrav
4.2(120)

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。

meta-patternspattern-recognitionabstractionproblem-solvingarchitecture-designGitHub
安装方式
git clone https://github.com/obra/superpowers-skills.git
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Before / After 效果对比

1 组
使用前

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。

使用后

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。

SKILL.md

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 InAbstract FormWhere Else?
CPU/DB/HTTP/DNS cachingStore frequently-accessed data closerLLM prompt caching, CDN
Layering (network/storage/compute)Separate concerns into abstraction levelsArchitecture, organization
Queuing (message/task/request)Decouple producer from consumer with bufferEvent systems, async processing
Pooling (connection/thread/object)Reuse expensive resourcesMemory 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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统计数据

安装量520
评分4.2 / 5.0
版本
更新日期2026年6月19日
对比案例1 组

用户评分

4.2(120)
5
37%
4
43%
3
13%
2
5%
1
2%

为此 Skill 评分

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创建2026年6月19日
最后更新2026年6月19日
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