Meta-Pattern Recognition

Spot patterns appearing in 3+ domains to find universal principles

10 stars

Best use case

Meta-Pattern Recognition is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Spot patterns appearing in 3+ domains to find universal principles

Teams using Meta-Pattern Recognition should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/meta-pattern-recognition/SKILL.md --create-dirs "https://raw.githubusercontent.com/Blurjp/ImagePrepMCP/main/.claude/skills/superpowers-problem-solving/meta-pattern-recognition/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/meta-pattern-recognition/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How Meta-Pattern Recognition Compares

Feature / Agent	Meta-Pattern Recognition	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Spot patterns appearing in 3+ domains to find universal principles

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# 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

Related Skills

We are still matching the closest adjacent skills for this page. In the meantime, continue through the full directory.