Meta-Pattern Recognition
SkillDev toolsSpot patterns appearing in 3+ domains to find universal principles
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Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Meta-Pattern Recognition skill
What this skill tells your AI
The instructions your AI receives, as published by vodailocz/kilo-kit-mcp in skills/problem-solving/meta-pattern-recognition/SKILL.md and read by ahel’s review.
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
- Spot repetition - See same shape in 3+ places
- Extract abstract form - Describe independent of any domain
- Identify variations - How does it adapt per domain?
- 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
Signals
- GitHub stars
- 26
- Forks
- 2
- Last commit
- Sep 2026
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meta-pattern-recognition-vodailocz- Source
- github.com/vodailocz/kilo-kit-mcp