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meta-pattern-recognition
Spot patterns appearing in 3+ domains to find universal principles
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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Spot patterns appearing in 3+ domains to find universal principles
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination
Use when creating or developing anything, before writing code or implementation plans - refines rough ideas into fully-formed designs through structured Socratic questioning, alternative exploration, and incremental validation
Force unrelated concepts together to discover emergent properties - "What if we treated X like Y?"
Use when tests have race conditions, timing dependencies, or inconsistent pass/fail behavior - replaces arbitrary timeouts with condition polling to wait for actual state changes, eliminating flaky tests from timing guesses
Use when invalid data causes failures deep in execution, requiring validation at multiple system layers - validates at every layer data passes through to make bugs structurally impossible
Use when partner provides a complete implementation plan to execute in controlled batches with review checkpoints - loads plan, reviews critically, executes tasks in batches, reports for review between batches
| name | Meta-Pattern Recognition |
| description | Spot patterns appearing in 3+ domains to find universal principles |
| when_to_use | when noticing the same pattern across 3+ different domains or experiencing déjà vu in problem-solving |
| version | 1.1.0 |
When the same pattern appears in 3+ domains, it's probably a universal principle worth extracting.
Core principle: Find patterns in how patterns emerge.
| 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 |
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)