Agent Skills

Meta-Pattern Recognition

Spot patterns appearing in 3+ domains to find universal principles

Install

npx skills add https://github.com/obra/superpowers-skills --skill meta-pattern-recognition
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 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

researchmattpocock575KInvestigate a question against high-trust primary sources and capture the findings as a Markdown file in the repo. Use when the user wants a topic researched, docs or API facts gathered, or reading legwork delegated to a background agent.paper-context-resolverlllllllama451KRigor Paper Context helper for README-first deep learning repo reproduction. Use only when the README and repository files leave a narrow reproduction-critical gap and the task is to resolve a specific paper detail such as dataset split, preprocessing, evaluation protocol, checkpoint mapping, or runtime assumption from primary paper sources while recording conflicts. Do not use for general paper summary, repo scanning, environment setup, command execution, title-only paper lookup, or replacing Renv-and-assets-bootstraplllllllama450KRigor Setup skill for README-first deep learning repo reproduction. Use when the task is specifically to prepare a conservative conda-first environment, checkpoint and dataset path assumptions, cache location hints, and setup notes before any run on a README-documented repository. Do not use for repo scanning, full orchestration, paper interpretation, final run reporting, or generic environment setup that is not tied to a specific reproduction target.ai-research-explorelllllllama311KRigor Explore compatible skill slug for meaningful and potentially novel deep learning research candidates. Use when the researcher has chosen the task family, dataset, benchmark, evaluation method, provided SOTA references, and wants candidate-only exploration on top of `current_research` with auditable repo understanding, idea gating, fair comparison, and governed experiments written to `explore_outputs/`. Do not use for README-first trusted reproduction, open-ended direction finding, narrow c

Search skills and MCP servers

Fuzzy search across 23,137 skills and servers