How does the 'Search Temperature' affect the results?

Temperature controls the breadth of the search: 0.0-0.3 focuses on the same or close domains for verification, while 0.7-1.0 searches distant domains like biology or music for universal parallels.

How does it distinguish between content and dynamics?

The skill abstracts the description to its 'structural dynamic'—for example, viewing a code review bottleneck not as 'code' but as 'adversarial feedback for error correction'—to find similar mechanics in other fields.

Can I use this to document new design patterns?

Absolutely. It is the exploration step that confirms if a pattern is novel and worth recording in your pattern library or if it should be merged with existing knowledge.

What is a 'vasana' in the context of this skill?

In this system, a vasana is a structural pattern that persists across unrelated contexts. This skill helps identify if an observation is a recurring vasana or a unique event.

Does this skill require specific MCPs to function?

Yes, it is designed to work alongside the relational-memory and edge-graph Model Context Protocols (MCP) to query existing patterns and navigate domain connections.

Find Similar: Cross-Domain Pattern Search

Name: Find Similar: Cross-Domain Pattern Search
Author: bogheorghiu

bybogheorghiu

0•

データサイエンスとML

Identifies and verifies recurring structural patterns across diverse domains to validate novelty and discover universal insights.

The Find Similar skill enables users to 'externalize cognition' by uncovering structural analogues of observed patterns across unrelated fields. By utilizing adjustable 'search temperatures,' it allows Claude to verify if a specific logic or behavior is a localized occurrence or part of a broader universal dynamic. This skill acts as a critical bridge in the Vasana system—moving from the initial observation of a pattern to its formal recording—helping developers and systems thinkers avoid reinventing existing concepts while discovering deep, cross-disciplinary connections that foster innovation.

主な機能

01Edge-Graph Exploration: Navigates weighted connections to find non-obvious parallels in adjacent or distant domains.

02Structural Isomorphism Identification: Focuses on the underlying dynamics and mechanics rather than surface-level content.

03Novelty Assessment: Evaluates matches based on structural similarity, generative insight, and testability.

04Variable Search Temperature: Adjust scope from same-domain verification (0.0) to abstract cross-domain exploration (1.0).

050 GitHub stars

06Relational Memory Integration: Automatically queries existing pattern libraries to prevent redundant documentation.

ユースケース

01Determining if a recurring team dynamic is a known systemic pattern or a unique, novel discovery.

02Expanding a narrow technical observation into a generalized principle by finding analogues in music or ecology.

03Verifying if a software architecture bottleneck has structural parallels in biological systems or urban planning.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add bogheorghiu/ex-cog find-similar

For use in Claude.ai and ChatGPT