pinren

pinren / morphism-mapper

Public

Category Theory Morphism Mapper - Cross-domain problem solving using category theory

25
1
100% credibility
Found Feb 06, 2026 at 19 stars -- GitGems finds repos before they trend. Get early access to the next one.
Sign Up Free
AI Analysis
Python
AI Summary

Morphism Mapper is a plugin for AI chat tools that maps the structure of user problems to concepts from diverse fields like physics, biology, and philosophy to generate innovative solutions.

How It Works

1
💡 Stuck on a tough problem?

You discover Morphism Mapper, a clever thinking tool that borrows wisdom from far-off fields like philosophy or biology to spark fresh ideas for your challenge.

2
📥 Grab the tool

You download the handy folder from the sharing site and prepare to add it to your AI chat helper.

3
🗂️ Add to your AI's toolbox

You place the folder in the special spot where your AI keeps its extra skills, like a drawer for helpful tricks.

4
🔄 Refresh your AI buddy

You restart your AI chat tool, and now it's equipped with this new way of thinking.

5
🗣️ Describe your dilemma

In your next chat, you simply share your problem, like 'How do I help a friend with tough feelings?' and it springs into action.

6
🌉 Witness cross-field magic

Your AI maps your issue's shape to surprising areas, pulling proven ideas from ecology or ancient wisdom to reveal hidden paths forward.

🚀 Breakthrough insights unlocked

You receive creative, eye-opening solutions that lift the fog, helping you tackle your original problem with renewed excitement and clarity.

Sign up to see the full architecture

5 more

Sign Up Free

Star Growth

See how this repo grew from 19 to 25 stars Sign Up Free
Repurpose This Repo

Repurpose is a Pro feature

Generate ready-to-use prompts for X threads, LinkedIn posts, blog posts, YouTube scripts, and more -- with full repo context baked in.

Unlock Repurpose
AI-Generated Review

What is morphism-mapper?

Morphism-mapper is a Python-based skill for Claude Code and OpenCode that applies category theory to map your problem's structure—entities, relationships, constraints—to distant domains like quantum mechanics or Zhuangzi philosophy, borrowing proven theorems for fresh solutions. Tell it a dilemma like "design ETF user retention," and it extracts the categorical skeleton, picks matching domains, and synthesizes actionable insights via cross-domain functors. It's category theory for programmers in practice, turning abstract math from Wikipedia or books like Category Theory for the Working Mathematician into a chat-activated tool for innovation.

Why is it gaining traction?

Unlike generic prompt libraries, it automates domain selection across 31 curated fields with dynamic morphism matching and commands like /morphism-map or /morphism-scale for Kan extensions on scaling strategies. Developers hook on the structured workflow: extract, map, synthesize, with custom domain adds for personal category theory pdf notes or github category tags. The pre-annotated knowledge base delivers non-obvious angles fast, standing out in the category encoder github space for real cross-domain breakthroughs.

Who should use this?

Strategists and product leads tackling wicked problems—user churn, market entry, team dynamics—in AI chats. Entrepreneurs blending military strategy with ecology for growth hacks, or indie devs applying game theory to distributed systems bugs. Pairs well with category theory in context readers seeking practical github category theory beyond memes.

Verdict

Try it if category theory sparks your curiosity; the detailed docs and MIT license make experimentation low-risk despite 22 stars and 1.0% credibility signaling early maturity. Solid for prototypes, but await more domains and tests before production reliance.

(198 words)

Sign up to read the full AI review Sign Up Free

Similar repos coming soon.