How does this skill improve AI reliability?

It uses mathematical conservation principles (GF(3)) to ensure that state changes are balanced and coherent, preventing the 'drift' or loss of context common in long LLM sessions.

Can this be used for database management?

Yes, it specifically integrates with tools like duckdb-timetravel and nix-acset to provide persistent, time-aware storage for complex data structures.

What is the role of 'Trit' in this skill?

Trits represent the state of the system in a triadic logic (-1, 0, +1), representing Memory, Remembering, and Worlding respectively, ensuring their sum always equals zero for system stability.

What is an autopoietic loop in this context?

It is a self-producing system where the output of the generation process (Worlding) is fed back into storage (Memory), which in turn informs the next cycle of retrieval (Remembering).

Autopoietic Memory & Worlding

Name: Autopoietic Memory & Worlding
Author: plurigrid

byplurigrid

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Data Science & ML

Implements a recursive, autopoietic loop for state management that synchronizes memory storage, pattern-matching recall, and generative world-building.

The World-Memory-Worlding skill provides a sophisticated cognitive framework for Claude to maintain long-term coherence through a self-referential 'strange loop.' By integrating Maturana-Varela's autopoiesis with GF(3) conservation logic, it treats memory persistence, contextual remembering, and generative output as a single, closed-loop system. This prevents common AI issues like sterile data retrieval or chaotic generation by ensuring that every world state generated is informed by previous memory and subsequently becomes part of the system's future knowledge base, making it ideal for complex simulations and autonomous agent architectures.

Key Features

012 GitHub stars

02Isomorphism tracking between memory traces and generative structures

03GF(3) triadic conservation for persistent state consistency

04Strange Loop logic (ι∘ι = id) for self-referential system closure

05Autopoietic loop integration (Memory → Remembering → Worlding)

06Pattern-matching recall for high-relevance context reconstruction

Use Cases

01Maintaining long-term coherence in complex, multi-stage AI simulation environments

02Implementing self-correcting state machines for autonomous coding agents

03Managing evolving technical knowledge bases where historical context must bound future generation

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi world-memory-worlding

For use in Claude.ai and ChatGPT

Download Skill