What does the 'Ergodic' trit (0) signify?

In this system, a trit of 0 (Ergodic) represents a balanced state of mutual awareness where an agent mediates between being an observer and being observed.

Can this skill be used for agent trajectory prediction?

Yes, it integrates with bisimulation games to predict future actions by ensuring that predicted moves maintain GF(3) conservation and sheaf consistency.

What mathematical theories does this skill utilize?

The skill is built on sheaf theory for observation consistency, ACSet schemas for categorical data representation, and GF(3) (Galois Field 3) for balancing agent roles.

How does this skill help with GitHub repository analysis?

It maps contributor interactions as an 'interactome,' allowing you to visualize how authors are aware of each other's work and identify the structural consistency of the project's social graph.

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

A backlink represents a mutual awareness edge within an awareness graph, signifying that two agents or entities are observing each other bidirectionally and consistently.

Mutual Awareness Graph Modeler

Name: Mutual Awareness Graph Modeler
Author: plurigrid

byplurigrid

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Data Science & ML

Maps and analyzes bidirectional observation relationships between agents using sheaf-theoretic consistency and multi-agent awareness graphs.

This skill provides a specialized framework for modeling mutual awareness and intersubjectivity within multi-agent systems and repository contributor networks. Leveraging advanced mathematical foundations like sheaf theory, Julia-native ACSet schemas, and Hamkins multiverse potentialism, it allows developers to analyze the 'interactome' of a project, predict agent trajectories via bisimulation games, and ensure consistency in complex observation relationships. It is particularly valuable for researchers and developers working on collaborative workflows, multi-agent coordination, and high-level behavioral modeling in software ecosystems.

Key Features

01Bidirectional observation mapping via structured awareness graphs

02GF(3) conservation modeling for balancing agent roles and triads

03Modal logic support for identifying necessarily and possibly aware states

04Sheaf-theoretic consistency checking using Bumpus FPT algorithms

05GitHub interactome integration for contributor relationship analysis

068 GitHub stars

Use Cases

01Predicting multi-agent trajectories in complex systems using bisimulation games

02Analyzing GitHub contributor networks to identify mutual awareness and collaboration patterns

03Modeling subjective observation boundaries and intersubjectivity in theoretical AI research

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi mutual-awareness-backlink

For use in Claude.ai and ChatGPT

Download Skill