How does the Darwin Gödel Machine integration work?

It uses recursive evolution loops that allow an agent to inspect, mutate, and verify its own source code while preserving functional fibrations and structural integrity.

Can this skill be used for standard refactoring?

Yes, it is highly effective for complex refactors where renaming or restructuring functions requires simultaneous, verified updates across a deep dependency tree.

What are GF(3) triadic operators?

GF(3) refers to a triadic logic (Generative +1, Neutral 0, Destructive -1) used to ensure that the sum of modifications remains conserved, preventing chaotic drift in the codebase.

What is covariant modification in AI development?

Covariant modification is a method of updating software where changes to a base component automatically trigger compatible, structure-preserving transformations in all dependent components.

Covariant Skill Evolution

Name: Covariant Skill Evolution
Author: plurigrid

byplurigrid

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Data Science & ML

Automates safe, structure-preserving self-modification for AI agents using covariant transport and Darwin Gödel Machine evolution loops.

About

The Covariant Modification skill provides a mathematically rigorous framework for Claude to evolve its own code and capabilities without introducing structural inconsistencies. By utilizing category theory concepts like fibrations and sheaves, the skill ensures that when one part of an agent is modified, all dependent skills are transformed covariantly to maintain system-wide integrity. It integrates the Darwin Gödel Machine for recursive improvement and MCP Tasks for managed self-rewriting, making it an essential tool for developers building autonomous, self-healing, or evolving AI systems that require formal safety guarantees during refactoring.

Key Features

Triadic modification operators (GF(3)) for structural conservation
MCP Tasks state machine for managed self-rewriting
Multi-agent sheaf gluing to ensure global consistency across modifications
2 GitHub stars
Covariant transport for synchronized dependency updates
Darwin Gödel Machine integration for recursive agent evolution

Use Cases

Building autonomous coding agents that safely evolve their own capabilities through feedback loops
Maintaining architectural consistency in large-scale, self-modifying software systems
Refactoring complex AI agent logic while automatically updating all dependent modules

About

Key Features

Triadic modification operators (GF(3)) for structural conservation
MCP Tasks state machine for managed self-rewriting
Multi-agent sheaf gluing to ensure global consistency across modifications
2 GitHub stars
Covariant transport for synchronized dependency updates
Darwin Gödel Machine integration for recursive agent evolution

Use Cases

Building autonomous coding agents that safely evolve their own capabilities through feedback loops
Maintaining architectural consistency in large-scale, self-modifying software systems
Refactoring complex AI agent logic while automatically updating all dependent modules