Which theoretical perspectives are included in this skill?

The skill synthesizes the work of Emily Riehl (Category Theory), Ilya Sutskever (Compression/Scaling), Jürgen Schmidhuber (Curiosity/Self-Improvement), and Yoshua Bengio (Causal Inference/GFlowNets).

How does this help with software development?

It provides a rigorous mathematical and information-theoretic foundation for system design, leading to more robust, efficient, and provably sound architectural choices.

What is GF(3) conservation in this skill?

GF(3) conservation is a balancing mechanism used within the skill to ensure that various cognitive superpositions maintain logical consistency and 'trit balance' during the reasoning process.

What is Cognitive Superposition in the context of Claude Code?

It is a meta-skill that allows Claude to maintain multiple high-level theoretical frameworks simultaneously, only 'collapsing' to a specific method when a task context requires a concrete solution.

Cognitive Superposition

Name: Cognitive Superposition
Author: plurigrid

byplurigrid

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数据科学与机器学习

Synthesizes multiple advanced theoretical frameworks from world-class researchers to solve complex problems through high-level meta-reasoning.

关于

Cognitive Superposition is an advanced meta-skill designed for high-level AI research, architectural design, and complex problem-solving. It enables Claude to simultaneously hold and integrate disparate expert perspectives—ranging from Emily Riehl’s synthetic ∞-categories and Ilya Sutskever’s compression-based intelligence to Jürgen Schmidhuber’s curiosity-driven learning and Yoshua Bengio’s causal GFlowNets. By applying principles like sheaf coherence and GF(3) conservation, the skill allows for 'measurement collapse' where theoretical abstractions are transformed into specific, actionable implementation patterns exactly when needed.

主要功能

Intrinsic motivation frameworks via curiosity-driven learning and Gödel machines
Compression-focused intelligence metrics based on Kolmogorov complexity
Diverse solution space exploration using Generative Flow Networks (GFlowNets)
2 GitHub stars
Multi-perspective synthesis of top-tier AI and mathematical research
Category-theoretic reasoning using synthetic ∞-categories and Rzk-style types

使用场景

Developing self-improving code logic based on formal proofs and compression progress.
Solving multi-dimensional engineering problems that lack a single standard framework.
Designing novel AI architectures that require bridging category theory with machine learning.

关于

主要功能

Intrinsic motivation frameworks via curiosity-driven learning and Gödel machines
Compression-focused intelligence metrics based on Kolmogorov complexity
Diverse solution space exploration using Generative Flow Networks (GFlowNets)
2 GitHub stars
Multi-perspective synthesis of top-tier AI and mathematical research
Category-theoretic reasoning using synthetic ∞-categories and Rzk-style types

使用场景

Developing self-improving code logic based on formal proofs and compression progress.
Solving multi-dimensional engineering problems that lack a single standard framework.
Designing novel AI architectures that require bridging category theory with machine learning.