Which programming frameworks are supported?

The skill provides patterns for Julia (via ACSets.jl), Ruby, and OCaml-style S-expression bridges for algebraic data structures.

Can I use this for sheaf neural network coordination?

Yes, it specifically bridges analytic stacks to cellular sheaves, using the Laplacian to measure failures of local-to-global consistency in neural architectures.

How does this skill handle Liquid Vector Spaces?

The skill includes implementation patterns for liquid norms and solid completions, allowing Claude to model convergent sequences and analytic ring structures for Huber pairs.

What is the primary benefit of the Condensed Analytic Stacks skill?

It provides a rigorous mathematical bridge between high-level condensed mathematics (Scholze-Clausen) and practical computational structures like sheaf neural networks, enabling more robust geometric reasoning in AI models.

Condensed Analytic Stacks

Name: Condensed Analytic Stacks
Author: plurigrid

byplurigrid

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데이터 과학 및 ML

Bridges Scholze-Clausen condensed mathematics and analytic stacks to sheaf neural networks via 6-functor formalisms.

The Condensed Analytic Stacks skill provides a specialized framework for integrating advanced topological concepts—such as condensed sets, liquid vector spaces, and pyknotic objects—into computational learning systems. By leveraging 6-functor formalisms and categorical Künneth formulas, it allows Claude to model complex analytic stacks and bridge them to cellular sheaf neural networks. This skill is ideal for researchers and developers working on geometric deep learning, algebraic databases, and high-level categorical modeling where traditional topology fails to provide necessary algebraic completions.

주요 기능

01Verification tools for descent conditions and categorical Künneth formulas

02Bridging condensed/pyknotic structures to cellular sheaf Laplacian coordination

03ACSet schema generation for algebraic databases of condensed structures

04Modeling 6-functor formalisms (f_*, f^*, f_!, f^!, Hom, ⊗) for analytic stacks

05Implementation of Liquid and Solid vector space completions for r-liquid norms

062 GitHub stars

사용 사례

01Formalizing abstract algebraic geometry concepts into executable Julia or Ruby code

02Designing geometric deep learning models that utilize sheaf diffusion and harmonic inference

03Implementing liquid-weighted consensus algorithms for decentralized agent coordination

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi condensed-analytic-stacks

For use in Claude.ai and ChatGPT

Download Skill