What does 'dynamic sufficiency gating' mean?

It is a safety mechanism that prevents the generation of artifacts unless the underlying discussion provides enough context, verified via ε-machine coverage scores.

What is the primary source of knowledge for zulip-cogen?

It utilizes a curated archive of over 121,000 messages from Category Theory Zulip discussions to find context for code generation.

Can I generate diagrams with this skill?

Yes, it can transform structural descriptions into Mermaid or tikz-cd diagrams suitable for documentation or academic papers.

How do I target a specific topic for generation?

You can use the --pond flag to specify a specific stream or topic cluster, such as 'theory:category-theory' or 'theory:topos-theory'.

Which programming languages are supported for implementations?

The skill primarily generates code for Julia and Python, with a specific focus on Category Theory-related libraries like AlgebraicJulia.

Zulip Category Theory Code Generator

Name: Zulip Category Theory Code Generator
Author: plurigrid

byplurigrid

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Aprendizaje y Documentación

Generates formal proofs, diagrams, and implementations from Category Theory Zulip discussions using context-aware gating.

Acerca de

The zulip-cogen skill bridges the gap between high-level mathematical discussions and executable code by mining the Category Theory Zulip archive. Leveraging a database of over 121,000 messages, it uses ε-machine coverage to ensure generation only occurs when sufficient context is available. It is particularly useful for researchers and developers working with Lean4, AlgebraicJulia, or formal category theory, providing a pipeline to transform informal chat threads into rigorous theorems, Mermaid diagrams, or functional code implementations. By integrating with 'ponds' of knowledge, it ensures that every generated artifact is backed by community-verified witnesses.

Características Principales

Deterministic Artifact Styling: Uses hash-based coloring for consistent visual representation of generated code.
Lean4 Theorem Generation: Converts math discussions into formal proofs.
Dynamic Sufficiency Gating: Employs ε-machine coverage to ensure context sufficiency before synthesis.
Pond-based Retrieval: Filters knowledge extraction by specific topical 'ponds' or Zulip streams.
Multi-format Output: Supports Mermaid diagrams, Julia implementations, and ACSet schemas.
2 GitHub stars

Casos de Uso

Converting informal Category Theory chat threads into formal Lean4 code for mathematical verification.
Generating architectural Mermaid diagrams from structural descriptions found in Zulip archives.
Prototyping mathematical algorithms in Julia based on community-discussed Kan extensions or limits.

Acerca de

Características Principales

Deterministic Artifact Styling: Uses hash-based coloring for consistent visual representation of generated code.
Lean4 Theorem Generation: Converts math discussions into formal proofs.
Dynamic Sufficiency Gating: Employs ε-machine coverage to ensure context sufficiency before synthesis.
Pond-based Retrieval: Filters knowledge extraction by specific topical 'ponds' or Zulip streams.
Multi-format Output: Supports Mermaid diagrams, Julia implementations, and ACSet schemas.
2 GitHub stars

Casos de Uso

Converting informal Category Theory chat threads into formal Lean4 code for mathematical verification.
Generating architectural Mermaid diagrams from structural descriptions found in Zulip archives.
Prototyping mathematical algorithms in Julia based on community-discussed Kan extensions or limits.