What are the primary benefits of using this skill?

It allows for efficient conversion between human-readable S-expressions and complex Julia ACSets, while providing a powerful 'Specter-style' API for querying and updating that data.

How does the performance optimization work?

It uses inline caching and Continuation-Passing Style (CPS) to avoid the overhead of repeated compilation or interpretation during data traversal.

Can this be used for general Lisp manipulation?

Yes, it includes specialized navigators for S-expression heads, tails, and children, making it useful for general Lisp-style data processing in Julia.

What is an ACSet in the context of this skill?

An ACSet (Attributed C-Set) is a data structure used in the AlgebraicJulia ecosystem to represent complex relational data, often used for scientific modeling and category theory applications.

Lisp-style ACSet Navigator

Name: Lisp-style ACSet Navigator
Author: plurigrid

byplurigrid

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Data Science & ML

Provides a bidirectional bridge between S-expressions and Algebraic Julia data structures with high-performance navigation and transformation.

About

This skill enables Claude to work with complex graph-based data structures in Julia by bridging LispSyntax.jl and ACSets.jl. It implements patterns from OCaml's ppx_sexp_conv for serialization and Clojure's Specter for powerful data navigation, allowing for seamless selection and in-place transformation of hierarchical data. Using inline caching and Continuation-Passing Style (CPS), it offers near-hand-written performance for manipulating scientific data schemas and category theory-based models within the AlgebraicJulia ecosystem.

Key Features

2 GitHub stars
Specter-style path navigation for selection and transformation
Integration with AlgebraicJulia schemas and morphisms
Recursive S-expression walking and manipulation
High-performance inline caching with Continuation-Passing Style (CPS)
Bidirectional S-expression to ACSet conversion

Use Cases

Navigating and modifying large hierarchical data structures with minimal performance overhead
Applying complex, nested data transformations to Category Theory-based schemas
Converting scientific graph data between S-expressions and Julia ACSet objects

About

Key Features

2 GitHub stars
Specter-style path navigation for selection and transformation
Integration with AlgebraicJulia schemas and morphisms
Recursive S-expression walking and manipulation
High-performance inline caching with Continuation-Passing Style (CPS)
Bidirectional S-expression to ACSet conversion

Use Cases

Navigating and modifying large hierarchical data structures with minimal performance overhead
Applying complex, nested data transformations to Category Theory-based schemas
Converting scientific graph data between S-expressions and Julia ACSet objects