acsets-hatchery

Attributed C-Sets as algebraic databases. Category-theoretic data structures generalizing graphs and dataframes with Gay.jl color integration.

181 stars

Best use case

acsets-hatchery is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Attributed C-Sets as algebraic databases. Category-theoretic data structures generalizing graphs and dataframes with Gay.jl color integration.

Teams using acsets-hatchery should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

  • You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

  • You only need a quick one-off answer and do not need a reusable workflow.
  • You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/acsets-hatchery/SKILL.md --create-dirs "https://raw.githubusercontent.com/majiayu000/claude-skill-registry/main/skills/data/acsets-hatchery/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/acsets-hatchery/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How acsets-hatchery Compares

Feature / Agentacsets-hatcheryStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Attributed C-Sets as algebraic databases. Category-theoretic data structures generalizing graphs and dataframes with Gay.jl color integration.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# ACSets Hatchery

## Overview

**ACSets.jl** provides acsets ("attributed C-sets") - data structures generalizing both graphs and data frames. They are an efficient in-memory implementation of category-theoretic relational databases.

## Core Features

- **Acset schemas** - Category-theoretic data structure definitions
- **Acsets** - Instances of schemas (like database rows)
- **Tabular columns** - Efficient columnar storage
- **Serialization** - JSON/binary format support

## What Are ACSets?

An ACSet is a functor from a category C to Set, with attributes. This means:
- **Objects** become tables
- **Morphisms** become foreign keys
- **Attributes** add data types to objects

## Usage

```julia
using ACSets

# Define a schema
@present SchGraph(FreeSchema) begin
    V::Ob
    E::Ob
    src::Hom(E, V)
    tgt::Hom(E, V)
end

# Create an acset
g = @acset Graph begin
    V = 3
    E = 2
    src = [1, 2]
    tgt = [2, 3]
end
```

## Extensions

- **Catlab.jl** - Homomorphisms, limits/colimits, functorial data migration
- **AlgebraicRewriting.jl** - DPO/SPO/SqPO rewriting for acsets

## Learning Resources

1. [Graphs and C-sets I](https://blog.algebraicjulia.org/post/2020/09/cset-graphs-1/) - What is a graph?
2. [Graphs and C-sets II](https://blog.algebraicjulia.org/post/2020/09/cset-graphs-2/) - Half-edges and rotation systems
3. [Graphs and C-sets III](https://blog.algebraicjulia.org/post/2021/04/cset-graphs-3/) - Reflexive graphs and homomorphisms
4. [Graphs and C-sets IV](https://blog.algebraicjulia.org/post/2021/09/cset-graphs-4/) - Propositional logic of subgraphs

## Gay.jl Integration

```julia
# Rec2020 wide gamut with acset seed
gay_seed!(0xb4545686b9115a09)

# Mixed mode checkpointing
params = OkhslParameters()
∂params = Enzyme.gradient(Reverse, loss, params, seed)
```

## Citation

> Patterson, Lynch, Fairbanks. Categorical data structures for technical computing. *Compositionality* 4, 5 (2022). [arXiv:2106.04703](https://arxiv.org/abs/2106.04703)

## Repository

- **Source**: plurigrid/ACSets.jl (fork of AlgebraicJulia/ACSets.jl)
- **Seed**: `0xb4545686b9115a09`
- **Index**: 494/1055
- **Color**: #204677

## GF(3) Triad

```
algebraic-rewriting (-1) ⊗ acsets-hatchery (0) ⊗ gay-monte-carlo (+1) = 0 ✓
```

## Related Skills

- `acsets-algebraic-databases` - Full ACSet guide
- `specter-acset` - Bidirectional navigation
- `world-a` - AlgebraicJulia ecosystem

## Forward Reference

- unified-reafference (ACSet schema consumer)


## Patterns That Work

- Schema-first database design
- Morphism-based foreign keys
- Integration with unified-reafference

## Patterns to Avoid

- Ad-hoc schema changes
- Missing attribute type annotations

## SDF Interleaving

This skill connects to **Software Design for Flexibility** (Hanson & Sussman, 2021):

### Primary Chapter: 3. Variations on an Arithmetic Theme

**Concepts**: generic arithmetic, coercion, symbolic, numeric

### GF(3) Balanced Triad

```
acsets-hatchery (+) + SDF.Ch3 (○) + [balancer] (−) = 0
```

**Skill Trit**: 1 (PLUS - generation)

### Secondary Chapters

- Ch4: Pattern Matching
- Ch7: Propagators
- Ch10: Adventure Game Example

### Connection Pattern

Generic arithmetic crosses type boundaries. This skill handles heterogeneous data.

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