acsets-hatchery
Attributed C-Sets as algebraic databases. Category-theoretic data structures generalizing graphs and dataframes with Gay.jl color integration.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/acsets-hatchery/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How acsets-hatchery Compares
| Feature / Agent | acsets-hatchery | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/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) Triads
```
algebraic-rewriting (-1) ⊗ acsets-hatchery (0) ⊗ gay-monte-carlo (+1) = 0 ✓
catcolab-ologs (-1) ⊗ acsets-hatchery (0) ⊗ catcolab-schemas (+1) = 0 ✓
catcolab-decapodes (-1) ⊗ acsets-hatchery (0) ⊗ catcolab-petri-nets (+1) = 0 ✓
tasks-acset (-1) ⊗ acsets-hatchery (0) ⊗ calendar-acset (+1) = 0 ✓
```
## Mixing-Optimal Cross-Layer Bridges
### catcolab-stock-flow (+1) [MIXING]
```julia
# Stock-flow models as ACSet instances
@acset StockFlow(SchStockFlow) begin
Stock = [:S, :I, :R]; Flow = 2
end
```
### catcolab-petri-nets (+1) [MIXING]
```julia
# Petri nets via AlgebraicPetri.jl
@acset LabelledPetriNet(SchPetri) begin
S = [:idle, :active]; T = [:start, :stop]
end
```
### gmail-anima (0) [MIXING]
```julia
# Email threads as ACSet graph
@acset ThreadGraph(SchThread) begin
Message = 5; reply_to = [nothing, 1, 1, 2, 3]
end
```
### catcolab-ologs (-1) [MIXING]
```julia
# Ologs as the conceptual foundation
# Every ACSet schema IS an olog instance
```
## 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 annotationsRelated Skills
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