Best use case
narya-hatchery is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Narya Hatchery
Teams using narya-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/narya-hatchery/SKILL.md --create-dirs "https://raw.githubusercontent.com/plurigrid/asi/main/plugins/asi/skills/narya-hatchery/SKILL.md"
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/narya-hatchery/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How narya-hatchery Compares
| Feature / Agent | narya-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?
Narya Hatchery
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
# Narya Hatchery
---
name: narya-hatchery
description: Higher-dimensional type theory proof assistant with observational Id/Bridge types, parametricity, and ProofGeneral integration.
trit: 0
color: "#3A71C0"
---
## Overview
**Narya** is a proof assistant implementing Multi-Modal, Multi-Directional, Higher/Parametric/Displayed Observational Type Theory.
## Core Features
- **Normalization-by-evaluation** algorithm and typechecker
- **Observational-style theory** with Id/Bridge types satisfying parametricity
- **Variable arity and internality** for bridge types
- **User-definable mixfix notations**
- **Record types, inductive datatypes, coinductive codatatypes**
- **Matching and comatching case trees**
- **Import/export and separate compilation**
- **Typed holes** with later solving
- **ProofGeneral interaction mode**
## Type Theory Features
### Bridge Types with Parametricity
```narya
-- Observational identity via bridges
bridge : (A : Type) → (x y : A) → Bridge x y → x ≡ y
```
### Higher-Dimensional Structure
Narya supports higher-dimensional type theory where:
- Types can have internal dimensions
- Parametricity is built into the type theory
- Bridge types generalize equality
## Gay.jl Integration
```julia
# Initialize with Narya's chromatic seed
gay_seed!(0xbfe738ce2e1c5f1f)
# P3 extension gamut learning
function loss(params, seed, target_gamut=:p3_extension)
color = forward_color(params, projection, seed)
return out_of_gamut_distance(color, target_gamut)
end
```
## Installation
```bash
# From source
git clone https://github.com/mikeshulman/narya
cd narya
dune build
```
## Documentation
- [Installation Guide](https://narya.readthedocs.io/en/latest/installation.html)
- [Full Documentation](https://narya.readthedocs.io/en/latest/)
- [Contributing](https://narya.readthedocs.io/en/latest/contributing.html)
## Repository
- **Source**: TeglonLabs/narya (fork of mikeshulman/narya)
- **Seed**: `0xbfe738ce2e1c5f1f`
- **Index**: 49/1055
- **Color**: #d6621c
## GF(3) Triad
```
proofgeneral-narya (-1) ⊗ narya-hatchery (0) ⊗ gay-mcp (+1) = 0 ✓
```
## Related Skills
- `proofgeneral-narya` - Emacs integration
- `holes` - Interactive proof development
- `move-narya-bridge` - Move contract verificationRelated Skills
We are still matching the closest adjacent skills for this page. In the meantime, continue through the full directory.