What are bridge types in Narya?

Bridge types are a feature of observational type theory that generalize equality and satisfy parametricity, allowing for more flexible internal logic in proofs.

Is there integration with other programming languages?

Yes, Narya provides specific integration patterns with Julia (Gay.jl) for chromatic seed management and gamut learning.

Does this skill support interactive proof development?

Yes, Narya Hatchery supports interactive workflows, including typed holes for later solving and integration with the ProofGeneral interaction mode.

What is Narya Hatchery used for?

It is used to facilitate work with the Narya proof assistant, specifically for higher-dimensional type theory, formal mathematical verification, and advanced type checking.

Can I use Narya Hatchery for software verification?

Absolutely. It is designed for verifying complex software contracts and ensuring correctness through rigorous type-theoretic proofs.

Narya Hatchery

Name: Narya Hatchery
Author: plurigrid

byplurigrid

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开发者工具

Facilitates formal verification and higher-dimensional type theory research using the Narya proof assistant.

Narya Hatchery is a specialized skill for interacting with the Narya proof assistant, a tool designed for multi-modal, higher-dimensional type theory. It provides a robust environment for normalization-by-evaluation, observational identity through bridge types, and parametricity. This skill is particularly useful for developers and mathematicians working on formal proofs, complex inductive/coinductive data structures, and verified software contracts, offering deep integration with ProofGeneral for an interactive development experience.

主要功能

018 GitHub stars

02Normalization-by-evaluation algorithm for efficient type checking

03Interactive proof development with typed holes and ProofGeneral integration

04Support for user-definable mixfix notations and record types

05Observational identity via bridge types with internal parametricity

06Matching and comatching case trees for (co)inductive datatypes

使用场景

01Developing domain-specific languages with custom mixfix notation and record types

02Formalizing complex mathematical proofs in higher-dimensional type theory

03Verifying software contract correctness using bridge types

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add plurigrid/asi narya-hatchery

For use in Claude.ai and ChatGPT

Download Skill