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jax-skills

Load, map, reduce, differentiate, scan, and JIT-compile arrays with JAX.

4|Updated Feb 22, 2026
Search Tags:#jax#numpy#jit#gradient#autodiff#rnn#array-processing
One-click install
npx skills add https://github.com/GeneralReasoning/env-skillsbench --skill jax-skills-generalreasoning
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: jax-skills
Source: https://github.com/GeneralReasoning/env-skillsbench/tree/main/jax-computing-basics/environment/skills/jax-skills
Command: npx skills add https://github.com/GeneralReasoning/env-skillsbench --skill jax-skills-generalreasoning

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Enables fast numerical computing and ML workflow prototyping using JAX, providing a concise API for array operations, differentiation, and compilation.

Core Features & Use Cases

  • Loading and saving arrays with numpy-compatible formats
  • Elementwise mapping and reductions over arrays
  • Gradient computation and automatic differentiation
  • Recurrent scan (RNN-style) workflows
  • Just-In-Time (JIT) compilation for performance
  • Lightweight utility functions for prototyping ML models

Use cases include scientific computing, ML model prototyping, and dynamic array transformations in research and data processing pipelines.

Quick Start

Load an array, apply a map_op to transform elements, perform a mean reduction, and execute a JIT-compiled function to produce a result.

Frequently Asked Questions about jax-skills

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I compute gradients for array operations using JAX?▼

JAX computes gradients for array operations using built-in automatic differentiation. The Skill applies autodiff to your functions, enabling fast gradient calculations for ML prototyping and scientific computing workflows.

Can I use JIT compilation to speed up numerical computing tasks?▼

JIT compilation speeds up numerical computing tasks by optimizing function execution. The Skill provides a clean Python API to run Just-In-Time compiled functions, enhancing performance for elementwise mapping and reductions over arrays.

What is the best way to run recurrent scan workflows for RNN-style processing?▼

Running recurrent scan workflows for RNN-style processing is best handled through this Skill's dedicated scan operations. It executes recurrent array transformations efficiently, supporting dynamic data processing pipelines and ML model prototyping.

Does this tool support loading and saving arrays in numpy-compatible formats?▼

This tool supports loading and saving arrays in numpy-compatible formats. It processes array data by reading and writing standard numpy structures, ensuring seamless integration with existing scientific computing pipelines.

Are there limitations when using JAX for dynamic array transformations?▼

When using JAX for dynamic array transformations, array shapes must remain static during JIT compilation. While the Skill supports dynamic transformations, shape variations require recompilation, which may impact prototyping speed.

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