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julia-jet-opt

Detect optimization failures in Julia code using JET.jl macros.

30|6|Updated Jan 21, 2026
Search Tags:#performance#optimization#dispatch#julia#type stability#JET.jl
One-click install
npx skills add https://github.com/Krastanov/JuliaLLMAgentSkills --skill julia-jet-opt
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: julia-jet-opt
Source: https://github.com/Krastanov/JuliaLLMAgentSkills/tree/main/julia-jet-opt
Command: npx skills add https://github.com/Krastanov/JuliaLLMAgentSkills --skill julia-jet-opt

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps developers identify and fix performance bottlenecks in Julia code by detecting runtime dispatch, captured variables, and type instabilities that hinder optimization.

Core Features & Use Cases

  • Detect Runtime Dispatch: Pinpoint functions that cannot be resolved at compile time, leading to slower execution.
  • Identify Captured Variables: Find instances where closures capture reassigned variables, causing unnecessary memory allocations.
  • Analyze Type Instabilities: Uncover issues related to non-constant global variables or abstract field types that prevent efficient code generation.
  • Use Case: When your Julia function is unexpectedly slow, use this skill to automatically scan its entire call graph for optimization issues that @code_warntype might miss.

Quick Start

Use the julia-jet-opt skill to report optimization issues for the call sum(Any[1, 2, 3]).

Frequently Asked Questions about julia-jet-opt

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

FAQPage Schema
How do I detect runtime dispatch and type instabilities in Julia code?▼

This Skill detects type instabilities and runtime dispatch by analyzing the entire call graph using JET.jl's `@report_opt` macro, pinpointing functions that cannot be resolved at compile time.

Why does my Julia function have unexpected performance regressions and memory allocations?▼

Performance regressions in Julia often stem from captured variables in closures or non-constant global variables, which this Skill identifies by scanning the entire call graph for optimization failures.

Can I use JET.jl to find optimization issues that `@code_warntype` misses?▼

Yes, JET.jl can find optimization issues that `@code_warntype` misses by analyzing the entire call graph rather than just a single function body.

How do I test for type stability across a Julia call graph?▼

You test for type stability across a Julia call graph by applying JET.jl's `@test_opt` macro, which reports type instabilities and ensures optimized execution.

What is the best way to find non-constant global variables hindering Julia optimization?▼

The best way to find non-constant global variables hindering Julia optimization is to run a full call graph analysis using JET.jl to uncover abstract field types and type instabilities.

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