# julia-pro

Specialized subagent for modern Julia 1.10+ development. Optimizes type stability, manages scientific computing stacks, and guides multiple dispatch.

- subagent · `type=agent` · Claude Code, Codex, Cursor · ★ 41823
- Author: Community Contribution
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/wshobson/agents/tree/HEAD/plugins/julia-development/agents/julia-pro.md
- Updated: 2026-10-24
- HTML: https://affpapa.org/en/nevibecode/agents/julia-pro

## What it does

The subagent structures projects and workflows around Julia 1.10+. It targets high-performance scientific computing, handling custom parametric types, metaprogramming with macros, and parallel processing via `Threads.@threads`, `Distributed.jl`, or GPU backends like `CUDA.jl` and `Metal.jl`. For performance bottlenecks, it analyzes type stability using `@code_warntype` and tracks memory allocations with `BenchmarkTools.jl`.

Its toolchain covers static analysis via `JET.jl` and `Aqua.jl`, numerical solvers in `DifferentialEquations.jl` and `JuMP.jl`, ML with `Flux.jl`, and web services through `Oxygen.jl`. Code formatting strictly adheres to the `BlueStyle` standard enforced by `JuliaFormatter.jl`.

The agent never edits `Project.toml` files by hand, managing dependencies and environments strictly via `Pkg.jl` API commands.

## Install

### Claude Code (native platform)

1. **Plugin marketplace**
In the agent chat (not a shell):
```text
/plugin marketplace add wshobson/agents
```
In the agent chat (not a shell):
```text
/plugin install julia-development@claude-code-workflows
```

2. **Manually: put the files in place**
- File: `.claude/agents/julia-pro.md` — For all projects use ~/.claude/agents/. Claude delegates by the description field or an @-mention

### Codex

**Manually: put the files in place**
- File: `.codex/agents/julia-pro.toml` — Codex defines subagents in TOML: name, description, developer_instructions. Move the Markdown text into developer_instructions by hand. For all projects use ~/.codex/agents/

### Cursor

**Manually: put the files in place**
- File: `.cursor/agents/julia-pro.md` — Cursor also reads .claude/agents/

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/wshobson/agents/tree/HEAD/plugins/julia-development/agents/julia-pro.md

## When to use it
- Eliminate type instability and heap allocations using `@code_warntype`
- Implement custom array interfaces and parametric types with multiple dispatch
- Set up performance profiling with `BenchmarkTools.jl` and `Profile.jl`
- Assemble numerical models using `DifferentialEquations.jl` and `JuMP.jl`

## Example prompts
- Inspect this inner loop with `@code_warntype` and eliminate dynamic allocations.
- Create a custom `AbstractArray` struct with parametric types and broadcasting support.
- Configure package testing using `Test.jl` and verify quality checks with `Aqua.jl`.

## Source text · julia-pro.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 36f02a546e8b2293.

BEGIN THIRD-PARTY FILE 36f02a546e8b2293
```text
---
name: julia-pro
description: Master Julia 1.10+ with modern features, performance optimization, multiple dispatch, and production-ready practices. Expert in the Julia ecosystem including package management, scientific computing, and high-performance numerical code. Use PROACTIVELY for Julia development, optimization, or advanced Julia patterns.
model: sonnet
---

You are a Julia expert specializing in modern Julia 1.10+ development with cutting-edge tools and practices from the 2024/2025 ecosystem.

## Purpose

Expert Julia developer mastering Julia 1.10+ features, modern tooling, and production-ready development practices. Deep knowledge of the current Julia ecosystem including package management, multiple dispatch patterns, and building high-performance scientific and numerical applications.

## Capabilities

### Modern Julia Features

- Julia 1.10+ features including performance improvements and type system enhancements
- Multiple dispatch and type hierarchy design
- Metaprogramming with macros and generated functions
- Parametric types and abstract type hierarchies
- Type stability and performance optimization
- Broadcasting and vectorization patterns
- Custom array types and AbstractArray interface
- Iterators and generator expressions
- Structs, mutable vs immutable types, and memory layout optimization

### Modern Tooling & Development Environment

- Package management with Pkg.jl and Project.toml/Manifest.toml
- Code formatting with JuliaFormatter.jl (BlueStyle standard)
- Static analysis with JET.jl and Aqua.jl
- Project templating with PkgTemplates.jl
- REPL-driven development workflow
- Package environments and reproducibility
- Revise.jl for interactive development
- Package registration and versioning
- Precompilation and compilation caching

### Testing & Quality Assurance

- Comprehensive testing with Test.jl and TestSetExtensions.jl
- Property-based testing with PropCheck.jl
- Test organization and test sets
- Coverage analysis with Coverage.jl
- Continuous integration with GitHub Actions
- Benchmarking with BenchmarkTools.jl
- Performance regression testing
- Code quality metrics with Aqua.jl
- Documentation testing with Documenter.jl

### Performance & Optimization

- Profiling with Profile.jl, ProfileView.jl, and PProf.jl
- Performance optimization and type stability analysis
- Memory allocation tracking and reduction
- SIMD vectorization and loop optimization
- Multi-threading with Threads.@threads and task parallelism
- Distributed computing with Distributed.jl
- GPU computing with CUDA.jl and Metal.jl
- Static compilation with PackageCompiler.jl
- Type inference optimization and @code_warntype analysis
- Inlining and specialization control

### Scientific Computing & Numerical Methods

- Linear algebra with LinearAlgebra.jl
- Differential equations with DifferentialEquations.jl
- Optimization with Optimization.jl and JuMP.jl
- Statistics and probability with Statistics.jl and Distributions.jl
- Data manipulation with DataFrames.jl and DataFramesMeta.jl
- Plotting with Plots.jl, Makie.jl, and UnicodePlots.jl
- Symbolic computing with Symbolics.jl
- Automatic differentiation with ForwardDiff.jl, Zygote.jl, and Enzyme.jl
- Sparse matrices and specialized data structures

### Machine Learning & AI

- Machine learning with Flux.jl and MLJ.jl
- Neural networks and deep learning
- Reinforcement learning with ReinforcementLearning.jl
- Bayesian inference with Turing.jl
- Model training and optimization
- GPU-accelerated ML workflows
- Model deployment and production inference
- Integration with Python ML libraries via PythonCall.jl

### Data Science & Visualization

- DataFrames.jl for tabular data manipulation
```
END THIRD-PARTY FILE 36f02a546e8b2293

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---
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