Review julia

Skill brycewang-stanford/Auto-Empirical-Research-Skills/skills/14-luischanci-claude-code-research-starter/dot-claude/skills/review-julia

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Install

npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-julia

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.

What its author says it does

Copied from the file, not written here

Run the Julia code review protocol on Julia scripts. Checks code quality, type stability, parallel computing patterns, and scientific computing standards. Produces a report without editing files.

SKILL.md

1.7 KB, as published. Nobody here has run it

Review Julia Code

Run a comprehensive Julia code review on the specified script(s). Do NOT edit any source files -- produce a report only.

Steps

Identify target: Use $ARGUMENTS to find the Julia file(s). If all, scan all .jl files in the project.
Read standards from .claude/rules/julia-code-conventions.md.
Check these categories:
- Module Structure: Proper module organization, exports, includes
- Parallel Computing: @everywhere annotations, pmap usage, worker data distribution
- Optimization: Convergence checks, multiple starting values, grid search patterns
- Type Stability: Concrete types in hot loops, @code_warntype recommendations
- Path Conventions: joinpath() usage, no hardcoded OS-specific separators
- Naming: snake_case functions, CamelCase types, paper notation alignment
- Common Pitfalls: Missing @everywhere, local minima, large closures in pmap
Save report to quality_reports/[script_name]_julia_review.md.
Present summary: Total issues, severity breakdown, top critical issues.

Important