review-julia

SkillFiles & storage

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.

Instructions available. Your AI can read the instructions. Execution depends on the setup they require.

Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.

Then ask your AI: use the review-julia skill

What this skill tells your AI

The instructions your AI receives, as published by brycewang-stanford/auto-empirical-research-skills in skills/14-luischanci-claude-code-research-starter/dot-claude/skills/review-julia/SKILL.md and read by ahel’s review.

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

  1. Identify target: Use $ARGUMENTS to find the Julia file(s). If all, scan all .jl files in the project.

  2. Read standards from .claude/rules/julia-code-conventions.md.

  3. 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
  4. Save report to quality_reports/[script_name]_julia_review.md.

  5. Present summary: Total issues, severity breakdown, top critical issues.

Important

  • NEVER edit source files. Report only.
  • Prioritize correctness and performance over style.
  • For Julia code generation patterns (MLE, GMM, simulation), see /econometrics-julia.

Signals

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Sep 2026
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Item type
skill
Key
review-julia
Source
github.com/brycewang-stanford/auto-empirical-research-skills