Review julia
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.From its SKILL.md
npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-juliaAssembled 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.
SKILL.md
1.7 KB, 318 tokens by cl100k_base, 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
$ARGUMENTSto find the Julia file(s). Ifall, scan all.jlfiles in the project. -
Read standards from
.claude/rules/julia-code-conventions.md. -
Check these categories:
- Module Structure: Proper module organization, exports, includes
- Parallel Computing:
@everywhereannotations,pmapusage, worker data distribution - Optimization: Convergence checks, multiple starting values, grid search patterns
- Type Stability: Concrete types in hot loops,
@code_warntyperecommendations - Path Conventions:
joinpath()usage, no hardcoded OS-specific separators - Naming:
snake_casefunctions,CamelCasetypes, paper notation alignment - Common Pitfalls: Missing
@everywhere, local minima, large closures inpmap
-
Save report to
quality_reports/[script_name]_julia_review.md. -
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.
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.