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/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.

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auto-empirical-research-skills
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Install
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-julia --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/review-julia

Context preview

The summary Claude sees to decide when to auto-load this skill.

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

review-julia.SKILL.md
name: review-julia
description: 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.
disable-model-invocation: true
argument-hint: "[filename or 'all']"
allowed-tools: ["Read", "Grep", "Glob", "Write", "Task"]

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`.
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📌 文档结构（2026-07-22 起）： 本文件是中文默认入口 —— banner + badges + 信任面 + 9 阶段流水线速览 + 76 行合集总表。 每个合集的完整描述、按用途分组、精确数字、验证方法在 docs/CONTENT_ZH.md（扩展正文，总表行内的 → 直接跳转到对应锚点）。 English version: README-en.md · 中文扩展正文：docs/CONTENT_ZH.md · README-zh-CN.md 已弃用（重定向占位） 🌐 语言: English |

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Repo: brycewang-stanford/Auto-Empirical-Research-Skills

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