pipeline
Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy +…
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.
Claude Code
$ npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-julia --agent claude-code
Codex
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-julia -a codex -y
Cursor
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-julia -a cursor -y
Gemini CLI
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-julia -a gemini-cli -y
GitHub Copilot
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-julia -a github-copilot -y
OpenCode
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-julia -a opencode -y
Windsurf
$ npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill review-julia -a windsurf -y
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"]
Run a comprehensive Julia code review on the specified script(s). **Do NOT edit any source files** -- produce a report only.
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**:
4. **Save report** to `quality_reports/[script_name]_julia_review.md`.
5. **Present summary**: Total issues, severity breakdown, top critical issues.
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