brycewang-stanford/review-julia
skills/14-luischanci-claude-code-research-starter/dot-claude/skills/review-julia/SKILL.md
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.
$ install --global
skillsauthnpx skillsauth add brycewang-stanford/Awesome-Agent-Skills-for-Empirical-Research review-juliaInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
Security Scan Results
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
3
Scanners Passed
9
Scanners in report
Clean
TrivyContainer and dependency vulnerability scanner
95%
Clean
SemgrepStatic code analysis for vulnerabilities
95%
Clean
mcp-scan (Snyk)Model Context Protocol security validation
95%
Skipped
Snyk (dep)Open source security scanning
50%
Skipped
Socket.devSupply chain security analysis
50%
Skipped
VirusTotalMulti-engine malware detection
50%
Skipped
CrowdStrikeAdvanced threat intelligence
50%
Skipped
OSV-ScannerOpen Source Vulnerability database check
50%
Skipped
OWASP Dep-Check
50%
Last scanned: Apr 24, 2026, 8:34 PM0.5s1 file scanned
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
-
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.
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