Review R and Python research scripts against an 11-category scorecard.
npx skills add https://github.com/flonat/claude-code-flonat --skill code-review-flonat
Please help me install this Agent Skill. Skill: code-review Source: https://github.com/flonat/claude-code-flonat/tree/main/skills/code-review Command: npx skills add https://github.com/flonat/claude-code-flonat --skill code-review-flonat
This Skill automates the rigorous quality assurance of research scripts, ensuring reproducibility, clarity, and correctness, thereby reducing errors and improving the reliability of research findings.
/code-review on your R and Python analysis scripts to generate a CODE-REVIEW-REPORT.md that identifies potential issues in reproducibility, estimator correctness, and figure presentation.Run a quality review on the script located at 'analysis/main.R'.
High-intent search queries and answers about installing and using this skill.
Perform a research script code review by evaluating R and Python files against an 11-category scorecard covering reproducibility, structure, correctness, and presentation. This process generates a detailed report file without modifying the original source code.
Check academic code reproducibility and quality by running an automated review that assesses script structure, output hygiene, and figure presentation. It produces a markdown report identifying potential issues in estimator correctness and reproducibility before paper submission.
No, this code review tool cannot automatically fix issues in your Python analysis scripts. It operates in a report-only mode, providing objective feedback by generating a markdown review file while leaving your original source files completely unmodified.
Yes, the research script quality assurance scorecard evaluates function quality and dependencies. It also assesses data persistence, domain correctness, figure quality, output hygiene, and language-specific checks for R and Python.
Generate a reproducibility report by running a report-only quality review on your analysis scripts. The process automatically creates a markdown file containing objective feedback on script structure and correctness without altering your source files.
choi-jaehyunAudit R scripts for reproducibility, correctness, and local style compliance.
irudikReview R scripts for quality, reproducibility, and academic standards.
Clay-HHKEnforce academic writing style and LaTeX/figure standards in manuscripts.
iliaalVerify code review feedback correctness and respond with supporting evidence.