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Rukkha1024
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analysis-reviewer

Validate and repair analysis workflows for reproducibility and statistical consistency.

Updated Aug 23, 2026
Search Tags:#code quality#debugging#validation#analysis#review#reproducibility
One-click install
npx skills add https://github.com/Rukkha1024/muscle_synergy_analysis --skill analysis-reviewer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: analysis-reviewer
Source: https://github.com/Rukkha1024/muscle_synergy_analysis/tree/main/.agents/skills/analysis-reviewer
Command: npx skills add https://github.com/Rukkha1024/muscle_synergy_analysis --skill analysis-reviewer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the challenge of ensuring the accuracy, reproducibility, and methodological soundness of existing data analysis workflows, particularly those found within analysis/* directories.

Core Features & Use Cases

  • Reproducibility Validation: Verifies that analysis scripts produce consistent outputs and align with documented methods.
  • Quantitative Consistency Check: Cross-references numerical results from scripts with reports to identify discrepancies.
  • Methodological Alignment: Assesses if the implemented analysis logic matches stated methodologies or cited literature.
  • Automated Fixing: Corrects critical (High severity) issues automatically to improve workflow reliability.
  • Use Case: A data scientist has an existing analysis pipeline for a research project. They use this Skill to ensure the Python scripts accurately reflect the methods described in their report.md and that the reported statistics are reproducible.

Quick Start

Use the analysis-reviewer skill to review the analysis folder located at analysis/experiment-001.

Frequently Asked Questions about analysis-reviewer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I validate the reproducibility of my data analysis workflows?▼

To validate analysis workflow reproducibility, review scripts within the analysis/* directory to verify they produce consistent outputs and align with documented methods, ensuring scientific rigor and quantitative consistency.

How do I check if my analysis scripts match the methods described in my report?▼

Checking methodological alignment involves assessing whether implemented analysis logic matches stated methodologies or cited literature, cross-referencing numerical results from scripts with reports to identify discrepancies.

Can I automatically fix high severity issues in my statistical analysis code?▼

Yes, you can automatically fix high severity issues in statistical analysis code by running a review process that corrects critical reproducibility and quantitative consistency errors to improve workflow reliability.

What is the best way to ensure quantitative consistency across data analysis pipelines?▼

The best way to ensure quantitative consistency is to cross-reference numerical results from analysis scripts with reports to identify discrepancies, validating that statistical interpretation aligns with paper methods.

Does the analysis review process report results to the console by default?▼

Yes, the analysis review process defaults to console-first reporting, outputting validation results for reproducibility, quantitative consistency, and methodological alignment directly to the terminal.

Why does my analysis pipeline fail to align with my documented scientific methods?▼

Analysis pipelines fail methodological alignment when implemented logic diverges from stated methodologies or cited literature, requiring a validation review to repair discrepancies and ensure statistical interpretation accuracy.

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