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research-harness-clean

Generate and execute reproducible data cleaning scripts for R, Python, or Stata.

39|46|Updated May 29, 2026
Search Tags:#merging#reproducible#data-cleaning#data-wrangling#research-workflow#audit-traceability
One-click install
npx skills add https://github.com/maxwell2732/claudecode-research-harness-workflow --skill research-harness-clean
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: research-harness-clean
Source: https://github.com/maxwell2732/claudecode-research-harness-workflow/tree/main/skills/research-harness-clean
Command: npx skills add https://github.com/maxwell2732/claudecode-research-harness-workflow --skill research-harness-clean

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates the end-to-end preparation of research-ready data by generating and executing reproducible cleaning, harmonization, reshaping, and merging scripts that preserve raw data integrity and provide auditable outputs.

Core Features & Use Cases

  • Generate deterministic cleaning, harmonization, and merging scripts that log every step.
  • Produce cleaned datasets along with a cleaning report and a merge report for traceability.
  • Use Case: apply to multi-source datasets where raw data must remain immutable while producing analysis-ready outputs.

Quick Start

Provide the data directory and plan to start a reproducible cleaning run while protecting raw data integrity.

Frequently Asked Questions about research-harness-clean

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

FAQPage Schema
How do I create reproducible data cleaning scripts for research datasets?▼

Reproducible data cleaning scripts are generated automatically by providing a data directory and plan, ensuring every harmonization and reshaping step is logged for audit traceability without modifying raw data.

Can I use this skill to merge multiple datasets while keeping the raw data immutable?▼

Yes, you can merge multi-source datasets while keeping raw data immutable. The process generates a merge report alongside the analysis-ready output to ensure full traceability of all transformations applied.

Does reproducible data wrangling work with R, Python, and Stata?▼

Reproducible data wrangling supports R, Python, and Stata. The execution environment is specified by the plan, allowing you to generate and run deterministic scripts in your preferred language for research workflows.

What is the best way to maintain an audit trail for data harmonization and reshaping?▼

Maintaining an audit trail for data harmonization requires plan-driven execution that automatically generates comprehensive cleaning and merge reports, logging every transformation step to verify reproducibility and data integrity.

Do I need to provide a specific plan to start the data wrangling pipeline?▼

Yes, you need to provide a plan alongside the data directory to initiate the pipeline. This plan dictates the script generation, execution environment, and specific cleaning, harmonization, and merging operations applied to the raw data.

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