data-validation

Validate data analyses against QA checklists for accuracy and reproducibility.

10|1|Updated Feb 19, 2026

Search Tags:#data validation #bias detection #reproducibility #data accuracy #QA checklist

Ready to run

npx skills add https://github.com/giadaf-boosha/claude-code --skill data-validation-giadaf-boosha

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-validation
Source: https://github.com/giadaf-boosha/claude-code/tree/main/skills/data-data-validation
Command: npx skills add https://github.com/giadaf-boosha/claude-code --skill data-validation-giadaf-boosha

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill provides a thorough QA process for data analysis, addressing methodology, accuracy, and bias, to ensure reliable insights for stakeholders.

Core Features & Use Cases

Pre-Delivery QA Checklist: Offers a structured checklist for reviewing analyses before sharing them with stakeholders.
Common Data Analysis Pitfalls: Identifies and prevents common errors in data analysis.
Result Sanity Checking: Validates the reasonableness of analysis results.
Documentation Standards: Provides templates for reproducible analysis documentation.
Use Case: Before presenting a data-driven report to senior management, use this Skill to run the QA checklist and verify the accuracy of your analysis.

Quick Start

Run the data-validation skill on your analysis to perform a comprehensive quality check.

Frequently Asked Questions about data-validation

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

FAQPage Schema

How do I run a data validation QA checklist before presenting analysis to stakeholders?▼

Data validation checks data quality and accuracy by performing comprehensive quality assurance on structured data. It identifies common analysis errors and validates results against predefined standards to ensure reliable insights.

How do I check my data analysis for bias and common methodology errors?▼

You check for bias and methodology errors by applying a comprehensive QA process that reviews analysis steps and validates results. This detects common data analysis pitfalls and ensures methodology correctness before reporting.

Does data validation work with unstructured data or do I need to format it first?▼

Data validation requires structured data to be effective. You must format and structure your data before running the QA checklist so the validation checks can accurately assess quality and reproducibility.

What is the best way to ensure reproducibility in my data analysis reports?▼

The best way to ensure reproducibility is to use documentation templates provided during data validation. These templates verify methodology correctness and create a structured record of your analysis process.

Why does my data analysis need result sanity checking before delivery?▼

Result sanity checking is needed to validate the reasonableness of analysis outputs and prevent common errors. It acts as a final pre-delivery QA step to ensure accuracy and methodology correctness for stakeholders.

What limitations should I expect when using a QA checklist for data accuracy?▼

The main limitation is that the QA checklist requires structured data and predefined standards to be effective. Without properly structured inputs, the validation checks cannot accurately assess data quality or reproducibility.