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Bauhaus-InfAU
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data-validation

Validate data analyses for accuracy, bias, and reproducibility.

Updated Feb 12, 2026
Search Tags:#qa#data quality#data validation#accuracy#bias detection#reproducibility
One-click install
npx skills add https://github.com/Bauhaus-InfAU/infau-skill-base --skill data-validation-bauhaus-infau
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-validation
Source: https://github.com/Bauhaus-InfAU/infau-skill-base/tree/main/data/skills/data-validation
Command: npx skills add https://github.com/Bauhaus-InfAU/infau-skill-base --skill data-validation-bauhaus-infau

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the critical need for rigorous quality assurance in data analysis, preventing errors, biases, and misinterpretations before sharing findings.

Core Features & Use Cases

  • Methodology Checks: Validates data quality, calculation logic, and aggregation correctness.
  • Accuracy Verification: Performs reasonableness checks and cross-validation against known benchmarks.
  • Bias Detection: Identifies common pitfalls like survivorship bias and selection bias.
  • Reproducibility: Enforces documentation standards for clear, repeatable analysis.
  • Use Case: Before presenting a quarterly business review, use this Skill to run through a comprehensive checklist ensuring all metrics are accurate, denominators are correct, and no biases are present in the segmentation.

Quick Start

Run through the pre-delivery QA checklist for the attached analysis report.

Frequently Asked Questions about data-validation

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

FAQPage Schema
How do I validate data analysis accuracy before presenting a quarterly business review?▼

Data validation identifies common analytical errors and biases by checking data quality, verifying calculation logic, and running reasonableness assessments against known benchmarks to ensure accuracy before presenting findings.

How do I detect survivorship bias and selection bias in my data aggregation?▼

Detect survivorship bias by applying a comprehensive validation framework that assesses aggregation correctness, identifies join explosions, and performs reasonableness checks on your segmentation logic.

What is the best way to enforce reproducibility standards for repeatable data workflows?▼

The best way to enforce reproducibility is to follow documentation standards that facilitate repeatable analysis workflows, ensuring all calculation logic and data quality checks are clearly recorded.

How do I check for join explosions and calculation errors in my data analysis?▼

Check for join explosions and calculation errors by performing methodology checks that validate data quality, verify calculation logic, and assess aggregation correctness within your analysis pipeline.

When do I need to run a data quality check on my analysis report?▼

You need to run a data quality check when preparing to share findings, such as before a quarterly business review, to prevent errors, biases, and misinterpretations in your metrics and denominators.

Can I cross-validate my metrics against known benchmarks to ensure reasonableness?▼

Yes, you can cross-validate metrics against known benchmarks through accuracy verification, which performs reasonableness assessments to confirm your data analysis results align with expected outcomes.

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