data-validation

Name: data-validation
Availability: InStock
Author: juandaniel190

Validate data analysis methodology, accuracy, and bias before delivery.

1|Updated Jan 17, 2026

Search Tags:#qa #analysis #data validation #accuracy #bias detection #reproducibility

Ready to run

npx skills add https://github.com/juandaniel190/personal-projects --skill data-validation-juandaniel190

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: data-validation
Source: https://github.com/juandaniel190/personal-projects/tree/main/.claude/.claude_backup/skills/data/data-data-validation
Command: npx skills add https://github.com/juandaniel190/personal-projects --skill data-validation-juandaniel190

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 inaccuracies before insights are shared with stakeholders.

Core Features & Use Cases

Methodology Checks: Validates data quality, calculation logic, and aggregation.
Accuracy Verification: Performs reasonableness checks and cross-validation against known sources.
Bias Detection: Identifies common pitfalls like survivorship bias and selection bias.
Reproducibility: Ensures analyses are well-documented for easy replication.
Use Case: Before presenting a quarterly business review, use this Skill to run through a comprehensive checklist ensuring all metrics are accurate, calculations are sound, and potential biases are addressed.

Quick Start

Run the data validation skill to check the methodology and accuracy of the latest sales report.

Frequently Asked Questions about data-validation

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

FAQPage Schema

How do I check data analysis accuracy before sending reports to stakeholders?▼

To ensure data analysis accuracy before sending reports, run methodology checks, verify calculations, and perform sanity checks to guarantee result integrity for stakeholders.

What is survivorship bias and how do I detect it in my data?▼

Survivorship bias in data analysis occurs when only successful entities are analyzed. Bias detection validates your methodology and scoping parameters to identify and mitigate these selection biases.

How do I validate calculation logic and prevent join explosions in data aggregation?▼

To validate calculation logic and prevent join explosions, perform methodology checks on data aggregation steps to identify common data pitfalls and verify calculation soundness.

How do I ensure reproducibility standards in my data analysis workflow?▼

To ensure reproducibility standards in data analysis, validate that your calculations and methodology are well-documented for easy replication, passing sanity checks for consistent result verification.

Can I use automated QA to verify quarterly business review metrics?▼

Yes, you can use automated QA to run a comprehensive checklist on quarterly business review metrics, verifying calculation accuracy, evaluating methodology soundness, and addressing potential biases before presentation.

What are the limitations of automated bias detection in data validation?▼

Limitations of automated bias detection in data validation include reliance on identifying known common pitfalls like survivorship and selection bias, serving as a sanity check rather than guaranteeing absolute analytical accuracy.