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data-validation

Validate data quality, calculation accuracy, and result reasonableness in analytical reports.

1|Updated Jan 26, 2026
Search Tags:#validation#qa#data quality#data analysis#bias detection#reproducibility
One-click install
npx skills add https://github.com/olavocarvalho/data-agents --skill data-validation-olavocarvalho
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-validation
Source: https://github.com/olavocarvalho/data-agents/tree/main/skills/data-validation
Command: npx skills add https://github.com/olavocarvalho/data-agents --skill data-validation-olavocarvalho

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill addresses the critical need for rigorous data quality assurance before analysis is shared, preventing errors, biases, and misinterpretations that can lead to flawed conclusions.

Core Features & Use Cases

  • Pre-Delivery QA Checklist: Comprehensive checks for data quality, calculation correctness, and result reasonableness.
  • Pitfall Identification: Explains common data analysis errors like join explosion, survivorship bias, and incomplete period comparisons.
  • Reproducibility Standards: Provides templates for documenting analyses and code to ensure others can replicate the work.
  • Use Case: Before presenting a quarterly business review, use this Skill to run through a checklist ensuring all data sources are correct, calculations are sound, and no common biases have crept into the analysis.

Quick Start

Run through the pre-delivery QA checklist to validate the current analysis.

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 quality and calculation accuracy before sharing an analytical report?▼

To check data quality and calculation accuracy, run through a pre-delivery QA checklist that validates source correctness, calculation logic, and result reasonableness to prevent flawed conclusions.

What is join explosion and how does it affect data analysis?▼

Join explosion is a data analysis pitfall where improperly configured table joins multiply records, skewing calculations. Identifying it ensures calculation accuracy and maintains overall data quality.

How do I identify survivorship bias and incomplete period comparisons in my data?▼

You identify survivorship bias and incomplete period comparisons by applying targeted data validation checks that detect mismatched timeframes and unrepresentative surviving data samples.

What's the best way to enforce reproducibility standards for data analysis?▼

The best way to enforce reproducibility standards is using provided documentation templates to thoroughly record analysis steps and code, ensuring others can easily replicate the work.

Can I use this data validation checklist for a quarterly business review?▼

Yes, you can use this data validation checklist for a quarterly business review to verify data sources, confirm calculation soundness, and ensure no common biases affect results.

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