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data-validation

Validate data analyses with methodology checks, accuracy verification, and bias detection.

Updated Mar 5, 2026
Search Tags:#data integrity#data quality#data validation#bias detection#reproducibility#analysis QA
One-click install
npx skills add https://github.com/8GG-Git/knowledge-work-plugins --skill data-validation-8gg-git
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-validation
Source: https://github.com/8GG-Git/knowledge-work-plugins/tree/main/data/skills/data-validation
Command: npx skills add https://github.com/8GG-Git/knowledge-work-plugins --skill data-validation-8gg-git

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 from reaching stakeholders.

Core Features & Use Cases

  • Pre-Delivery QA Checklist: Comprehensive checks for data quality, calculation logic, and reasonableness.
  • Pitfall Identification: Educates on common data analysis mistakes like join explosions, 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 sharing a crucial sales performance report, use this Skill to run through the checklist, verify aggregation logic, and ensure no survivorship bias is present in the user segmentation.

Quick Start

Use the data-validation skill to run through the pre-delivery QA checklist on the attached analysis document.

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 pre-delivery QA checklist on my data analysis report?▼

Run a pre-delivery QA checklist by verifying calculation logic, checking data quality, and assessing reasonableness to ensure data accuracy before sharing analytical reports with stakeholders.

What is survivorship bias and how do I detect it in user segmentation?▼

Survivorship bias occurs when segments exclude invisible failures. Detect it during pitfall identification by validating that user segments include all relevant populations, preventing skewed analytical conclusions.

How do I prepare data analysis documentation for reproducibility?▼

Prepare data analysis documentation for reproducibility by using templates to record methodology, calculation logic, and code, ensuring others can replicate the work and verify data integrity.

What are common data analysis pitfalls like join explosions and incomplete period comparisons?▼

Common data analysis pitfalls include join explosions that duplicate records, survivorship bias that skews segments, and incomplete period comparisons that misrepresent trends. Identify these to maintain data quality.

Can I use this data validation process for general data governance tasks?▼

Use this data validation process to satisfy robust data governance requirements by performing methodology checks, accuracy verification, and bias detection, ensuring reliable decision-making based on analyzed data.

What is the best way to verify calculation logic and aggregation accuracy before sharing a report?▼

Verify calculation logic and aggregation accuracy by applying a comprehensive pre-delivery QA checklist that identifies common mistakes and ensures data analyses are free from errors before reaching stakeholders.

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