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validate-data

Assess accuracy, methodology, and biases in analyses and generate validation reports.

1|Updated Apr 2, 2026
Search Tags:#methodology#validation#analysis#quality#bias#stakeholders
One-click install
npx skills add https://github.com/kongaharsha/claude-skills --skill validate-data-kongaharsha
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: validate-data
Source: https://github.com/kongaharsha/claude-skills/tree/main/validate-data
Command: npx skills add https://github.com/kongaharsha/claude-skills --skill validate-data-kongaharsha

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

QA analysts often need to validate the integrity of analyses before presenting to stakeholders, ensuring the conclusions are accurate, the methodology is sound, and biases are identified and mitigated.

Core Features & Use Cases

  • Review methodology and assumptions for a given analysis to ensure framing, data selection, and population definitions are correct.
  • Run the pre-delivery QA checklist to verify data quality, calculations, and presentation.
  • Generate a confidence assessment and actionable improvement suggestions to clarify limitations and caveats for stakeholders.

Quick Start

Run /validate-data with your analysis to generate a validation report.

Frequently Asked Questions about validate-data

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

FAQPage Schema
How do I validate analysis accuracy before sharing reports with stakeholders?▼

To validate analysis accuracy, apply a structured workflow covering methodology review, calculation verification, pitfall checks, and narrative evaluation to generate a final confidence assessment for your reports.

What is pre-delivery QA for data projects and when do I need it?▼

Pre-delivery QA is a verification process checking data quality, calculations, and presentation before sharing results. You need it whenever distributing documents, SQL results, charts, or narratives to stakeholders across data projects.

How do I check my SQL results for methodology and bias issues?▼

Check SQL results for methodology and bias by reviewing assumptions, verifying data selection and population definitions, and evaluating the narrative framing to ensure conclusions are sound and biases are identified.

Can I assess biases in document narratives and charts before presenting them?▼

Yes, you can assess biases in documents and charts by evaluating the narrative framing, reviewing underlying assumptions, and running pitfall checks to identify limitations before generating actionable improvement suggestions.

What's the best way to verify calculations and methodology in a data analysis?▼

The best way to verify calculations and methodology is applying a structured QA checklist that reviews framing, confirms data selection accuracy, verifies computations, and produces a confidence assessment with caveats.

What are the limitations of manually reviewing data quality without a structured workflow?▼

Without a structured workflow, manual data quality review risks missing methodology flaws, unchecked biases, calculation errors, and undocumented limitations, leading to inaccurate conclusions being shared with stakeholders.

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