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data-validate

Audit data analyses for methodology, bias, and data quality flaws.

14|3|Updated Jan 19, 2026
Search Tags:#methodology#qa#analysis#data#accuracy#bias#validate
One-click install
npx skills add https://github.com/kevinlin/cowork-z --skill data-validate-kevinlin
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: data-validate
Source: https://github.com/kevinlin/cowork-z/tree/main/src-tauri/resources/skill-templates/data-validate
Command: npx skills add https://github.com/kevinlin/cowork-z --skill data-validate-kevinlin

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Auditing data analyses prior to sharing to ensure methodology accuracy, bias checks, and data quality.

Core Features & Use Cases

  • Systematic methodology review: check framing, data sources, variables, and assumptions.
  • Bias and validity checks: identify potential bias, confounders, and data quality gaps.
  • Report-quality outputs: generate a structured validation report with actionable recommendations.

Quick Start

Run /validate on your analysis to generate a structured QA report.

Frequently Asked Questions about data-validate

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

FAQPage Schema
How do I validate data analysis methodology before sharing a report?▼

You validate data analysis methodology by applying a structured checklist to audit framing, data sources, variables, and assumptions, which generates a detailed validation report with actionable caveats and recommendations.

What is the best way to check data quality and identify bias in dashboards?▼

Checking data quality and bias in dashboards involves systematically auditing data sources and assumptions to identify potential confounders, validity gaps, and methodology flaws before producing a structured QA validation report.

Can I run a QA review on a notebook or data request across different projects?▼

Yes, you can run a QA review on notebooks, reports, dashboards, and data requests across different teams and projects to identify methodology flaws, verify conclusions, and output structured validation reports.

How do I audit conclusions and calculations in my data analysis for accuracy?▼

Auditing conclusions and calculations for accuracy requires a systematic methodology review that checks framing, validates variables, and supports calculations to produce a detailed report with recommended caveats.

Does data validation support visualizations when reviewing analysis accuracy?▼

Yes, data validation supports calculations and visualizations during the QA review process to effectively audit methodology, identify data quality gaps, and report flaws with actionable recommendations.

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