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validate-data

Validate data analyses for accuracy, methodology, and bias before presentation.

23.4k|2.8k|Updated Jan 23, 2026
Search Tags:#data validation#qa checklist#bias detection#analysis review#statistical rigor#methodology check
One-click install
npx skills add https://github.com/anthropics/knowledge-work-plugins --skill validate-data-anthropics
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: validate-data
Source: https://github.com/anthropics/knowledge-work-plugins/tree/main/data/skills/validate-data
Command: npx skills add https://github.com/anthropics/knowledge-work-plugins --skill validate-data-anthropics

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill addresses the critical need to ensure the accuracy, methodological soundness, and bias-free nature of data analyses before they are shared with stakeholders, preventing costly errors and misinterpretations.

Core Features & Use Cases

  • Methodology Review: Assesses the framing, data selection, metric definitions, and assumptions of an analysis.
  • QA Checklist Execution: Systematically checks for data quality, calculation correctness, reasonableness, and presentation issues.
  • Pitfall Identification: Detects common analytical traps like join explosions, survivorship bias, and denominator shifting.
  • Visualization Assessment: Evaluates charts for clarity, accuracy, and potential for misleading interpretations.
  • Narrative and Conclusion Evaluation: Verifies that conclusions are data-supported and that uncertainty is communicated.
  • Confidence Assessment: Provides a clear rating (Ready to share, Share with caveats, Needs revision) with actionable improvement suggestions.
  • Use Case: Before presenting a quarterly performance report to the executive team, use this Skill to rigorously vet the underlying analysis, ensuring all calculations are correct, methodologies are sound, and conclusions are well-supported by the data.

Quick Start

Use the validate-data skill to review the attached analysis document for accuracy and methodology.

Frequently Asked Questions about validate-data

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

FAQPage Schema
How do I validate data analysis methodology before stakeholder presentation?▼

To validate data analysis methodology, you can review the framing, data selection, metric definitions, and assumptions, and then execute a QA checklist to generate a confidence assessment with actionable improvement suggestions.

What is the best way to check SQL queries and charts for analytical pitfalls?▼

Checking SQL queries and charts for analytical pitfalls involves assessing visualizations for misleading interpretations and identifying common traps like join explosions, survivorship bias, and denominator shifting.

How do I detect bias in statistical findings and data reports?▼

Detecting bias in statistical findings requires evaluating the narrative and conclusions to verify they are data-supported, ensuring uncertainty is communicated, and systematically checking for calculation correctness and reasonableness.

Can I use a QA checklist to assess the confidence level of my data analysis?▼

You can use a QA checklist to assess data analysis confidence levels, generating a clear rating of Ready to share, Share with caveats, or Needs revision based on the accuracy and rigor of your statistical findings.

When do I need to review data visualizations for misleading interpretations?▼

You need to review data visualizations for misleading interpretations before sharing reports with stakeholders, evaluating charts for clarity and accuracy to prevent costly errors and misinterpretations of statistical findings.

Does reviewing methodology and data quality require specific statistical tools?▼

Reviewing methodology and data quality does not require specific external statistical tools, as the validation process executes a systematic checklist internally to assess calculation correctness, metric definitions, and analytical assumptions.

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