What is the Data Validation skill?

It is a specialized Claude Code skill designed to help users perform thorough quality assurance on data analyses by checking methodology, verifying accuracy, and detecting potential biases.

How does it help with SQL query verification?

The skill provides specific patterns for detecting 'join explosions' that inflate counts, verifying aggregation grain, and ensuring WHERE clauses don't lead to unintended data exclusions.

What kind of sanity checks does it perform?

It guides users through magnitude checks (ensuring numbers are in a plausible range), trend continuity checks, and cross-validation against known benchmarks or dashboards.

Does this skill provide templates for documentation?

Yes, it includes standardized templates for documenting data sources, definitions, methodology, and assumptions to ensure your work is fully reproducible by others.

Can this skill identify logical biases in my analysis?

Yes, it includes frameworks for identifying and preventing common analytical errors such as survivorship bias, incomplete period comparisons, and selection bias in segmentation.

Data Validation & QA

Name: Data Validation & QA
Author: anthropics

byanthropics

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8,000

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データサイエンスとML

Validates data analysis methodology, accuracy, and documentation to ensure high-quality, reproducible insights for stakeholders.

The Data Validation skill provides a comprehensive framework for quality assurance in data analysis workflows within Claude. It helps knowledge workers and analysts avoid common pitfalls like survivorship bias and join explosions while ensuring calculation accuracy, logical consistency, and professional documentation. By implementing standardized sanity checks and pre-delivery checklists, it empowers users to catch errors before they reach stakeholders and ensures that every analysis is fully reproducible, transparent, and grounded in sound methodology.

主な機能

018,000 GitHub stars

02Detection and mitigation strategies for common pitfalls like join explosions and survivorship bias.

03Standardized documentation templates for capturing methodology, assumptions, and SQL queries.

04Magnitude and reasonableness sanity checks to identify outliers or unrealistic growth rates.

05Comprehensive pre-delivery QA checklists for data freshness, completeness, and null handling.

06Calculation verification frameworks for aggregation logic, denominators, and date alignment.

ユースケース

01Reviewing data visualizations and dashboards for accuracy, labeling, and scale consistency.

02Preparing reproducible documentation and methodology summaries for peer review or stakeholder handoff.

03Auditing complex SQL queries and join logic before generating high-stakes business reports.

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add anthropics/knowledge-work-plugins data-validation

For use in Claude.ai and ChatGPT

Download Skill