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adrianliechti
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validate-data

QA analyses for accuracy, methodology, and bias before stakeholder sharing.

4|4|Updated Dec 15, 2024
Search Tags:#methodology#qa#analysis#validate#calculations#data-quality#bias-review
One-click install
npx skills add https://github.com/adrianliechti/wingman-chat --skill validate-data-adrianliechti
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: validate-data
Source: https://github.com/adrianliechti/wingman-chat/tree/main/skills/data/validate-data
Command: npx skills add https://github.com/adrianliechti/wingman-chat --skill validate-data-adrianliechti

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill helps QA analyses before sharing with stakeholders by checking methodology, assumptions, calculations, and potential biases to ensure decisions are well-supported.

Core Features & Use Cases

  • Review methodology and assumptions: Examine question framing, data selection, population definition, and metric definitions to ensure alignment with stakeholder intent.
  • Run pre-delivery QA checks: Execute data quality, calculation, reasonableness, and presentation checks to catch errors before sharing.
  • Identify common analytical pitfalls: Systematically assess for issues like survivorship bias, incomplete period comparisons, and denominator shifts.
  • Verify calculations and narrative: Spot-check key numbers, verify subtotals, and ensure conclusions are logically supported with actionable improvement suggestions.
  • Documentation and confidence: Provide a clear confidence assessment and recommended next steps for stakeholders.

Quick Start

Provide the analysis document or data summary you want QA, and I will run the validation workflow.

Frequently Asked Questions about validate-data

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

FAQPage Schema
How do I QA data analysis methodology before sharing reports with stakeholders?▼

QA data analysis methodology by examining question framing, data selection, and metric definitions to ensure alignment with stakeholder intent. The validation workflow checks assumptions and identifies analytical pitfalls like survivorship bias before delivery.

What is the best way to verify calculations and check for bias in SQL results?▼

Verify calculations and check for bias in SQL results by spot-checking key numbers, verifying subtotals, and systematically assessing for incomplete period comparisons or denominator shifts. This ensures conclusions are logically supported and accurate.

Can I use automated pre-delivery checks to catch data quality errors in stakeholder reports?▼

You can use automated pre-delivery checks to execute data quality, calculation, reasonableness, and presentation validations. This workflow catches errors in documents, charts, and SQL results before sharing analyses with stakeholders.

How do I assess confidence in analytical conclusions and identify common analytical pitfalls?▼

Assess confidence in analytical conclusions by evaluating the narrative for logical support and checking for common analytical pitfalls like survivorship bias. The process generates a confidence assessment with actionable improvement suggestions.

Does methodology validation work for documents, charts, and methodological descriptions?▼

Methodology validation works for documents, reports, SQL results, charts, and methodological descriptions. It enforces a pre-delivery QA workflow that reviews assumptions, verifies calculations, and evaluates the narrative used in stakeholder reviews.

What common analytical pitfalls should I check for during data analysis validation?▼

During data analysis validation, check for common analytical pitfalls including survivorship bias, incomplete period comparisons, and denominator shifts. The workflow systematically assesses these issues to ensure decisions are well-supported by accurate data.

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