analyze-data-quality Skill

Source

• Creator: openai
• Repository: openai/role-specific-plugins
• Skill file: plugins/data-analytics/skills/analyze-data-quality/SKILL.md

What it does

Assess whether structured data, query results, dashboards, or analytical evidence are trustworthy enough to use. Use when the task is to check data quality, reconcile conflicting sources or metric definitions, or decide whether evidence is safe to cite.

Skill instructions

Related Skills Use $design-kpis when the work is to define or redesign a KPI framework, metric definition, guardrail, or target rather than checking whether existing data is trustworthy. Use $validate-data when the work is to QA an analysis, chart, report, or recommendation rather than investigate the underlying data. Analyze Data Quality Assess whether a dataset is trustworthy enough for analysis, modeling, dashboards, experiments, or downstream pipelines. Start with the intended use and grain, run the highest-value checks for the data shape, and report concrete evidence, analytical risk, likely causes, and the smallest useful remediation or automated test. Workflow 1. Clarify the quality question and operating context. Establish what the dataset represents, the intended unit of analysis, the downstream use, whether the user cares about raw ingestion quality, transformed-model quality, or both, and the comparison baseline such as prior weeks, prior schema, or a trusted reference table