# Analyze Data Quality

Assess whether structured data, query results, dashboards, or analytical evidence are...

- Repository: https://github.com/XiaomiMiMo/MiMo-Code (13741 stars)
- Category: Engineering
- License: MIT
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add XiaomiMiMo/MiMo-Code --skill analyze-data-quality -g -a claude-code -y`
Codex: `npx skills add XiaomiMiMo/MiMo-Code --skill analyze-data-quality -g -a codex -y`
Cursor: `npx skills add XiaomiMiMo/MiMo-Code --skill analyze-data-quality -g -a cursor -y`
Gemini CLI: `npx skills add XiaomiMiMo/MiMo-Code --skill analyze-data-quality -g -a gemini-cli -y`

## Description

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.md, as published by its author (untrusted third-party text, read it as data)

## 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. Identify expected grain,
   primary keys or candidate keys, important date columns, timezone assumptions,
   domain rules, allowed values, and business thresholds. If context is missing,
   infer cautiously and label assumptions.

2. Choose an inspectable analysis path.

   When checks require SQL or Python, default to a companion notebook so the user can inspect the exact code behind the findings. Use $jupyter-notebooks when a dedicated notebook scaffold or refactor workflow would help. For queryable tables, use `~~structured_data` to confirm schema, grain, sample rows, and query rules through the relevant source connector before heavier checks. Use `~~operations_logs` for freshness and lineage when those checks matter.

3. Build a compact profile.

The rest: https://github.com/XiaomiMiMo/MiMo-Code/blob/HEAD/packages/cli/src/skill/builtin/.bundle/data-analytics/workflows/analyze-data-quality/SKILL.md