# Validate Data

Validate whether an analysis is accurate, well-supported, and ready to share or use for...

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

## Description

Validate whether an analysis is accurate, well-supported, and ready to share or use for a decision. Use when reviewing methodology, calculations, comparisons, visuals, caveats, or conclusions.

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

## Related Skills

Use $analyze-data-quality when validation depends on whether the underlying data is trustworthy, comparable, fresh, or at the right grain.

Use $product-business-analysis when the task asks for a recommendation or decision after the validation pass.

# Validate Data Analysis

Validate an analysis before it is shared with stakeholders. Focus on whether the question, data, methodology, calculations, visuals, claims, caveats, and recommendations are trustworthy enough for the stated audience and decision.
This skill is for analysis QA, not raw dataset profiling alone. When validation depends on dataset reliability checks such as freshness, grain, missingness,
duplicates, join coverage, or source mismatches, use $analyze-data-quality as a companion.

## Workflow

1. Inventory the artifact and claims.

   Identify the report, notebook, spreadsheet, SQL, dashboard, chart, pasted analysis, or recommendation being validated. Inspect source artifacts when a path, link, query, notebook, spreadsheet, or dashboard is referenced. Extract the main question, audience, decision, key claims, headline numbers, data sources, time windows, populations, filters, comparison baselines, and stated caveats. Verify that every metric or KPI requested by the user appears in the analysis or is explicitly marked unavailable, not applicable, or out of scope.

2. Validate the question, methodology, and assumptions.

   Confirm that the analysis answers the stated business or product question,
   not a nearby easier question. Check whether the population, eligibility rules, exclusions, sampling, metric definitions, formulas, units,
   denominators, timezones, cohorts, comparison periods, and baselines match the stakeholder decision. Flag hidden exclusions, inconsistent definitions,
   partial-period comparisons, and causal wording that lacks experimental or otherwise credible causal evidence.

3. Validate data selection and quality risks.

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