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skill CX Codex GP ChatGPT official · OpenAI

analyze-data-quality — Codex skill: what it does, how to install

Evaluates whether datasets and metric tables are reliable enough for dashboards or models. Helps catch duplicates and anomalies.

Install into Codex · Skills CLI (Vercel) All methods and platforms ↓ 
npx skills add openai/plugins --skill analyze-data-quality -a codex
Open on GitHub

What analyze-data-quality does

The skill audits structured tables before teams feed them into downstream pipelines or reports. It starts by clarifying table grain and downstream use. Next, it profiles the data. The agent checks identifier duplicates and timestamp boundaries.

Checks adjust to the data shape. For event streams, the agent flags future timestamps and session gaps. In feature tables, it checks for target leakage, while fact tables get screened for mixed grain. When analysis requires code, it defaults to a companion notebook or SQL queries via ~~structured_data.

Teams running data pipelines should install this to catch flaws before they break production dashboards.

What it helps with

  • detect duplicate primary and composite keys
  • spot target leakage in machine learning feature sets
  • catch variant imbalance in experiment assignment logs
  • track null-rate spikes and partition arrival lags

Example prompts for the agent

  • "Inspect this fact table for join blowups and mixed grain."
  • "Check our experiment assignment data for variant imbalance and duplicate exposures."
  • "Audit this feature store table for target leakage and distribution drift."

How to install analyze-data-quality

1Skills CLI (Vercel)

npx skills add openai/plugins --skill analyze-data-quality -a codex

2Manually: put the files in place

.agents/skills/analyze-data-quality/SKILL.md

For all projects use ~/.agents/skills/

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Questions about analyze-data-quality

How do I install analyze-data-quality in Codex?

Run npx skills add openai/plugins --skill analyze-data-quality -a codex in your project terminal.

What makes analyze-data-quality different from validate-data?

This skill inspects underlying datasets for structural flaws and anomalies. For auditing final charts, reports, or analytical arguments, use $validate-data.

Which data shapes does the skill support?

It includes check patterns for event streams, fact tables, dimension tables, experiment datasets, and machine learning feature stores.

See also

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