Dataset Profiler
SkillFreeProfile a messy dataset before analyzing it: types, missingness, duplicates, outliers, and the questions it cannot answer
v1.0.0MIT
npx openagents-cli add openagents/data-cleaning --runtime claude-codeDataset Profiler
Most wrong analyses are not wrong in the statistics, they are wrong in the assumptions made about the data in the first ten minutes. This skill front-loads that: what each column actually contains, where the nulls are and whether they are random, which rows are duplicates, and which questions this dataset simply cannot answer.
When to use
- Any dataset you did not create yourself.
- Before the first chart, model, or aggregate.
- When a number looks surprising and you need to know whether it is real.
Install
bash
npx openagents-cli add openagents/data-cleaning| Runtime | Installed to |
|---|---|
claude-code | .claude/skills/data-cleaning/ |
codex | .codex/skills/data-cleaning/ |
openai-agents | agents/data-cleaning/ |
generic | .openagents/data-cleaning/ |
What is in the package
SKILL.md- the profiling procedure and what to report.checks.md- the specific checks per column type, with what each failure implies.
License
MIT