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