data-cleaning
Installation
SKILL.md
Data cleaning
Treat cleaning as a controlled transformation of an observed dataset, not cosmetic editing. Preserve raw input, state the target use and grain, make every lossy decision explicit, and prove that the cleaned output satisfies a contract.
Route by task
| Need | Read next |
|---|---|
| End-to-end method, scope, and stopping rules | references/methodology.md |
| Choose a library or platform | references/tool-selection.md |
| Missingness, duplicates, types, ranges, categories, dates, joins | references/operations.md |
| Text, identifiers, Unicode, and entity resolution | references/text-and-entity.md |
| Schemas, contracts, validation, drift, scale | references/validation-and-scale.md |
| CLI, OpenRefine, monitoring, and interactive remediation | references/cli-and-interactive-tools.md |
| Source claims and version-sensitive caveats | references/sources.md |
| Plan, logs, exceptions, contracts, or reports | templates/cleaning-plan.md, templates/transformation-log.jsonl, templates/exception-register.csv, templates/schema-contract.yml, templates/quality-report.md |
| Lightweight profile or reconciliation | Run python3 scripts/profile_dataset.py --help or python3 scripts/reconcile_dataset.py --help |