OpenClaw Data Cleaning & Reporting

Background

Data teams often spend time on:

• Inconsistent field formats (dates, currencies, units)
• Duplicates and missing values
• Spotty anomaly detection
• Repetitive report drafting

This use case delivers standardize → detect → report.

Key steps

1. Define normalization rules: date format, currency, null handling.
2. Process batch: apply rules, flag duplicates, mark missing fields.
3. Detect anomalies: outliers, unexpected patterns, cross-field checks.
4. Generate report: summary, flagged rows, recommended actions.

Workflow sketch

Raw data → Normalize fields → Mark duplicates/missing → Anomaly detection → Report output

Skill recommendations

openclaw skills install @clawhub/summarize

Use file-read and spreadsheet skills if available. Keep rules in config or workspace so they can be audited.

Expected results

• Normalized fields and consistent format
• Duplicates and missing values clearly marked
• Anomaly notes in report
• Report template ready for daily or weekly runs

Common pitfalls

• Changing rules without documenting impact
• Trusting anomaly detection without human review for high-stakes data
• No rollback or versioning for rule changes

Related pages

• Data report templates

• Research report system

• Config templates

• Official docs → docs.openclaw.ai

• Issues → GitHub Issues