CSV Cleanroom

Purpose

Profile messy CSV files, standardize columns, detect data quality issues, and produce a reproducible cleanup plan.

Trigger phrases

• - 清洗 CSV
• profile this dataset
• 数据质量检查
• 列名规范化
• build a cleanup plan

Ask for these inputs

• - CSV file or schema
• target schema if available
• known bad values
• dedupe rules
• date/currency locale

Workflow

1. 1. Profile the CSV: row count, nulls, duplicates, type mismatches, and outliers.
2. Normalize headers and map to the target schema.
3. Generate a step-by-step cleanup plan and optional transformed output.
4. Document irreversible operations before applying them.
5. Return a quality score and remediation checklist.

Output contract

• - profile report
• normalized schema
• cleanup plan
• quality scorecard

Files in this skill

• - Script: INLINECODE0
• Resource: INLINECODE1

Operating rules

• - Be concrete and action-oriented.
• Prefer preview / draft / simulation mode before destructive changes.
• If information is missing, ask only for the minimum needed to proceed.
• Never fabricate metrics, legal certainty, receipts, credentials, or evidence.
• Keep assumptions explicit.

Suggested prompts

• - 清洗 CSV
• profile this dataset
• 数据质量检查

Use of script and resources

Use the bundled script when it helps the user produce a structured file, manifest, CSV, or first-pass draft.
Use the resource file as the default schema, checklist, or preset when the user does not provide one.

Boundaries

• - This skill supports planning, structuring, and first-pass artifacts.
• It should not claim that files were modified, messages were sent, or legal/financial decisions were finalized unless the user actually performed those actions.

Compatibility notes

• - Directory-based AgentSkills/OpenClaw skill.
• Runtime dependency declared through metadata.openclaw.requires.
• Helper script is local and auditable: scripts/csv_cleanroom.py.
• Bundled resource is local and referenced by the instructions: resources/data_quality_checklist.md.