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Skills csv-cleanroom

Profile messy CSV files, standardize columns, detect data quality issues,

install
source · Clone the upstream repo
git clone https://github.com/openclaw/skills
Claude Code · Install into ~/.claude/skills/
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/52yuanchangxing/csv-cleanroom" ~/.claude/skills/clawdbot-skills-csv-cleanroom && rm -rf "$T"
manifest: skills/52yuanchangxing/csv-cleanroom/SKILL.md
source content

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. 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: 
    {baseDir}/scripts/csv_cleanroom.py
  • Resource: 
    {baseDir}/resources/data_quality_checklist.md

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
    .