csv-cleanroom

Original source / Raw SKILL.md

---
name: csv-cleanroom
description: Profile messy CSV files, standardize columns, detect data quality issues,
  and produce a reproducible cleanup plan.
version: 1.1.0
metadata:
  openclaw:
    requires:
      bins:
      - python3
    emoji: 🧰
---

# 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`.


---

## Referenced Files

> The following files are referenced in this skill and included for context.

### scripts/csv_cleanroom.py

```python
#!/usr/bin/env python3
import argparse, csv, json, statistics

def main():
    ap = argparse.ArgumentParser()
    ap.add_argument("csv_path")
    ap.add_argument("--out", default="csv_profile.json")
    args = ap.parse_args()
    with open(args.csv_path, "r", encoding="utf-8-sig", newline="") as f:
        reader = csv.DictReader(f)
        rows = list(reader)
    fields = reader.fieldnames or []
    null_counts = {field: 0 for field in fields}
    for row in rows:
        for field in fields:
            if row.get(field, "") in ("", None):
                null_counts[field] += 1
    out = {"rows": len(rows), "columns": fields, "null_counts": null_counts}
    json.dump(out, open(args.out, "w", encoding="utf-8"), ensure_ascii=False, indent=2)
    print(f"Wrote {args.out}")

if __name__ == "__main__":
    main()

```

### resources/data_quality_checklist.md

```markdown
# Data Quality Checklist

- Header consistency
- Null handling rules
- Duplicate record policy
- Date and timezone normalization
- Currency/unit normalization
- Referential integrity assumptions
- Allowed value domains
- Export / archival plan

```



---

## Skill Companion Files

> Additional files collected from the skill directory layout.

### README.md

```markdown
# CSV Cleanroom

Slug: `csv-cleanroom`

## 功能定位

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

## 适用场景

- 当用户需要：清洗 CSV
- 当用户手头已有原始材料，需要快速整理成可执行输出
- 当用户希望先预览方案、再决定是否落盘或批量处理

## 安装要求

- OpenClaw / AgentSkills 兼容目录结构
- `python3` 可执行文件在 PATH 中可用
- 无远程安装脚本、无隐藏联网依赖、无未声明凭据要求

## 目录结构

- `SKILL.md`：触发描述、工作流、输出契约
- `scripts/csv_cleanroom.py`：本地辅助脚本
- `resources/data_quality_checklist.md`：被 SKILL/README 引用的资源文件
- `examples/example-prompt.md`：触发与输入示例
- `tests/smoke-test.md`：最小冒烟测试
- `SELF_CHECK.md`：规范与安全自检
- `CHANGELOG.md`：变更记录

## 触发示例

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

## 建议输入

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

## 预期输出

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

## 辅助脚本

脚本：`scripts/csv_cleanroom.py`

建议先运行帮助信息确认参数：

```bash
python3 scripts/csv_cleanroom.py --help
```

该脚本设计原则：

- 本地执行，便于审计与回滚
- 输入输出路径显式传入
- 不使用 `curl|bash`、远程直灌、base64 混淆执行
- 仅处理用户明确提供的文件或目录

## 输入输出示例

输入示例见：`examples/example-prompt.md`

输出示例建议至少包含：

- 结构化主结果
- 未决问题 / 风险项
- 可交付给他人的摘要或清单

## 常见问题

### 1. 这个 skill 会直接改我的文件吗？

默认不应直接进行破坏性批量操作；应优先生成预览、清单或草案，只有在用户明确要求时才建议执行进一步动作。

### 2. 这个 skill 需要联网吗？

当前目录内未声明联网依赖，也没有内置远程下载步骤。是否联网应由具体会话任务决定，而不是由 skill 包本身强制触发。

### 3. 资源文件的作用是什么？

`resources/data_quality_checklist.md` 为脚本或说明提供模板、规则、清单或模式参考，便于输出格式统一、可复用、可审计。

## 风险提示

- 对用户提供的数据、文本、截图或本地文件进行整理时，应先确认范围与目标。
- 涉及重命名、移动、合并、覆盖、生成正式对外内容时，应先给预览版本。
- 对不确定字段使用“待确认”标记，不应编造事实。

## 安全审计结论

- 依赖边界：仅声明 `python3`
- 凭据边界：未声明环境变量依赖
- 执行边界：本地脚本、本地资源、显式输入
- 高风险模式检查：未引入 `curl|bash`、远程管道执行、混淆载荷或私有 API 绑定

```

### _meta.json

```json
{
  "owner": "52yuanchangxing",
  "slug": "csv-cleanroom",
  "displayName": "csv-cleanroom",
  "latest": {
    "version": "1.0.0",
    "publishedAt": 1773300725224,
    "commit": "https://github.com/openclaw/skills/commit/80623976314bf072322f6e6880396e990e44f84b"
  },
  "history": []
}

```