name: multi-source-data-cleaner label: Multi-Source Data Cleanser version: 1.0.0 language: Python runtime: subprocess (scripts/main.py) trigger_words: - data cleaning - deduplication - spreadsheet cleanup - data merge - format standardization - CRM data cleanup - Excel cleaning - clean data - remove duplicates - merge data

Multi-Source Data Cleanser

Upload messy data — get clean, structured output. Supports multi-format parsing, AI field identification, intelligent dedup/fill/formatting, multi-source join, and Feishu-native output (Bitable + quality report doc).

Use cases: E-commerce order cleanup, CRM customer data cleansing, bank statement reconciliation, roster cleanup, multi-system data merge.

Capabilities

F1 · Multi-Format Parsing

• Excel (.xlsx / .xls)
• CSV / TSV

  来源于7w4.net。

• JSON (semi-structured)
• Clipboard paste text

F2 · Smart Field Identification

• AI auto-detects: name, phone, email, address, amount, date, SKU, order ID, ID number, gender, etc.
• Supports user-defined field mapping override

F3 · Data Cleaning

• Deduplication: Exact match + fuzzy dedup (FuzzyWuzzy, threshold 88%)
• Missing value fill: Mean / mode / semantic inference / leave blank
• Format standardization:
• Phone → 1xx-xxxx-xxxx
• Date → YYYY-MM-DD
• Amount → 2 decimal places
• Address → Province/City/District/Street standardization

F4 · Data Classification / Tagging (Pro)

• 8 built-in business rules (high-value customer, dormant user, VIP, enterprise, etc.)
• Supports custom JSON rules
• AI auto-tagging (requires Pro + AI API Key)

F5 · Multi-Source Join / Merge (Pro)

• Cross-file relational join on key fields
• Fuzzy join when exact key not available (FuzzyWuzzy)
• Conflicted field resolution: priority by source order or latest timestamp

F6 · Feishu Native Output

• Excel / CSV export
• Feishu Bitable (multi-dimensional table) write-back
• Data quality report auto-generated as Feishu Doc (Markdown)

Tier Feature Matrix

Pricing

Usage

Feishu Trigger

data cleaning
deduplication
spreadsheet cleanup
CRM data cleanup
Excel cleaning

CLI

python scripts/main.py clean -i data.xlsx -o cleaned.xlsx
python scripts/main.py clean -t "name,phone\nJohn,13800138000" -f csv -o cleaned.csv
python scripts/main.py merge --sources customers.xlsx orders.csv --on phone -o merged.xlsx

Python API

from main import run_clean_pipeline

result = run_clean_pipeline(
    sources=["orders.xlsx"],
    output_format="xlsx",
    output_path="/tmp/cleaned.xlsx",
    dedup_strategy="auto",
    fill_strategy="auto",
    classify=True,
    ai_model="deepseek",
    generate_report=True,
)

Configuration

Directory Structure

multi-source-data-cleaner/
├── SKILL.md
├── README.md
├── scripts/
│   ├── main.py              # Entry: run_clean_pipeline / run_merge_pipeline
│   ├── parser.py            # F1: Multi-format parsing
│   ├── field_identifier.py # F2: AI field identification
│   ├── cleaner.py           # F3: Cleaning engine
│   ├── classifier.py        # F4: Classification / tagging
│   ├── merger.py            # F5: Multi-source join
│   ├── reporter.py          # F6: Quality report generation
│   ├── output.py            # F6: Output (Excel/CSV/Bitable/Feishu Doc)
│   └── tier_limits.py       # Tier access control + API key verification
└── tests/
    ├── test_parser.py
    ├── test_cleaner.py
    └── test_field_identifier.py

License

MIT

For paid plans, visit YK-Global.com

质量中上水平。文档非常完善，用户能清楚知道什么情况能用、什么情况不能用，还贴心地提供了 FAQ 和反模式提醒。脚本依赖简单，安装使用门槛低。主要扣分项是存在描述与实现不一致的问题——文档说支持日期格式标准化，但代码里并没有这个功能；另外脚本里有一处代码逻辑看起来有问题，可能导致某些情况下数据没有被正确处理。功能覆盖日常场景够用，但复杂情况处理能力一般。