FlowSync
Open App
中文
FreeData5 nodes
Score 69

Data Cleaning Automation

Drop messy CSVs and JSON — Pandas-powered pipeline auto-detects encoding, normalises columns, fills gaps, deduplicates, and flags anomalies before landing clean output.

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What does this workflow do?

数据源格式自动识别 → 全表扫描（重复率/缺失率/类型推断/异常值检测/分布分析）→ AI 清洗策略生成（基于数据特征自动选最优方案）→ 自动执行（去重/填充/类型修正/异常截断/标准化）→ 清洗前后对比验证 → 数据质量评分 → 变更日志追溯 → 双表输出（清洁数据+清洗记录）

System flow

数据源格式自动识别 → 全表扫描（重复率/缺失率/类型推断/异常值检测/分布分析）→ AI 清洗策略生成（基于数据特征自动选最优方案）→ 自动执行（去重/填充/类型修正/异常截断/标准化）→ 清洗前后对比验证 → 数据质量评分 → 变更日志追溯 → 双表输出（清洁数据+清洗记录）

Pipeline breakdown

Pandas xlsx LLM Reasoning json Pandas json OpenPyXL json Python-DOCX json
  1. Pandas pandas — Tabular compute — join, aggregate, validate and reshape the extracted records xlsx → json
  2. LLM Reasoning llm — Large-language-model step that extracts, classifies, validates or writes structured output json → json
  3. Pandas pandas — Tabular compute — join, aggregate, validate and reshape the extracted records json → json
  4. OpenPyXL openpyxl — Writes a formatted, formula-ready Excel workbook as the deliverable json → xlsx
  5. Python-DOCX python-docx — Renders the final Word document with headings, tables and styling json → docx

Workflow Reputation Score

Based on executions, success rate, update frequency, component quality, and user rating.

69Overall
★★★★★ 4.2
▶Executions
25
✓Success rate
96
↻Update frequency
65
⚙Component quality
88
★User rating
84

Based on executions, success rate, update frequency, component quality, and user rating.

Components used

Pandaspandas

Tabular compute — join, aggregate, validate and reshape the extracted records

pandas-dev/pandas ↗
LLM Reasoningllm

Large-language-model step that extracts, classifies, validates or writes structured output

FlowSync proprietary
OpenPyXLopenpyxl

Writes a formatted, formula-ready Excel workbook as the deliverable

theorchard/openpyxl ↗
Python-DOCXpython-docx

Renders the final Word document with headings, tables and styling

python-openxml/python-docx ↗

How is this different from n8n or Zapier?

Unlike generic workflow builders, FlowSync workflows ship pre-configured with AI skills baked into every node — OCR, LLM reasoning, transcription, super-resolution — not just webhook triggers. Each node is white-box auditable: you see input, output, and config. Deployment is instant — no self-hosting, no API-key per node.

Use cases

  • 月度经营数据清洗
  • 多源数据合并去重
  • 数据仓库 ETL 预处理
  • 审计数据质量检查

Author & Source

This workflow is maintained by the FlowSync team. All components are open-source or commercially licensed. Source code for each component is linked in the Components section above.

Pricing: Free tier available · FREE tier · Official

Field notes

以前用 OpenRefine 要学 Java 表达式。这个模板上传就出清洁数据，质量报告还能直接发邮件给业务方。

— 数据分析师 Tom, 电商数据分析

合并 12 个部门报表，格式不一、重复严重。清洗后直接能用，变更记录可追溯。

— 财务 李, 财务主管

Data handling

上传的数据仅在内存中处理，不在服务器硬盘上存储原始文件。清洁数据由您选择是否保存。我们不会查看或使用您的业务数据。

FlowSync Workflow Marketplace · 69 reputation score · 25% execution · 96% success · 5-node white-box pipeline · maintained by FlowSync Official

Ready to run?

Drop messy CSVs and JSON — Pandas-powered pipeline auto-detects encoding, normalises columns, fills gaps, deduplicates, and flags anomalies before landing clean output.

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