Data Merge Join logo

Data Merge Join

Organization
cas-bigdatalab
data_merge_join

数据横向关联工具。读取多个结构化数据文件,按指定键进行横向关联(JOIN),输出关联后的合并文件。 当用户提到数据关联、表连接、横向合并、键合并、JOIN等需求时使用此skill。 即使用户没有明确说出"关联",只要任务涉及按某个字段将多个表的数据关联在一起,就应该使用此skill。 不负责数据纵向拼接(concat)、数据去重、或字段值聚合(GROUP BY)。

Overview

Publishercas-bigdatalab
Repositorypiflow
Skill namedata_merge_join
Stars
541
Forks
172
Bundled files
3
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by cas-bigdatalab on GitHub. Read the source before you install it.

Installation

Install the Data Merge Join AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/cas-bigdatalab/piflow/tree/master/workspace/skills/data_merge_join
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/cas-bigdatalab/piflow.git /tmp/piflow
mkdir -p .claude/skills
cp -r /tmp/piflow/workspace/skills/data_merge_join .claude/skills/data_merge_join
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Data Merge Join in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Data Merge Join on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Data Merge Join is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Data Merge Join 数据横向关联 Skill

功能概述

本skill用于将多个结构化数据文件按指定键进行横向关联(JOIN),支持内连接(inner)、左连接(left)、右连接(right)、全连接(outer)四种方式。多文件关联时按顺序依次进行两两关联,重名列自动添加后缀 _1、_2 以避免冲突。

文件1:          文件2:          关联结果(inner join on key):
| key | A |     | key | B |     | key | A | B |
|-----|---|     |-----|---|     |-----|---|---|
| k1  | 1 |     | k1  | x |     | k1  | 1 | x |
| k2  | 2 |     | k3  | y |

触发条件

当用户请求以下任务时,应使用此skill:

  • 数据关联/表连接
  • 横向合并/键合并
  • JOIN操作
  • 按字段关联数据

核心参数说明

必需参数

参数说明
--input_files输入文件路径列表,多个文件用逗号分隔
--output输出文件路径
--join_key关联键字段名,必须在所有输入文件中存在

可选参数

参数说明默认值
--join_how关联方式:inner/left/right/outerinner
--use_dask是否使用Dask处理大数据False
--blocksizeDask分块大小64MB

输入文件格式

支持 CSV、TSV、Excel(.xls/.xlsx)、SPSS(.sav)格式的结构化数据文件,每个文件必须包含关联键字段。

示例 employees.csv:

csv
employee_id,name,department_id,salary,city
E001,Alice,SALES,8000,Beijing
E002,Bob,ENG,12000,Shanghai

示例 departments.csv:

csv
department_id,dept_name,manager,location
SALES,Sales Department,Alice,Beijing
ENG,Engineering Department,Bob,Shanghai

使用方法

内连接(只保留两表都匹配的行)

bash
python scripts/run_data_merge_join.py \
  --input_files "file1.csv,file2.csv" \
  --output merged.csv \
  --join_key "id" \
  --join_how inner

左连接(保留左表所有行)

bash
python scripts/run_data_merge_join.py \
  --input_files "main.csv,lookup.csv" \
  --output merged.csv \
  --join_key "user_id" \
  --join_how left

右连接(保留右表所有行)

bash
python scripts/run_data_merge_join.py \
  --input_files "main.csv,lookup.csv" \
  --output merged.csv \
  --join_key "dept_id" \
  --join_how right

全连接(保留两表所有行)

bash
python scripts/run_data_merge_join.py \
  --input_files "table_a.csv,table_b.csv" \
  --output merged.csv \
  --join_key "key" \
  --join_how outer

输出示例

命令行输出:

[OK] Data merge completed!
   Engine: pandas
   Merge type: join
   Input files: 2
   Total rows before merge: 10, 5
   Total rows after merge: 8
   Total columns after merge: 8
   Output file: merged.csv

输出CSV格式:

csv
employee_id,name,department_id,salary,city,dept_name,manager,location
E001,Alice,SALES,8000,Beijing,Sales Department,Alice,Beijing
E002,Bob,ENG,12000,Shanghai,Engineering Department,Bob,Shanghai

环境要求

本skill依赖 pandas 及可选依赖 dask、chardet:

bash
pip install pandas openpyxl xlrd xlwt chardet
# 可选:大数据场景
pip install dask[dataframe]

注意事项

  1. join_key必须在所有输入文件中存在,否则程序会报错
  2. 多文件关联时,按顺序依次进行两两关联
  3. 重名列会自动添加后缀 _1、_2 等以避免冲突
  4. 不负责纵向拼接堆叠(concat),如需纵向合并请使用其他skill

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Data Merge Join AI skill do?

数据横向关联工具。读取多个结构化数据文件,按指定键进行横向关联(JOIN),输出关联后的合并文件。 当用户提到数据关联、表连接、横向合并、键合并、JOIN等需求时使用此skill。 即使用户没有明确说出"关联",只要任务涉及按某个字段将多个表的数据关联在一起,就应该使用此skill。 不负责数据纵向拼接(concat)、数据去重、或字段值聚合(GROUP BY)。

Why use Data Merge Join on TypingMind?

Because you install it once and use it with any model. Data Merge Join is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Data Merge Join in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/cas-bigdatalab/piflow/tree/master/workspace/skills/data_merge_join. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Data Merge Join?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Data Merge Join?

As many as you like. As long as a model supports skills, you can use Data Merge Join with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Data Merge Join AI skill free?

It is published on GitHub by cas-bigdatalab. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇