OpenClaw

llm-data-automation

Automate construction data processing using LLM (ChatGPT, Claude, LLaMA). Generate Python/Pandas scripts, extract data from documents, and create automated pipelines without deep programming knowledge.

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查看来源 ↗安装与使用 SKILL.md ↗

安装与使用

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。

SkillHub CLI

skillhub install datadrivenconstruction/llm-data-automation

打开来源仓库，查看说明和兼容环境后，按来源提供的命令或步骤安装。

将来源链接提供给你的 Agent，让它先读取说明、核对依赖，并向你说明将执行的安装步骤。

查看来源 ↗使用指南

技能说明

LLM Data Automation for Construction

Overview

Based on DDC methodology (Chapter 2.3), this skill enables automation of construction data processing using Large Language Models (LLM). Instead of manually coding data transformations, you describe what you need in natural language, and the LLM generates the necessary Python/Pandas code.

Book Reference: "Pandas DataFrame и LLM ChatGPT" / "Pandas DataFrame and LLM ChatGPT"

"LLM-модели, такие как ChatGPT и LLaMA, позволяют специалистам без глубоких знаний программирования внести свой вклад в автоматизацию и улучшение бизнес-процессов компании." — DDC Book, Chapter 2.3

Quick Start

Option 1: Use ChatGPT/Claude Online

Simply describe your data processing task in natural language:

Prompt: "Write Python code to read an Excel file with construction materials,
filter rows where quantity > 100, and save to CSV."

Option 2: Run Local LLM (Ollama)

# Install Ollama from ollama.com
ollama pull mistral

# Run a query
ollama run mistral "Write Pandas code to calculate total cost from quantity * unit_price"

Option 3: Use LM Studio (GUI)

Download from lmstudio.ai
Install and select a model (e.g., Mistral, LLaMA)
Start chatting with your local AI

Core Concepts

DataFrame as Universal Format

import pandas as pd

# Construction project as DataFrame
# Rows = elements, Columns = attributes
df = pd.DataFrame({
    'element_id': ['W001', 'W002', 'C001'],
    'category': ['Wall', 'Wall', 'Column'],
    'material': ['Concrete', 'Brick', 'Steel'],
    'volume_m3': [45.5, 32.0, 8.2],
    'cost_per_m3': [150, 80, 450]
})

# Calculate total cost
df['total_cost'] = df['volume_m3'] * df['cost_per_m3']
print(df)

LLM Prompts for Construction Tasks

Data Import:

"Write code to import Excel file with construction schedule,
parse dates, and create a Pandas DataFrame"

Data Filtering:

来源信息

Source: github
Repository: openclaw/skills
Path: skills/datadrivenconstruction/llm-data-automation
Version: —
Commit: e7ddeb970396698857cd1ed645d35a34e63ea58f
分类来源: rules
状态: active
可见性: public

这些信息来自收录时的来源分析，可能滞后；使用前请核对最新来源文件。

来源内容

SKILL.md

# LLM Data Automation for Construction

## Overview

Based on DDC methodology (Chapter 2.3), this skill enables automation of construction data processing using Large Language Models (LLM). Instead of manually coding data transformations, you describe what you need in natural language, and the LLM generates the necessary Python/Pandas code.

**Book Reference:** "Pandas DataFrame и LLM ChatGPT" / "Pandas DataFrame and LLM ChatGPT"

> "LLM-модели, такие как ChatGPT и LLaMA, позволяют специалистам без глубоких знаний программирования внести свой вклад в автоматизацию и улучшение бизнес-процессов компании."
> — DDC Book, Chapter 2.3

## Quick Start

### Option 1: Use ChatGPT/Claude Online
Simply describe your data processing task in natural language:

```
Prompt: "Write Python code to read an Excel file with construction materials,
filter rows where quantity > 100, and save to CSV."
```

### Option 2: Run Local LLM (Ollama)
```bash
# Install Ollama from ollama.com
ollama pull mistral

# Run a query
ollama run mistral "Write Pandas code to calculate total cost from quantity * unit_price"
```

### Option 3: Use LM Studio (GUI)
1. Download from lmstudio.ai
2. Install and select a model (e.g., Mistral, LLaMA)
3. Start chatting with your local AI

## Core Concepts

### DataFrame as Universal Format
```python
import pandas as pd

# Construction project as DataFrame
# Rows = elements, Columns = attributes
df = pd.DataFrame({
    'element_id': ['W001', 'W002', 'C001'],
    'category': ['Wall', 'Wall', 'Column'],
    'material': ['Concrete', 'Brick', 'Steel'],
    'volume_m3': [45.5, 32.0, 8.2],
    'cost_per_m3': [150, 80, 450]
})

# Calculate total cost
df['total_cost'] = df['volume_m3'] * df['cost_per_m3']
print(df)
```

### LLM Prompts for Construction Tasks

**Data Import:**
```
"Write code to import Excel file with construction schedule,
parse dates, and create a Pandas DataFrame"
```

**Data Filtering:**
```...

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