Lecture 9: OpenClaw Practice: Develop Data Processing Skill

Build a complete data processing Skill hands-on, master OpenClaw practical techniques.

1. Project Goal

Develop "Data Analyst" Skill, functions include:

  • 📊 Data cleaning and preprocessing
  • 📈 Data statistical analysis
  • 📉 Visualization chart generation
  • 📄 Analysis report export

2. Project Structure

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data-analyst-skill/
├── skill.yaml
├── main.py
├── tools/
│   ├── __init__.py
│   ├── data_cleaner.py
│   ├── analyzer.py
│   └── visualizer.py
├── templates/
│   └── report_template.html
├── requirements.txt
└── tests/
    └── test_analyzer.py

3. Core Code

3.1 Skill Main Program

# main.py
from openclaw import Skill
from tools.data_cleaner import DataCleaner
from tools.analyzer import DataAnalyzer
from tools.visualizer import Visualizer

class DataAnalystSkill(Skill):
    def __init__(self):
        super().__init__()
        self.cleaner = DataCleaner()
        self.analyzer = DataAnalyzer()
        self.visualizer = Visualizer()

        self.register_intent("analyze_data", self.analyze)
        self.register_intent("clean_data", self.clean)
        self.register_intent("visualize", self.visualize)

    def analyze(self, file_path, analysis_type="overview"):
        """Data analysis"""
        df = self.cleaner.load(file_path)
        result = self.analyzer.analyze(df, analysis_type)
        return result

    def clean(self, file_path, operations=None):
        """Data cleaning"""
        df = self.cleaner.load(file_path)
        df = self.cleaner.clean(df, operations)
        return {"cleaned_file": self.cleaner.save(df)}

    def visualize(self, file_path, chart_type="bar"):
        """Data visualization"""
        df = self.cleaner.load(file_path)
        chart = self.visualizer.create_chart(df, chart_type)
        return {"chart": chart}

if __name__ == "__main__":
    skill = DataAnalystSkill()


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