A collection of Claude Code Skills for developers. Boosts productivity and content creation. Works with Claude Code v2.0 and above. Includes tools for automation and efficiency.
git clone https://github.com/wwwzhouhui/skills_collection.gitA collection of Claude Code Skills for developers. Boosts productivity and content creation. Works with Claude Code v2.0 and above. Includes tools for automation and efficiency.
1. **Identify the task**: Determine which repetitive task you want to automate (e.g., report generation, data cleaning, file organization). Use tools like `Claude Code` in your IDE or terminal. 2. **Define requirements**: Specify inputs, outputs, and edge cases. For example: 'I need a script that processes CSV files in [INPUT_FOLDER] and generates summary statistics saved to [OUTPUT_FOLDER].' Include any constraints like file formats or data sources. 3. **Write the script**: Use the prompt template to generate a script. For complex tasks, break it into smaller functions. Test with sample data first. 4. **Run and iterate**: Execute the script in your terminal (e.g., `python script.py`) or IDE. Review the output and refine the script based on errors or unexpected results. 5. **Integrate into workflow**: Schedule the script to run automatically (e.g., using cron jobs or GitHub Actions) or trigger it manually when needed. Monitor performance and adjust as your requirements evolve.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/wwwzhouhui/skills_collectionCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Use Claude Code to automate [TASK] by writing a script that [SPECIFIC_ACTION]. The script should handle [EDGE_CASES] and output results to [OUTPUT_LOCATION]. Example: 'Use Claude Code to automate the generation of monthly sales reports by writing a Python script that fetches data from [DATABASE], calculates key metrics, and saves the report as a PDF to [REPORT_FOLDER].'
```python
# Monthly Sales Report Generator
# Automates report generation for Q3 2024 sales data
import pandas as pd
from datetime import datetime
import matplotlib.pyplot as plt
from fpdf import FPDF
# Fetch sales data from PostgreSQL database
sales_data = pd.read_sql(
"""
SELECT product_id, region, sales_rep, amount, date
FROM sales
WHERE date BETWEEN '2024-07-01' AND '2024-09-30'
""",
con="postgresql://user:pass@localhost:5432/sales_db"
)
# Calculate key metrics
total_sales = sales_data['amount'].sum()
avg_sale = sales_data['amount'].mean()
top_region = sales_data.groupby('region')['amount'].sum().idxmax()
top_rep = sales_data.groupby('sales_rep')['amount'].sum().idxmax()
# Generate visualizations
plt.figure(figsize=(10, 6))
sales_data.groupby('date')['amount'].sum().plot(title='Daily Sales Q3 2024')
plt.savefig('daily_sales_q3.png')
# Create PDF report
class PDF(FPDF):
def header(self):
self.set_font('Arial', 'B', 12)
self.cell(0, 10, 'Q3 2024 Sales Report', 0, 1, 'C')
pdf = PDF()
pdf.add_page()
pdf.set_font('Arial', '', 12)
pdf.cell(0, 10, f'Generated: {datetime.now().strftime("%Y-%m-%d %H:%M")}', 0, 1)
pdf.cell(0, 10, f'Total Sales: ${total_sales:,.2f}', 0, 1)
pdf.cell(0, 10, f'Average Sale: ${avg_sale:,.2f}', 0, 1)
pdf.cell(0, 10, f'Top Region: {top_region}', 0, 1)
pdf.cell(0, 10, f'Top Sales Rep: {top_rep}', 0, 1)
pdf.image('daily_sales_q3.png', x=10, y=60, w=190)
pdf.output('Q3_2024_Sales_Report.pdf')
print("Report generated successfully at Q3_2024_Sales_Report.pdf")
```
**Output Summary:**
- Generated Q3 2024 sales report with total sales of $1,245,872.34
- Identified top region as 'North America' ($456,789.12) and top rep as 'Sarah Johnson' ($234,567.89)
- Created visualizations showing daily sales trends
- Saved PDF report to 'reports/Q3_2024_Sales_Report.pdf'AI assistant built for thoughtful, nuanced conversation
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