Claude-meta-skill

🥈Silver

A curated collection of reusable skills for Claude Code. Enhance Claude's capabilities with ready-to-use skill modules including comprehensive guides, templates, and best practices for creating your own skills.

209200Updated Today

Intermediate30min to implementautomation

Saves ~80 min per use

Quick InstallView Source

git clone https://github.com/YYH211/Claude-meta-skill.git

Works with:

Claude

Overview

About This Skill

Setup & Installation

Quick Install

No install command available. Check the GitHub repository for manual installation instructions.

Alternative Install (Git Clone)

git clone https://github.com/YYH211/Claude-meta-skill

Requirements

Claude Code or compatible AI agent
Works with: Claude

Quick Start Guide

Install the Skill

Copy the install command above and run it in your terminal.

Open Your AI Agent

Launch Claude Code, Cursor, or your preferred AI coding agent.

Try It Out

Use the prompt template or examples below to test the skill.

Customize

Adapt the skill to your specific use case and workflow.

Usage Examples

Prompt Template

I want to use the Claude-meta-skill to [SPECIFIC TASK, e.g., 'automate data analysis']. Provide me with a ready-to-use skill module, including a guide, template, and best practices for this task. [COMPANY] is in the [INDUSTRY] industry and needs to [SPECIFIC NEED, e.g., 'process customer feedback data'].

Example Output

# Skill Module: Automated Data Analysis for Customer Feedback

## Guide
1. **Data Collection**: Gather customer feedback data from sources like surveys, reviews, and social media.
2. **Data Cleaning**: Remove duplicates, correct errors, and standardize formats.
3. **Analysis**: Use natural language processing to identify trends, sentiments, and key themes.
4. **Visualization**: Create charts and graphs to present findings clearly.
5. **Reporting**: Generate a comprehensive report with actionable insights.

## Template
```python
# Example Python code for automated data analysis
import pandas as pd

# Load data
data = pd.read_csv('customer_feedback.csv')

# Clean data
data = data.drop_duplicates()

# Analyze data
sentiment_analysis = data['feedback'].apply(lambda x: analyze_sentiment(x))
```

## Best Practices
- Ensure data privacy and compliance with regulations.
- Regularly update analysis models to improve accuracy.
- Involve stakeholders in defining key metrics and KPIs.
- Automate reporting to save time and reduce manual errors.