csv-data-summarizer-claude-skill
🥈SilverAutomatically analyzes CSV files to generate summary statistics, detect missing data, and create quick visualizations. Operations teams benefit from faster data insights and improved data quality checks. Connects to Python and pandas for data processing.
254350Updated Today
Intermediate30min to implementautomation
Saves ~45 min per use
Quick InstallView Source
git clone https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill.gitWorks with:
Claude
Overview
About This Skill
Automatically analyzes CSV files to generate summary statistics, detect missing data, and create quick visualizations. Operations teams benefit from faster data insights and improved data quality checks. Connects to Python and pandas for data processing.
Tags
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/coffeefuelbump/csv-data-summarizer-claude-skillRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
Analyze the attached CSV file named [FILENAME]. Provide a summary of key statistics, identify any missing or inconsistent data, and generate a simple visualization. The data represents [DATA_TYPE] for [COMPANY] in the [INDUSTRY] sector. Focus on [SPECIFIC_ASPECT] if relevant.
Example Output
# CSV Data Analysis Summary ## Key Statistics - **Total Rows**: 1,250 - **Columns**: 8 (Date, Product ID, Quantity, Price, Region, Salesperson, Discount, Profit) - **Average Price**: $45.23 - **Median Quantity**: 5 units - **Total Profit**: $28,450.00 ## Missing Data - **Missing Values**: 12 entries in 'Discount' column (1% of total) - **Inconsistent Data**: 3 entries with negative 'Quantity' values ## Visualization  ## Recommendations - Investigate negative quantity entries - Consider imputing missing discount values with regional averages
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