data-analysis

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Analyze CSV and tabular data, create summaries, and generate insights

⭐57stars🔀5forks📁View on GitHub🕐Updated Jan 2, 2026
[Reporting]

When & Why to Use This Skill

This Claude skill automates the end-to-end process of analyzing CSV and tabular data, transforming raw datasets into actionable insights. It provides a structured workflow for reading files, performing statistical analysis, identifying data quality issues, and generating comprehensive summary reports. Ideal for users needing rapid data interpretation and professional reporting without manual spreadsheet manipulation.

Use Cases

  • Sales Performance Analysis: Quickly calculate total revenue, average order values, and identify top-selling products or seasonal trends from sales CSV exports.
  • Data Quality Auditing: Automatically detect duplicate records, missing values, and data anomalies to ensure dataset integrity before migration or further research.
  • Business Intelligence Reporting: Generate structured executive summaries from raw data, highlighting key statistics, correlations, and strategic recommendations.
  • Research Data Summarization: Parse and summarize large TSV or CSV research datasets to identify patterns, value ranges, and statistical outliers.
namedata-analysis
descriptionAnalyze CSV and tabular data, create summaries, and generate insights

Data Analysis Skill

This skill provides step-by-step workflows for analyzing tabular data (CSV, TSV, etc.).

When to Use This Skill

Use this skill when the user:

  • Wants to analyze CSV or tabular data
  • Needs data summaries or statistics
  • Asks for insights from datasets
  • Wants to parse structured data files

Workflow

1. Understand the Data Source

First, determine where the data is:

  • Is it in a file? Get the file path
  • Is it provided inline? Store it in the filesystem first
  • Does it need to be fetched? Use appropriate tools

2. Read and Parse the Data

Use read_file to load the data. Look for:

  • Column headers (first row usually)
  • Data types in each column
  • Missing or null values
  • Data format (CSV, TSV, etc.)

3. Analyze the Data

Perform these analyses based on user needs:

Basic Statistics:

  • Row count
  • Column count
  • Value ranges (min, max)
  • Missing value counts

Data Quality:

  • Check for duplicates
  • Identify anomalies
  • Validate data types

Insights:

  • Trends or patterns
  • Correlations
  • Key findings

4. Create Summary Report

Structure your summary as:

# Data Analysis Report

## Dataset Overview
- Rows: [count]
- Columns: [count]
- Columns: [list]

## Key Statistics
[Relevant statistics based on data type]

## Data Quality
[Any issues found]

## Insights
[Key findings and patterns]

## Recommendations
[Suggested next steps]

Example

User request: "Analyze this sales data: sales.csv"

Your approach:

  1. Read sales.csv using read_file
  2. Parse the CSV structure (headers, data types)
  3. Calculate: total sales, average order value, top products
  4. Check for: missing data, date ranges, outliers
  5. Generate summary report with insights

Best Practices

  • Always validate data before analysis
  • Handle missing values gracefully
  • Provide context for statistics (what do they mean?)
  • Suggest visualizations when appropriate
  • Ask clarifying questions if data structure is unclear