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