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chrispangg/deepagentsdk/data-analysis

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PublishedSeptember 29, 2026 at 09:02 PM
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version: "1.0.1" name: data-analysis description: Analyze 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
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