Data Visualization & Analysis Skill - User Guide
Enable your AI agents to analyze data and create visual charts from CSV, Excel, and other data formats using Python.
Overview
The Data Visualization & Analysis skill empowers your Swiftask AI agents to perform data analysis and generate visual charts directly within your conversations. This ready-to-use skill leverages Python's powerful data processing libraries to transform raw data into actionable insights through graphs, charts, and visualizations.
Key capabilities:
Analyze data from CSV, Excel, and other formats
Generate charts (bar, line, pie, scatter plots, etc.)
Perform statistical analysis
Create custom visualizations based on your requirements
Return high-quality chart images directly in chat
Prerequisites
Before using this feature, ensure you have:
An active Swiftask account with agent creation permissions
An existing AI agent or the ability to create a new one
Data files ready for analysis (CSV, Excel, or similar formats)
Basic understanding of your data structure
Step-by-Step Setup Guide
Step 1: Access Agent Configuration
Navigate to your Swiftask workspace
Open the Agents section from the main menu
Select an existing agent or create a new one
Click on the agent to open its configuration settings
Step 2: Add the Data Visualization Skill
In the agent configuration panel, locate the Skills section
Click on "Ready-to-Use Skills" or "Browse Skills"
Use the search bar to find: "Data Visualization" or "Python Code Execution"
Click "Add" or "+" button next to the skill
Confirm the skill has been added to your agent's capabilities
Step 3: Save Configuration
Review your agent's skills list to ensure the Data Visualization skill appears
Click "Save" or "Update Agent" to apply changes
Wait for confirmation that the agent has been updated successfully
Step 4: Start Using the Skill
Create a new chat with your configured agent
Upload your data file (CSV, Excel, etc.) or provide data inline
Request a specific visualization or analysis
The agent will process your data and return chart images
Practical Use Cases
Use Case 1: Sales Performance Analysis
Scenario: You have a CSV file with monthly sales data and want to visualize trends.
Request example:
Expected output: A line chart image displaying revenue progression over time with labeled axes and legend.
Use Case 2: Customer Demographics Breakdown
Scenario: Understand your customer base distribution by age group.
Request example:
Expected output: A pie chart with percentage breakdowns and color-coded segments.
Use Case 3: Comparative Analysis
Scenario: Compare performance metrics across different departments.
Request example:
Expected output: A grouped or stacked bar chart with clear department labels and metrics.
Use Case 4: Statistical Summary
Scenario: Get quick statistical insights from a dataset.
Request example:
Expected output: Text summary with statistics followed by a histogram visualization.
Best Practices & Tips
Data Preparation
Ensure your data files are clean and well-structured
Use clear column headers that describe the data
Remove unnecessary rows or columns before uploading
Check for missing values or inconsistencies
Effective Prompting
Be specific about the type of chart you want (line, bar, pie, scatter, etc.)
Mention which columns or fields to visualize
Specify any particular formatting preferences (colors, labels, titles)
Request multiple visualizations in separate messages for clarity
File Format Recommendations
CSV: Best for simple tabular data
Excel (.xlsx): Ideal for multi-sheet workbooks with complex data
Ensure files are not password-protected
Keep file sizes reasonable for faster processing
Optimization Tips
For large datasets, specify the exact data range you want to visualize
Request summary statistics before creating visualizations
Save frequently used chart configurations by noting successful prompts
Combine multiple related visualizations in a single analysis session
Troubleshooting
Problem: Agent doesn't recognize the data file
Solutions:
Verify the file format is supported (CSV, Excel)
Check that the file uploaded successfully
Try re-uploading the file
Ensure the file is not corrupted
Problem: Chart doesn't display as expected
Solutions:
Be more specific in your visualization request
Check that the column names in your request match those in the data
Request a data preview first to verify structure
Try a different chart type that better suits your data
Problem: "Error executing Python code" message
Solutions:
Simplify your request and try again
Check for special characters or formatting issues in your data
Ensure your data doesn't contain incompatible data types
Contact support if the error persists
Problem: Skill not appearing in agent configuration
Solutions:
Refresh the skills catalog
Check your account permissions
Verify your subscription plan includes custom skills
Clear browser cache and try again
Technical Details
Execution Environment
Python code runs in a secure E2B sandbox environment
Isolated execution ensures data security and system stability
No code persists after execution completes
Available Libraries
The skill includes access to popular Python data analysis libraries:
pandas: Data manipulation and analysis
matplotlib: Chart and graph creation
seaborn: Statistical data visualization
numpy: Numerical computing
Output Format
Charts are returned as PNG images
High-resolution output suitable for reports and presentations
Images are displayed directly in the chat interface
Frequently Asked Questions
Q: What file size limit applies to uploaded data? A: File size limits depend on your subscription plan. Generally, files up to 10MB are supported for most plans.
Q: Can the agent create multiple charts from one dataset? A: Yes, you can request multiple visualizations in sequence or ask for a comprehensive analysis with several charts.
Q: Is my data secure when using this skill? A: Yes, all data processing occurs in isolated sandbox environments and is not stored after execution.
Q: Can I customize chart colors and styles? A: Yes, include specific styling preferences in your request (e.g., "use blue and green colors" or "create a minimalist chart").
Q: Does this work with real-time data sources? A: When combined with Data Connectors, you can analyze data from connected sources like Google Sheets or databases.
Last updated: December 2025 Feature availability: All plans with agent creation capabilities
Last updated