Datasets
View, edit, filter, and export structured data tables that your agent creates and manages.
Tip: Your agent can manage datasets directly. Ask it — "Create a dataset for tracking leads", "Add a row to the contacts dataset", or "Export the research data as CSV."
Overview
Datasets are structured data tables — like spreadsheets — that agents create, populate, and transform during their work. They're the primary way agents store and organize extracted information.
Unlike traditional databases that require schema design upfront, datasets in Communa are dynamic: agents create them on the fly when they extract structured data, and schemas evolve as new columns are discovered.
How Datasets Are Created
Datasets appear through two main paths:
Auto-Capture
When an agent extracts structured data from the sandbox (screen, clipboard, files) using the data_capture tool, a dataset is created automatically. The agent defines the schema based on what it finds:
- Scraping a table from a website → dataset with matching columns
- Reading a spreadsheet → dataset mirroring the sheet's structure
- Extracting form data → dataset with field names as columns
Manual Creation
Agents can also create datasets explicitly using the data_management tool:
- "Create a dataset called 'Leads' with columns: name, email, company, status"
- The agent creates the schema and starts populating it
The Datasets Tab
The Datasets tab shows all datasets belonging to the agent.
Sidebar Navigation
A sidebar lists all datasets with their names. Click a dataset to view it in the main area. The currently selected dataset is highlighted.
The Data Table
The main area displays the selected dataset as an interactive table. You can click any cell to edit it inline, sort by any column, filter rows using the controls above the table, and select rows for bulk actions. Changes are saved when you click outside the cell or press Enter.
Data Transforms
Ask your agent to transform data within a dataset:
- "Normalize all email addresses to lowercase"
- "Extract the domain from each email"
- "Calculate the total for the revenue column"
- "Flag any rows where the status is 'overdue'"
The agent uses the data_management tool to apply transformations row by row or in bulk. You can also apply transforms through the UI's transform action.
Exporting Data
Click the Export CSV button to download the dataset as a CSV file. The export:
- Includes all columns and rows (respecting current filters)
- Uses UTF-8 encoding with BOM for Excel compatibility
- Downloads immediately to your browser
Dataset Permissions
Datasets can be protected with per-operation permissions to prevent accidental modification:
| Permission | What It Controls |
|---|---|
| Insert | Whether new rows can be added |
| Update | Whether existing rows can be modified |
| Delete | Whether rows can be removed |
| Read | Whether data can be queried |
You can also lock a dataset entirely, making it read-only until unlocked. This is useful for finalized datasets that shouldn't be modified.
Deleting Datasets
To delete a dataset, use the delete action on the dataset item in the sidebar. A confirmation dialog appears since deletion is permanent and removes all data.
Tips & Best Practices
- Let the agent create schemas — Don't pre-define columns. Tell the agent what data you need and let it design the schema based on what it finds
- Use filters liberally — Filters help you find specific data without asking the agent to search
- Export regularly — Download important datasets as CSV backups
- Lock finalized data — Use permissions to prevent accidental changes to completed datasets
- Combine with skills — Create skills that define exactly what data to capture and in what format for consistent results across runs