An advanced data management skill that enables AI agents to read, write, and analyze tabular data across multiple platforms with persistent schema memory.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install spreadsheet
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install spreadsheet using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Spreadsheet?
The Spreadsheet skill is a robust utility designed for AI agents to interact with structured data across Google Sheets, Excel, and CSV formats. By integrating this into Openclaw Skills, developers provide their agents with a sophisticated memory-backed system that preserves formatting, understands complex schemas, and manages large-scale datasets efficiently. It serves as a bridge between raw data sources and actionable insights, ensuring that every interaction respects the existing structure and locale-specific requirements of the user's files.
This skill is built with a focus on reliability and data integrity. It employs a schema-first approach, meaning it documents and remembers the structure of every spreadsheet it touches. This allows for more accurate data entry and analysis over time, as the agent maintains a persistent understanding of column types, formulas, and recent updates within the local filesystem.
Spreadsheet Use Cases
- Automating the generation of financial reports by pulling data from multiple CSV sources into a master Excel file.
- Synchronizing data between local project trackers and cloud-based Google Sheets.
- Performing large-scale data analysis on datasets exceeding 10,000 rows using intelligent pagination.
- Maintaining structured inventory lists with strict column schema enforcement.
- Exporting processed data into reusable templates for consistent stakeholder updates.
How Spreadsheet Works
- The AI agent identifies a spreadsheet task and checks the local directory for existing schema definitions.
- For new files, the skill performs a schema-first scan to document column names, data types, and sample values.
- The skill selects the appropriate integration method, prioritizing Google Sheets API, then Excel (.xlsx) via openpyxl, and finally CSV.
- Large datasets are handled based on row count, utilizing sampling or targeted queries to maintain performance and avoid memory overflows.
- During write operations, the skill applies format preservation rules to match the user's locale, date preferences, and existing cell formulas.
- All structural changes and access history are logged to the local memory to optimize future interactions with the same datasets.
Spreadsheet Setup
To get started with this skill in the Openclaw Skills ecosystem, you need to initialize the local storage directory:
mkdir -p ~/spreadsheet/{projects,templates,exports}
Ensure that your environment has the necessary dependencies installed for your chosen file types, such as openpyxl for Excel handling. For Google Sheets integration, ensure your API credentials are configured according to the instructions in the google-sheets.md reference file.
Spreadsheet Data Schema & Taxonomy
The Spreadsheet skill maintains a structured memory hierarchy within the user's home directory to ensure data persistence and schema accuracy:
| Path | Purpose |
|---|---|
~/spreadsheet/memory.md |
Stores global preferences, format rules, and a log of recently accessed sheets. |
~/spreadsheet/projects/ |
Contains per-project schemas ({name}.md) including Sheet IDs and column definitions. |
~/spreadsheet/templates/ |
Stores reusable spreadsheet structures for automated report generation. |
~/spreadsheet/exports/ |
Default directory for all generated or processed files. |
Spreadsheet Advanced Features
- Schema-first logic that prevents data corruption by documenting structure before any write operation.
- Intelligent pagination and sampling for datasets over 10,000 rows to ensure agent responsiveness.
- Automatic format preservation that respects local currency, date formats, and pre-existing formulas.
- Self-verifying memory that checks for schema drift if a file hasn't been accessed in over 7 days.
- Batch request optimization for Google Sheets to remain within API rate limits (max 100 requests per 100 seconds).
SKILL.md
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