supabase

What This Skill Does

The Supabase skill for OpenClaw provides a robust interface for interacting with Supabase projects directly from your agent environment. It streamlines database operations, allowing you to perform CRUD actions, execute raw SQL queries, manage table schemas, and leverage advanced vector similarity search features using pgvector. By utilizing the Supabase CLI wrappers, this skill acts as a bridge between your natural language prompts and your cloud-hosted PostgreSQL database, effectively turning your AI agent into a database administrator and data analyst.

Installation

To integrate this skill into your OpenClaw environment, execute the following command via the CLI:

clawhub install openclaw/skills/skills/bobdevibecoder/bobagent-supabase

Once installed, ensure your project environment variables are correctly set. You must export SUPABASE_URL and SUPABASE_SERVICE_KEY in your shell configuration or current session. For advanced management tasks like table schema updates, provide the optional SUPABASE_ACCESS_TOKEN.

Use Cases

This skill is ideal for developers and data engineers looking to automate backend workflows. Common use cases include:

• Automated Data Processing: Periodically inserting records based on external trigger events.
• Vector Knowledge Bases: Performing RAG-style semantic searches on document databases using pgvector to retrieve context-aware answers.
• Schema Management: Rapidly prototyping table structures using SQL queries or inspecting data integrity.
• Application Maintenance: Batch updating statuses or cleaning up records with filtered delete operations.

Example Prompts

1. "Check the current number of active users in the users table and give me a count."
2. "Perform a vector search in the documents table for 'how to configure Supabase' and return the top 3 matches."
3. "Update the status of user id 456 to 'banned' and delete all their pending orders from the orders table."

Tips & Limitations

When using the Supabase skill, always verify your service keys have the appropriate permissions. While raw SQL allows for powerful operations, it bypasses ORM validation, so be cautious with destructive queries (e.g., DELETE, DROP). Use the built-in select command for day-to-day filtering to ensure cleaner query construction. Remember that performance in vector similarity searches depends on the efficiency of your match function and the size of your embedding dataset. Always test your queries in a staging environment before running them against production databases.