Dbos Python

What it does

This skill enables AI agents to automate database interactions and data processing tasks using Python scripts within a dbOS environment. It provides guidelines for building reliable, fault-tolerant Python applications utilizing DBOS durable workflows, including configuring and launching applications, managing concurrency with queues, and implementing communication between workflows. The skill focuses on best practices for integrating DBOS into existing Python codebases.

When to use it

• Adding DBOS functionality to existing Python code.
• Creating new workflows and individual steps within a dbOS application.
• Implementing concurrent task processing using queues.
• Configuring and launching DBOS applications.
• Testing the behavior of DBOS applications.

Key capabilities

• Defining workflows composed of deterministic steps.
• Using @DBOS.workflow to designate workflow functions.
• Utilizing @DBOS.step to mark functions performing complex operations or accessing external services.
• Configuring and launching DBOS applications with a DBOSConfig.
• Managing concurrency using queues.
• Implementing communication between workflows via events, messages, and streams.

Example prompts

• "Create a dbOS workflow that calls an external API."
• "How do I configure my Python application to launch within DBOS?"
• "Show me an example of using a queue for concurrency control in dbOS."

Tips & gotchas

• Workflows must be deterministic; non-deterministic operations should be placed within steps.
• Avoid calling DBOS.start_workflow or DBOS.recv from within a step.
• Refer to the detailed rule files (e.g., references/lifecycle-config.md, references/workflow-determinism.md) for comprehensive explanations and examples.