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name: robocorp-cursor-rules description: Guidelines for building RoboCorp RPA automation with Python, emphasizing functional programming, Pydantic validation, and async operations. metadata: maintainer: Mindrally source: https://github.com/Mindrally/skills

RoboCorp Python Development

You are an expert in Python and RoboCorp RPA development.

Core Guidelines

Key Principles

• Write concise, technical responses with accurate Python examples
• Emphasize functional, declarative programming while avoiding classes
• Prioritize iteration and modularization over code duplication
• Use descriptive variable names with auxiliary verbs (e.g., is_active, has_permission)
• Adopt lowercase with underscores for directories/files (e.g., tasks/data_processing.py)
• Favor named exports for utility functions and task definitions
• Implement the Receive an Object, Return an Object (RORO) pattern

Python/RoboCorp Standards

• Use def for pure functions and async def for asynchronous operations
• Include type hints for all function signatures
• Prefer Pydantic models over raw dictionaries for input validation
• Structure files with: exported tasks, sub-tasks, utilities, static content, types

Error Handling and Validation

• Handle errors and edge cases at the beginning of functions
• Use early returns for error conditions to avoid deeply nested statements
• Place the happy path last for improved readability
• Implement guard clauses for preconditions and invalid states
• Provide proper error logging and user-friendly messages
• Use custom error types for consistent handling

RoboCorp-Specific Guidelines

• Use functional components (plain functions) and Pydantic models
• Create declarative task definitions with clear return type annotations
• Minimize lifecycle event handlers; prefer context managers
• Employ middleware for logging, error monitoring, and optimization
• Optimize performance using async functions for I/O-bound tasks
• Use specific exceptions like RPA.HTTP.HTTPException for expected errors
• Apply Pydantic's BaseModel for consistent input/output validation

Performance Optimization

• Minimize blocking I/O operations; use asynchronous operations for all database calls
• Implement caching for static and frequently accessed data using Redis or in-memory stores
• Optimize data serialization/deserialization with Pydantic
• Use lazy loading techniques for large datasets

Key Conventions

1. Rely on RoboCorp's dependency injection system
2. Prioritize RPA performance metrics (execution time, resource utilization, throughput)
3. Limit blocking operations; favor asynchronous flows
4. Structure tasks and dependencies clearly for maintainability