PRP Template for Pydantic AI Agents

use-cases/pydantic-ai/.claude/commands/execute-pydantic-ai-prp.md

@@ -0,0 +1,55 @@
+# Execute Pydantic AI Agent PRP
+
+Implement a Pydantic AI agent using the PRP file.
+
+## PRP File: $ARGUMENTS
+
+## Execution Process
+
+1. **Load PRP**
+   - Read the specified Pydantic AI PRP file
+   - Understand all agent requirements and research findings
+   - Follow all instructions in the PRP and extend research if needed
+   - Review main_agent_reference patterns for implementation guidance
+   - Do more web searches and Pydantic AI documentation review as needed
+
+2. **ULTRATHINK**
+   - Think hard before executing the agent implementation plan
+   - Break down agent development into smaller steps using your todos tools  
+   - Use the TodoWrite tool to create and track your agent implementation plan
+   - Follow main_agent_reference patterns for configuration and structure
+   - Plan agent.py, tools.py, dependencies.py, and testing approach
+
+3. **Execute the plan**
+   - Implement the Pydantic AI agent following the PRP
+   - Create agent with environment-based configuration (settings.py, providers.py)
+   - Use string output by default (no result_type unless structured output needed)
+   - Implement tools with @agent.tool decorators and proper error handling
+   - Add comprehensive testing with TestModel and FunctionModel
+
+4. **Validate**
+   - Test agent import and instantiation
+   - Run TestModel validation for rapid development testing
+   - Test tool registration and functionality
+   - Run pytest test suite if created
+   - Verify agent follows main_agent_reference patterns
+
+5. **Complete**
+   - Ensure all PRP checklist items done
+   - Test agent with example queries
+   - Verify security patterns (environment variables, error handling)
+   - Report completion status
+   - Read the PRP again to ensure complete implementation
+
+6. **Reference the PRP**
+   - You can always reference the PRP again if needed
+
+## Pydantic AI-Specific Patterns to Follow
+
+- **Configuration**: Use environment-based setup like main_agent_reference  
+- **Output**: Default to string output, only use result_type when validation needed
+- **Tools**: Use @agent.tool with RunContext for dependency injection
+- **Testing**: Include TestModel validation for development
+- **Security**: Environment variables for API keys, proper error handling
+
+Note: If validation fails, use error patterns in PRP to fix and retry. Follow main_agent_reference for proven Pydantic AI implementation patterns.