flow-skill-ai-skel-ts

To scaffold a production-ready AI agent, this generates a 10-module bottom-up architecture featuring LLM integration, tool calling, and observability. It provides a comprehensive skeleton that includes logging, cost tracking, and session management modules for immediate end-to-end workflows.

Adding AI agent capabilities to an existing project is achieved by generating a modular scaffold with LLM integration and tool calling. This approach implements a 10-module architecture across four layers, enabling observability, session compaction, and content fetching without rewriting your entire application.

This AI agent scaffold supports TypeScript and is applicable across multiple languages. It allows you to bootstrap a new AI agent or add AI capabilities to an existing project regardless of your language, generating a standardized 10-module architecture for the framework.

Yes, you can set up observability and cost tracking for your LLM agent using the scaffold's built-in modules. The generated architecture includes dedicated modules for logging, cost tracking, and run context, ensuring your end-to-end AI agent workflows are fully monitored.