Guides the end-to-end lifecycle of LLM projects, from evaluating task-model fit to architecting resilient, agent-assisted data pipelines.
This skill provides a comprehensive framework for designing and implementing LLM-powered applications and batch processing pipelines. It emphasizes a manual-first prototyping approach to validate task-model fit, outlines robust pipeline architectures using file-system state management, and offers strategies for cost estimation and structured output parsing. Whether building multi-agent systems or simple data processors, it helps developers avoid common pitfalls like over-engineering and provides a clear roadmap for rapid, agent-assisted iteration.