This skill provides a comprehensive methodology for building robust LLM projects, ranging from initial task-model fit evaluation to complex multi-agent architectures. It emphasizes a structured pipeline approach—acquire, prepare, process, parse, and render—while utilizing the file system as a state machine for reliable, idempotent execution. Whether you are designing batch processing workflows or interactive agent applications, this skill helps optimize performance, manage costs, and ensure high-quality structured outputs through rapid prototyping and architectural reduction principles.
主要功能
01File system-based state management for idempotency and debugging
02Cost estimation and token usage optimization frameworks
03Guidance on choosing between single and multi-agent architectures
04Structured pipeline architecture design (Acquire to Render)
057,140 GitHub stars
06Task-model fit evaluation and manual prototyping strategies
