Guides the design, evaluation, and implementation of robust LLM-powered projects and agentic architectures.
This skill provides a comprehensive framework for building LLM-integrated applications, from initial task-model fit evaluation to designing scalable pipeline architectures. It emphasizes rapid iteration through manual prototyping, structured output design, and a file-system-first approach to state management, ensuring developers build cost-effective, reliable, and maintainable AI systems while avoiding common pitfalls like over-engineering or high latency.
主な機能
01Task-Model Fit Evaluation Framework
02Staged Pipeline Architecture (Acquire to Render)
030 GitHub stars
04Structured Output & Parsing Reliability
05File System as State Machine Patterns
06Cost & Scale Estimation Formulas
ユースケース
01Deciding between single-agent and multi-agent architectures for complex tasks
02Designing high-throughput batch processing pipelines for unstructured data
03Optimizing agentic workflows to reduce costs and improve deterministic outputs