LLM Project Methodology

About

This skill provides a comprehensive framework for building robust, cost-effective agent systems and LLM applications. It helps developers determine task-model fit through manual prototyping, design modular 'acquire-to-render' pipelines, and manage state efficiently using the file system. By emphasizing architectural reduction and structured output design, it ensures that AI projects are scalable, debuggable, and optimized for production performance.

Key Features

• LLM Cost and Scale Estimation
• File-System State Machine Patterns
• Modular Pipeline Architecture Design
• 5,499 GitHub stars
• Structured Output & Parser Optimization
• Task-Model Fit Evaluation Framework

Use Cases

• Evaluating when to use single-agent vs. multi-agent architectures for complex workflows.
• Designing high-throughput batch processing pipelines for unstructured data analysis.
• Refactoring monolithic agent scripts into modular, idempotent processing stages.