llm-app-patterns

8.1

155

276

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

llm-patterns

8.1

Rating

Installs

AI & LLM

Quick Review

Excellent comprehensive skill covering LLM application patterns with production-ready code examples. The description accurately reflects the content, which includes detailed implementations of RAG pipelines, multiple agent architectures, prompt management, and LLMOps. Structure is clear with well-organized sections and visual diagrams. Task knowledge is strong with concrete Python implementations, configuration options, and decision matrices. Novelty is moderate-to-good: while individual patterns are known, the consolidated reference with production considerations (caching, rate limiting, fallbacks, metrics) provides significant value over a CLI agent researching these topics separately. The skill would save substantial tokens and implementation time when building LLM applications. Minor improvement areas: could benefit from more cross-referencing between sections and additional troubleshooting guidance, but overall this is a highly useful, well-crafted skill for AI application development.