Agent skill · research science · aiskillstore
llm-app-patterns
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.
Why this skill is useful
Provides detailed implementation patterns and executable code snippets for building LLM applications, including RAG pipelines and agent architectures.
What it needs
About 8k tokens when loaded. Last updated 2026-08-07. 405 stars on the source repository.
What this skill does
🤖 LLM Application Patterns Production-ready patterns for building LLM applications, inspired by Dify and industry best practices. When to Use This Skill Use this skill when: Designing LLM-powered applications Implementing RAG (Retrieval-Augmented Generation) Building AI agents with tools Setting up LLMOps monitoring Choosing between agent architectures --- 1. RAG Pipeline Architecture Overview RAG (Retrieval-Augmented Generation) grounds LLM responses in your data. 1.1 Document Ingestion 1.2 Embedding & Storage 1.3 Retrieval Strategies 1.4 Generation with Context --- 2. Agent Architectures 2.1 ReAct Pattern (Reasoning + Acting) 2.2 Function Calling Pattern 2.3 Plan-and-Execute Pattern 2.4 Multi-Agent Collaboration --- 3. Prompt IDE Patterns 3.1 Prompt Templates with Variables 3.2 Prompt Versioning & A/B Testing 3.3 Prompt Chaining --- 4. LLMOps & Observability 4.1 Metrics to Track 4.2 Logging & Tracing 4.3 Evaluation Framework --- 5. Production Patterns 5.1 Caching Strategy 5.2 Rate Limiting & Retry 5.3 Fallback Strategy `python class LLMWithFallback: def init(self, primary: str, fallbacks: list[str]): self.primary = primary self.fallbacks = fallbacks def generate(self, prompt: str, kwargs) -> str: models = [self.primary] + se
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills aiskillstore/llm-app-patterns