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llm-app-patterns

Provide production-ready patterns for RAG pipelines, agent architectures, prompt IDEs, and LLMOps observability.

1|Updated Dec 15, 2025
Search Tags:#llm#rag#patterns#agents#llmops#prompt-ide
One-click install
npx skills add https://github.com/jokken79/YuKyuDATA-app1.0v --skill llm-app-patterns-jokken79
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: llm-app-patterns
Source: https://github.com/jokken79/YuKyuDATA-app1.0v/tree/main/.agent/skills/llm-app-patterns
Command: npx skills add https://github.com/jokken79/YuKyuDATA-app1.0v --skill llm-app-patterns-jokken79

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Production-ready patterns for building and operating LLM-powered applications, helping teams standardize architecture and reduce time-to-value.

Core Features & Use Cases

  • RAG pipelines: robust retrieval-augmented generation architectures for data-grounded answers.
  • Agent architectures: patterns for multi-tool agents and planning-execution flows.
  • Prompt IDEs: templates and tooling to manage prompts, versioning, and experimentation.
  • LLMOps observability: monitoring, logging, and evaluation for safe deployments.

Quick Start

Review the included patterns and implement a starter ReAct-style agent to solve a sample task.

Frequently Asked Questions about llm-app-patterns

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What are the core production-ready patterns for building LLM applications?▼

Core production-ready patterns for building LLM applications include RAG pipelines for data-grounded answers, multi-tool agent architectures, prompt IDEs for versioning, and LLMOps observability for safe deployments.

How do I implement a ReAct-style agent for task planning and execution?▼

To implement a ReAct-style agent, review the included agent architecture patterns and apply the provided templates to establish planning-execution flows that solve sample tasks using multiple tools.

How do I design a RAG pipeline for data-grounded answers?▼

Design a RAG pipeline using the provided retrieval-augmented generation architectures, which offer robust templates to ensure your LLM applications deliver reliable, data-grounded answers.

What is LLMOps observability and how does it handle monitoring and evaluation?▼

LLMOps observability provides monitoring, logging, and evaluation frameworks to track LLM application behavior, ensuring reliable pattern selection and safe end-to-end deployment.

Can I use prompt IDEs to manage prompt versioning and experimentation?▼

Yes, prompt IDEs provide dedicated tooling and templates to manage prompt versioning and streamline experimentation for your LLM applications.

When should I not use multi-tool agent architectures for my LLM application?▼

Multi-tool agent architectures may not suit simple retrieval tasks; consider standard RAG pipelines instead to reduce complexity when planning-execution flows are unnecessary.

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