llm-patterns

Orchestrates LLM-driven logic with modular prompts and typed interfaces.

1|Updated Jan 10, 2026

Search Tags:#testing #architecture #llm #patterns #typing #prompt-management

Ready to run

npx skills add https://github.com/artofrawr/claude-control --skill llm-patterns-artofrawr

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: llm-patterns
Source: https://github.com/artofrawr/claude-control/tree/main/skills/llm-patterns
Command: npx skills add https://github.com/artofrawr/claude-control --skill llm-patterns-artofrawr

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI-first applications rely on combining LLM logic with traditional code, enabling scalable, testable reasoning and prompt management.

Core Features & Use Cases

LLM-driven classification, extraction, and transformation with code-backed plumbing
Testing and evaluation patterns for LLMs, mocks, and fixtures
Structured, versioned prompt templates and safe orchestration examples

Quick Start

This Quick Start commands AI to adopt LLM-driven patterns in a new project and shows how to wire prompts, tests, and tooling.

Frequently Asked Questions about llm-patterns

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

FAQPage Schema

How do I structure prompt management for AI-first applications?▼

Testing LLM logic requires deterministic tests, mocks, and fixtures to enforce reliability and guardrails. By separating reasoning from plumbing through typed LLM interfaces, you can create structured tests that validate classification and extraction patterns, ensuring robust code-backed evaluations.

What is the best way to separate LLM reasoning from application plumbing?▼

Separating LLM reasoning from plumbing involves using typed LLM interfaces and modular prompts. This pattern-driven architecture isolates AI-driven logic from traditional code foundations, enabling safe orchestration and reliable execution for tasks like transformation and classification.

Can I use typed LLM interfaces for deterministic testing?▼

Typed LLM interfaces enable deterministic testing by enforcing strict input and output contracts for LLM-driven logic. Using mocks and fixtures alongside typed interfaces ensures your classification, extraction, and transformation patterns are validated reliably under defined guardrails.

How do I implement LLM testing patterns without external dependencies?▼

Implementing LLM testing patterns without dependencies uses built-in mocks and fixtures to validate logic locally. This approach applies deterministic tests directly to modular prompts and typed interfaces, ensuring reliable guardrails for classification and extraction without requiring external testing frameworks.

When do I need pattern-driven LLM architecture for my project?▼

Pattern-driven LLM architecture is needed when building AI-first applications requiring scalable, testable reasoning. If your project involves complex prompt management, classification, extraction, or transformation tasks demanding clean separation of logic from plumbing, these patterns enforce necessary reliability.