Orchestrates LLM-driven logic with modular prompts and typed interfaces.
npx skills add https://github.com/artofrawr/claude-control --skill llm-patterns-artofrawr
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
AI-first applications rely on combining LLM logic with traditional code, enabling scalable, testable reasoning and prompt management.
This Quick Start commands AI to adopt LLM-driven patterns in a new project and shows how to wire prompts, tests, and tooling.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
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JuanDiego30Apply prompt-engineering patterns for structured outputs and production LLM workflows.
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