Prompt Engineering Patterns

What it does

This skill is a comprehensive patterns guide for prompt engineering. It documents concrete techniques — few-shot selection, chain-of-thought, structured JSON outputs, template systems, progressive disclosure, and error recovery — with code examples and best practices for production use. It's designed to make prompts more reliable, efficient, and easy to maintain.

When to use it

Use this skill when designing or debugging prompts for production LLM systems: building structured output pipelines, reducing hallucinations, implementing self-verification, or constructing reusable prompt templates. Helpful for engineers, ML engineers, and prompt authors.

What's included

• Scripts: example code snippets in Python and pseudocode; example templates for structured outputs and verification workflows.
• References: links to Anthropic/OpenAI prompt docs and example libraries; practical code for embedding-driven example selection.
• Instructions: pattern descriptions, quick-start examples, and implementation recipes for each pattern.

Compatible agents

Agent-agnostic patterns that work with Claude, Anthropic, OpenAI, LangChain-based chains, and other LLM runtimes supporting system prompts and structured output modes.