prompt-engineering-patterns

Overview

What is prompt-engineering-patterns?

The prompt-engineering-patterns skill provides a comprehensive toolkit for designing, optimizing, and managing advanced prompts for large language models (LLMs) in production. It is ideal for AI engineers, prompt designers, and developers who need reliable, high-quality outputs from models like OpenAI's GPT or ChatGPT.

Who Should Use This Skill?

• AI engineers building production LLM applications
• Prompt authors seeking robust templates and best practices
• Teams optimizing prompt performance, consistency, and reliability
• Anyone implementing few-shot, chain-of-thought, or structured prompt patterns

Problems It Solves

• Inconsistent or unreliable LLM outputs
• Difficulty scaling prompt design across tasks and domains
• Lack of reusable, production-ready prompt templates
• Challenges in debugging, refining, and validating prompts

How to Use

Installation Steps

1. Add the skill to your agent or project using:

   npx skills add https://github.com/wshobson/agents --skill prompt-engineering-patterns

2. Start by reviewing SKILL.md for a high-level overview and usage notes.

Key Files and Folders

• assets/few-shot-examples.json: Ready-to-use few-shot examples for tasks like sentiment analysis, entity extraction, and code generation.
• assets/prompt-template-library.md: A library of prompt templates for classification, extraction, generation, and transformation tasks.
• references/chain-of-thought.md: Guides and code for implementing chain-of-thought prompting.
• references/few-shot-learning.md: Strategies for selecting and managing few-shot examples.
• references/prompt-optimization.md: Best practices for systematic prompt refinement and evaluation.
• references/prompt-templates.md: Template architecture and code for dynamic, reusable prompts.
• references/system-prompts.md: Patterns for designing effective system prompts for specialized AI assistants.
• scripts/optimize-prompt.py: Example script for automating prompt optimization workflows.

Workflow Recommendations

• Adapt templates and patterns to your own use cases—do not copy verbatim.
• Use the provided few-shot examples and template library as starting points for your domain.
• Leverage the references for advanced techniques like self-consistency, semantic example selection, and structured outputs.

FAQ

What makes prompt-engineering-patterns different from basic prompt guides?

This skill offers production-focused patterns, reusable templates, and code for advanced prompt engineering—not just simple prompt examples. It covers few-shot learning, chain-of-thought, system prompt design, and optimization workflows.

Can I use prompt-engineering-patterns with OpenAI, ChatGPT, or other LLMs?

Yes. The templates and patterns are designed for compatibility with OpenAI, ChatGPT, and similar LLM platforms.

Where should I start after installation?

Begin with SKILL.md for a roadmap. Then explore the assets/ and references/ folders for templates, examples, and best practices tailored to your tasks.

Is this skill suitable for non-production or hobby projects?

While optimized for production, the skill is also valuable for prototyping, research, and learning advanced prompt engineering techniques.

How do I customize templates for my domain?

Edit the templates and examples in the assets/ folder, or extend the template classes in references/prompt-templates.md to fit your requirements.

Where can I find more examples and helper scripts?

Check the assets/ folder for examples and the scripts/ folder for automation tools. The references/ directory contains in-depth guides and code patterns.

For a full file tree and more details, open the Files tab in the repository.