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prompt-engineering-patterns

Create and maintain reusable prompt patterns for LLM applications with structured outputs.

4|Updated Apr 21, 2013
Search Tags:#chain-of-thought#LLM#patterns#few-shot#prompt-engineering#system-prompts#structured-outputs
One-click install
npx skills add https://github.com/zeero/dotfiles --skill prompt-engineering-patterns-zeero
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/zeero/dotfiles/tree/main/home/.claude/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/zeero/dotfiles --skill prompt-engineering-patterns-zeero

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires numpy, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill standardizes and accelerates the creation of robust prompt strategies for LLMs, reducing trial-and-error and promoting reusable patterns across projects.

Core Features & Use Cases

  • Few-Shot & CoT Patterns: Build reliable reasoning and dynamic example selection for consistent outputs.
  • Structured Outputs & Templates: Enforce JSON schemas and modular templates for easy parsing and automation.
  • System & Role Prompts: Design role-based systems and guides that align model behavior with business goals.
  • Evaluation & Debugging: Provide validation, error recovery, and A/B testing to optimize prompts in production.
  • Use cases include building production-grade assistants, data analysis copilots, and automated content pipelines.

Quick Start

Instruct the AI to generate a starter set of prompt patterns for few-shot and CoT, with structured outputs and a basic evaluation plan.

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema
How do I create reusable prompt patterns for production LLM applications?▼

Create reusable prompt patterns by standardizing templates for few-shot, chain-of-thought, and system prompts, enabling consistent outputs and modular maintenance across workflows. This approach reduces trial-and-error.

What is the best way to enforce structured JSON outputs from LLM prompts?▼

Enforce structured JSON outputs by applying strict schemas and modular templates within your prompt patterns. This ensures easy parsing and automation, aligning model behavior with business goals.

How do I evaluate and debug prompt templates for consistent LLM behavior?▼

Evaluate and debug prompt templates using built-in validation, error recovery, and A/B testing. This optimizes prompts in production by verifying reasoning and dynamic example selection.

Can I generate few-shot and chain-of-thought prompts without installing external dependencies?▼

Yes, you can generate few-shot and chain-of-thought prompts with no external installations beyond standard tooling. The system requires only numpy to build reliable reasoning and dynamic example selection.

When do I need system prompts versus few-shot patterns for LLM workflows?▼

Use system prompts to design role-based guides aligning behavior with goals, and few-shot patterns for dynamic example selection ensuring consistent outputs. Both standardize prompt strategies.

How do I version and maintain prompt templates across different workflows?▼

Maintain and version prompt templates using tooling for verification within a template system. This supports automated content pipelines and production-grade assistants by ensuring reliable evaluation.

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