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alexsandrocruz
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prompt-engineering-patterns

Design production prompt templates with few-shot and chain-of-thought patterns.

Updated Jan 23, 2026
Search Tags:#verification#chain-of-thought#LLM#few-shot#tree-of-thought#prompt-engineering-patterns
One-click install
npx skills add https://github.com/alexsandrocruz/DominusLeads --skill prompt-engineering-patterns-alexsandrocruz
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/alexsandrocruz/DominusLeads/tree/main/backend/Sapienza.Leads/.claude/skills/meta/prompt-engineering-patterns
Command: npx skills add https://github.com/alexsandrocruz/DominusLeads --skill prompt-engineering-patterns-alexsandrocruz

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Master advanced prompt engineering patterns to maximize reliability, controllability, and performance of production LLMs. This skill helps teams design robust prompts, templates, and verification flows to produce consistent outputs across diverse tasks.

Core Features & Use Cases

  • Pattern Library & Reusability: structured templates for system prompts, instructions, examples, and formatting.
  • Advanced Prompt Techniques: chain-of-thought, tree-of-thought, few-shot selection, error handling, and self-verification.
  • Production-Ready Workflows: prompt versioning, A/B testing, evaluation metrics, and template composition.
  • Real-world Use Case: Build a customer support bot that uses system prompts, few-shot demos, and a verification step to generate consistent, safe responses across multiple channels.

Quick Start

Provide an example prompt that combines system prompts, few-shot examples, and a verification step to solve a multi-step reasoning task.

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema
How does chain-of-thought scaffolding improve complex reasoning in LLMs?▼

Chain-of-thought scaffolds guide LLMs through explicit intermediate reasoning steps before reaching a final conclusion. This structured approach improves performance and reliability for complex reasoning tasks by reducing logical errors.

How do I add verification workflows to an LLM prompt pipeline?▼

Implement few-shot patterns by integrating structured examples directly into your prompt templates. This approach provides the model with clear formatting and context references, maximizing output consistency for production environments.

What is the best way to implement few-shot patterns in LLM templates?▼

Implement few-shot patterns by integrating structured examples directly into your prompt templates. This approach provides the model with clear formatting and context references, maximizing output consistency for production environments.

Can I use tree-of-thought patterns for multi-step reasoning tasks?▼

Tree-of-thought patterns support multi-step reasoning by exploring multiple intermediate states or branches of thought. Combined with template composition, they provide structured scaffolds for complex problem-solving.

How do I manage prompt versioning and A/B testing for LLMs?▼

Manage prompt versioning and A/B testing by utilizing production-ready workflows that track template composition and evaluation metrics. This allows teams to systematically compare prompt variations and measure performance.

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