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prompt-engineering-patterns

Design, optimize, and validate prompts and templates for LLM applications.

Updated Aug 27, 2026
Search Tags:#templates#chain-of-thought#LLM#few-shot#prompt-engineering#system-prompts
One-click install
npx skills add https://github.com/olivier-motium/mimesis --skill prompt-engineering-patterns-olivier-motium
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering-patterns
Source: https://github.com/olivier-motium/mimesis/tree/main/docs/claude-code/config/skills/prompt-engineering-patterns
Command: npx skills add https://github.com/olivier-motium/mimesis --skill prompt-engineering-patterns-olivier-motium

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 guides engineers to design, optimize, and validate prompts and templates that drive reliable, high-quality LLM behavior in production.

Core Features & Use Cases

  • CoT and structured reasoning patterns to improve complex reasoning tasks
  • Few-shot and retrieval-based prompts to improve generalization with minimal examples
  • Template systems and modular design for reusable prompt components
  • System prompt design to set role, constraints, and output formats
  • Evaluation and optimization workflows including A/B testing and metrics

Quick Start

Try a simple request: "Create a concise, 3-step prompt template to classify sentiment from user input, with a verification step and a clear output format."

Frequently Asked Questions about prompt-engineering-patterns

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

FAQPage Schema
How do I design system prompts that set clear roles and output formats for LLM applications?▼

Chain-of-thought patterns structure LLM reasoning into explicit intermediate steps, improving accuracy on complex tasks. Apply these structured reasoning templates to break down multi-step problems and verify logical flow before output generation.

What's the best way to use few-shot prompts to improve LLM generalization with minimal examples?▼

Evaluation workflows apply A/B testing and metrics to production prompts, measuring reliability and output quality. Automate template validation by running structured verification techniques against expected results across different prompt variations.

Does this prompt engineering approach require specific dependencies to build template pipelines?▼

This approach requires the numpy dependency to support prompt evaluation workflows and template optimization. It provides scripts and reference assets to automate few-shot pipelines, system prompts, and chain-of-thought patterns for production LLM applications.

How do I create a prompt template to classify sentiment from user input with a verification step?▼

Build a modular prompt template that defines the classification task, instructs the LLM to analyze sentiment, and enforces a verification step before output. Use structured system prompts to constrain the format and validate the generated response.

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