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prompt-engineering

Design, debug, and optimize prompts for LLMs with few-shot and chain-of-thought techniques.

Updated Sep 10, 2025
Search Tags:#chain-of-thought#llm#few-shot#prompt-engineering#system-prompt#template-design
One-click install
npx skills add https://github.com/tillysoso/mv1 --skill prompt-engineering-tillysoso
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/tillysoso/mv1/tree/main/.claude/skills/prompt-engineering
Command: npx skills add https://github.com/tillysoso/mv1 --skill prompt-engineering-tillysoso

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Provides a structured approach to creating clear, reliable prompts for large language models, reducing ambiguity and improving task outcomes.

Core Features & Use Cases

  • Prompt drafting: Create effective prompts from user goals, with clear constraints and formats.
  • Prompt optimization: Iterate on prompts using pattern-driven strategies and measurable improvements.
  • System prompts & templates: Design reusable system prompts and templates for consistent model behavior across tasks.
  • Use Case: When a user asks to write, rewrite, or optimize a prompt, or to implement few-shot, chain-of-thought, or template-based prompting.

Quick Start

Draft a clear, reusable prompt for a given task, including a brief system prompt and a simple few-shot example structure.

Frequently Asked Questions about prompt-engineering

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

FAQPage Schema
How do I write better prompts for LLMs using few-shot examples and chain-of-thought?▼

Optimize LLM prompts by applying few-shot example selection and chain-of-thought reasoning within a modular, testable template design, ensuring clear constraints and measurable improvements for reliable task outcomes.

What is the best way to structure a system prompt for consistent model behavior?▼

Structure a system prompt by establishing clear constraints, defining expected output formats, and providing context rules to guide the LLM, ensuring consistent model behavior and reliable task execution across workflows.

How do I debug and optimize prompts that produce poor or ambiguous LLM outputs?▼

Debug and optimize prompts by iterating with pattern-driven strategies, reviewing few-shot examples, and refining chain-of-thought steps to resolve ambiguity and achieve measurable improvements in LLM outputs.

Does prompt engineering work for designing reusable templates across different tasks?▼

Prompt engineering works for designing reusable templates across tasks by enforcing modular design and testability, allowing you to create consistent system prompts that adapt to various LLM workflows efficiently.

When do I need to use chain-of-thought reasoning in my LLM prompts?▼

You need chain-of-thought reasoning in LLM prompts when tasks require complex logic or multi-step problem solving, as it guides the model through structured reasoning to produce accurate, reliable outputs.

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