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prompt-engineering

Applies prompt engineering patterns to improve LLM output quality and consistency.

Updated Jul 29, 2026
Search Tags:#prompt engineering#prompt optimization#chain-of-thought#llm#few-shot learning#system prompt#ai agents
One-click install
npx skills add https://github.com/MaiconGambini/opencode-harness-guide --skill prompt-engineering-maicongambini
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/MaiconGambini/opencode-harness-guide/tree/main/skills/prompt-engineering
Command: npx skills add https://github.com/MaiconGambini/opencode-harness-guide --skill prompt-engineering-maicongambini

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? LLM prompts often produce inconsistent, inaccurate, or poorly formatted results because they lack structure, examples, or reasoning guidance. This Skill provides proven prompt engineering patterns to systematically improve model performance, reliability, and controllability. ## Core Features & Use Cases - Few-Shot Learning: Teach the model desired behavior with 2-5 input-output examples for consistent formatting and edge-case handling. - Chain-of-Thought Prompting: Request step-by-step reasoning to improve accuracy on complex analytical and multi-step logic tasks. - Prompt Optimization & Templates: Iterate prompts through A/B testing and build reusable template systems with variables and conditional sections. - Use Case: When building a support ticket classifier, use few-shot examples to enforce a consistent JSON output schema, then apply chain-of-thought reasoning to improve categorization accuracy on ambiguous tickets. ## Quick Start Ask the AI to help you rewrite an existing prompt using few-shot examples and chain-of-thought reasoning to get more consistent results.

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 consistent LLM outputs?▼

Start with a specific instruction, add constraints like output format and length, then include 2-5 input-output examples demonstrating the desired behavior. Test on diverse inputs and iterate based on measured accuracy and consistency.

What is chain-of-thought prompting and when should I use it?▼

Chain-of-thought prompting asks the model to reason step by step before answering, either with a phrase like "Let's think step by step" or with example reasoning traces. Use it for multi-step logic, math, or root-cause analysis tasks.

How many few-shot examples should a prompt include?▼

Include 2-5 input-output pairs that demonstrate the desired behavior, including edge cases. More examples improve accuracy but consume tokens, so balance example count against task complexity and context limits.

What is the difference between a system prompt and a user prompt?▼

A system prompt sets persistent global behavior such as role, rules, and output format across the whole conversation. User prompts carry variable per-turn content, keeping stable instructions out of repeated messages.

Why does my prompt give inconsistent results across inputs?▼

Inconsistency usually comes from vague instructions, missing output format constraints, or examples that do not match the target task. Add explicit formatting rules, representative examples, and test on edge-case inputs.

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