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prompt-engineering

Select prompting techniques and output formats for AI tasks.

100|15|Updated Nov 13, 2025
Search Tags:#react#llm#prompting#prompt-engineering#CoT#ToT#self-cons consistency
One-click install
npx skills add https://github.com/rfxlamia/claude-skillkit --skill prompt-engineering-rfxlamia
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineering
Source: https://github.com/rfxlamia/claude-skillkit/tree/main/skills/prompt-engineering
Command: npx skills add https://github.com/rfxlamia/claude-skillkit --skill prompt-engineering-rfxlamia

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Crafting prompts that reliably guide AI systems to accurate, useful outputs can be challenging. This Skill helps you select the right prompting technique (Chain of Thought, Few-Shot, Zero-Shot, ReAct, Tree of Thoughts, Self-Consistency) and the appropriate output format (XML, JSON, YAML, Natural Language) based on task characteristics and target LLM.

Core Features & Use Cases

  • End-to-end prompting strategy: choose technique, format, and structure for each task.
  • Supports common workflows: content generation, data extraction, analysis, coding help, and planning.
  • On-demand formats: XML (Claude), JSON (GPT/others), YAML (human-readable), Natural Language.

Quick Start

Provide a ready-to-use prompt plan for a given task by selecting the optimal technique and format.

Frequently Asked Questions about prompt-engineering

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

FAQPage Schema
How do I design reliable AI prompts for different LLM targets?▼

To design reliable AI prompts, select the optimal prompting technique like Chain of Thought or Few-Shot, and match the output format to your specific target LLM environment.

What is the best prompting technique for complex reasoning tasks?▼

Tree of Thoughts (ToT) and Self-Consistency are prompting techniques best suited for complex reasoning tasks, allowing the AI to explore multiple branches and select the most consistent answer.

How do I get AI outputs in JSON or YAML format?▼

To get AI outputs in JSON or YAML format, specify the desired structure in your prompt and use a prompting strategy that enforces strict output compatibility for natural language processing.

When should I use ReAct over Zero-Shot prompting?▼

Use ReAct prompting over Zero-Shot when your task requires the AI to reason and act iteratively, such as fetching external data, whereas Zero-Shot is better for direct content generation.

Does this prompting strategy work for security testing and research?▼

Yes, this prompting strategy works for security testing and research by applying tailored prompt engineering frameworks to elicit specific outputs and test LLM boundaries effectively.

What output format should I use for Claude vs GPT models?▼

For Claude models, XML is often the preferred output format, while JSON is typically used for GPT and other models to ensure structured and parseable AI responses.

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