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prompt-engineer

Transform user prompts into structured prompts using frameworks like RTF and Chain of Thought.

6|1|Updated Feb 27, 2026
Search Tags:#prompt engineering#frameworks#llm optimization#prompt generation#ai prompting#intent analysis
One-click install
npx skills add https://github.com/RepairYourTech/cfsa-antigravity --skill prompt-engineer-repairyourtech
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: prompt-engineer
Source: https://github.com/RepairYourTech/cfsa-antigravity/tree/main/.agent/skills/prompt-engineer
Command: npx skills add https://github.com/RepairYourTech/cfsa-antigravity --skill prompt-engineer-repairyourtech

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill transforms vague or unstructured user prompts into highly effective, optimized prompts using established frameworks, ensuring maximum AI output quality and clarity.

Core Features & Use Cases

  • Intent Analysis: Detects prompt type, complexity, clarity, and domain.
  • Framework Selection: Automatically chooses the best framework (RTF, RISEN, Chain of Thought, RODES, etc.) based on the task.
  • Optimized Prompt Generation: Creates structured, self-contained prompts ready for AI execution.
  • Use Case: A user asks "help me code Python." This Skill generates a detailed prompt for an AI to act as a senior software engineer, review code, and provide specific feedback on readability, correctness, performance, and security.

Quick Start

Use the prompt-engineer skill to optimize the prompt: "I need to build a recommendation system using purchase history and user preferences".

Frequently Asked Questions about prompt-engineer

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

FAQPage Schema
How do I optimize AI prompts for complex tasks using frameworks?▼

Optimize AI prompts by analyzing user intent and applying structured frameworks like RTF, RISEN, Chain of Thought, or RODES to generate clear, step-by-step reasoning instructions for large language models.

What is the best way to structure a vague prompt for an AI model?▼

Structure a vague prompt by detecting its complexity and domain, then mapping it to an appropriate prompting framework to produce a self-contained, role-based instruction ready for AI execution.

How does intent analysis improve LLM prompt generation?▼

Intent analysis improves LLM prompt generation by evaluating prompt clarity and domain to automatically select the most suitable prompting framework, ensuring maximum output quality from the AI model.

Can I use prompt engineering frameworks for coding and software design tasks?▼

Yes, you can apply prompt engineering frameworks to coding tasks by generating structured prompts that instruct the AI to act as a senior engineer, providing specific feedback on readability, performance, and security.

When should I use Chain of Thought vs RISEN for AI prompting?▼

Use Chain of Thought for tasks requiring step-by-step reasoning, and apply RISEN when you need complex, role-based instructions, with the optimal framework selected automatically based on prompt analysis.

Why does my unstructured prompt produce poor AI output quality?▼

Unstructured prompts produce poor AI output quality because they lack clear intent and framework backing, which are necessary to guide the large language model effectively and ensure maximum clarity.

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