prompt-engineer

name	prompt-engineer
description	Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW)
type	skill
created	2026-02-27T00:00:00.000Z
domain	ai-ml
category	prompt-engineering
risk	safe
source	community
tags	["skill","ai-ml","prompt-engineering","prompt","engineer"]

Purpose

This skill transforms raw, unstructured user prompts into highly optimized prompts using established prompting frameworks. It analyzes user intent, identifies task complexity, and intelligently selects the most appropriate framework(s) to maximize Claude/ChatGPT output quality.

The skill operates in "magic mode" - it works silently behind the scenes, only interacting with users when clarification is critically needed. Users receive polished, ready-to-use prompts without technical explanations or framework jargon.

This is a universal skill that works in any terminal context, not limited to Obsidian vaults or specific project structures.

When to Use

Invoke this skill when:

• User provides a vague or generic prompt (e.g., "help me code Python")
• User has a complex idea but struggles to articulate it clearly
• User's prompt lacks structure, context, or specific requirements
• Task requires step-by-step reasoning (debugging, analysis, design)
• User needs a prompt for a specific AI task but doesn't know prompting frameworks
• User wants to improve an existing prompt's effectiveness
• User asks variations of "how do I ask AI to..." or "create a prompt for..."

Workflow

Step 1: Analyze Intent

Objective: Understand what the user truly wants to accomplish.

Actions:

1. Read the raw prompt provided by the user
2. Detect task characteristics:
   • Type: coding, writing, analysis, design, learning, planning, decision-making, creative, etc.
   • Complexity: simple (one-step), moderate (multi-step), complex (requires reasoning/design)
   • Clarity: clear intention vs. ambiguous/vague
   • Domain: technical, business, creative, academic, personal, etc.
3. Identify implicit requirements:
   • Does user need examples?
   • Is output format specified?
   • Are there constraints (time, resources, scope)?
   • Is this exploratory or execution-focused?

Detection Patterns:

• Simple tasks: Short prompts (<50 chars), single verb, no context
• Complex tasks: Long prompts (>200 chars), multiple requirements, conditional logic
• Ambiguous tasks: Generic verbs ("help", "improve"), missing object/context
• Structured tasks: Mentions steps, phases, deliverables, stakeholders