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)
category	Document Processing
source	antigravity
tags	["python","markdown","api","claude","ai","gpt","automation","workflow","template","design"]
url	https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/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

Step 3: Select Framework(s)

Objective: Map task characteristics to optimal prompting framework(s).

Framework Mapping Logic:

Task Type	Recommended Framework(s)	Rationale
Role-based tasks (act as expert, consultant)	RTF (Role-Task-Format)	Clear role definition + task + output format
Step-by-step reasoning (debugging, proof, logic)	Chain of Thought	Encourages explicit reasoning steps
Structured projects (multi-phase, deliverables)	RISEN (Role, Instructions, Steps, End goal, Narrowing)	Comprehensive structure for complex work
Complex design/analysis (systems, architecture)	RODES (Role, Objective, Details, Examples, Sense check)	Balances detail with validation
Summarization (compress, synthesize)	Chain of Density	Iterative refinement to essential info
Communication (reports, presentations, storytelling)	RACE (Role, Audience, Context, Expectation)	Audience-aware messaging
Investigation/analysis (research, diagnosis)	RISE (Research, Investigate, Synthesize, Evaluate)	Systematic analytical approach
Contextual situations (problem-solving with background)	STAR (Situation, Task, Action, Result)	Context-rich problem framing
Documentation (medical, technical, records)	SOAP (Subjective, Objective, Assessment, Plan)	Structured information capture
Goal-setting (OKRs, objectives, targets)	CLEAR (Collaborative, Limited, Emotional, Appreciable, Refinable)	Goal clarity and actionability
Coaching/development (mentoring, growth)	GROW (Goal, Reality, Options, Will)	Developmental conversation structure

Blending Strategy:

• Combine 2-3 frameworks when task spans multiple types
• Example: Complex technical project → RODES + Chain of Thought (structure + reasoning)
• Example: Leadership decision → CLEAR + GROW (goal clarity + development)

Selection Criteria:

• Primary framework = best match to core task type
• Secondary framework(s) = address additional complexity dimensions
• Avoid over-engineering: simple tasks get simple frameworks

Critical Rule: This selection happens silently - do not explain framework choice to user.

Role: You are a senior software architect. [RTF - Role]

Objective: Design a microservices architecture for [system]. [RODES - Objective]

Approach this step-by-step: [Chain of Thought]

1. Analyze current monolithic constraints
2. Identify service boundaries
3. Design inter-service communication
4. Plan data consistency strategy

Details: [RODES - Details]

• Expected traffic: [X]
• Data volume: [Y]
• Team size: [Z]