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:
- Read the raw prompt provided by the user
- 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.
- 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]
- Analyze current monolithic constraints
- Identify service boundaries
- Design inter-service communication
- Plan data consistency strategy
Details: [RODES - Details]
- Expected traffic: [X]
- Data volume: [Y]
- Team size: [Z]
1---2name: prompt-engineer3description: Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW)4---567## Purpose89This 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.1011The 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.1213This is a **universal skill** that works in any terminal context, not limited to Obsidian vaults or specific project structures.1415## When to Use16Invoke this skill when:1718- User provides a vague or generic prompt (e.g., "help me code Python")19- User has a complex idea but struggles to articulate it clearly20- User's prompt lacks structure, context, or specific requirements21- Task requires step-by-step reasoning (debugging, analysis, design)22- User needs a prompt for a specific AI task but doesn't know prompting frameworks23- User wants to improve an existing prompt's effectiveness24- User asks variations of "how do I ask AI to..." or "create a prompt for..."2526## Workflow2728### Step 1: Analyze Intent2930**Objective:** Understand what the user truly wants to accomplish.3132**Actions:**331. Read the raw prompt provided by the user342. Detect task characteristics:35 - **Type:** coding, writing, analysis, design, learning, planning, decision-making, creative, etc.36 - **Complexity:** simple (one-step), moderate (multi-step), complex (requires reasoning/design)37 - **Clarity:** clear intention vs. ambiguous/vague38 - **Domain:** technical, business, creative, academic, personal, etc.393. Identify implicit requirements:40 - Does user need examples?41 - Is output format specified?42 - Are there constraints (time, resources, scope)?43 - Is this exploratory or execution-focused?4445**Detection Patterns:**46- **Simple tasks:** Short prompts (<50 chars), single verb, no context47- **Complex tasks:** Long prompts (>200 chars), multiple requirements, conditional logic48- **Ambiguous tasks:** Generic verbs ("help", "improve"), missing object/context49- **Structured tasks:** Mentions steps, phases, deliverables, stakeholders505152### Step 3: Select Framework(s)5354**Objective:** Map task characteristics to optimal prompting framework(s).5556**Framework Mapping Logic:**5758| Task Type | Recommended Framework(s) | Rationale |59|-----------|-------------------------|-----------|60| **Role-based tasks** (act as expert, consultant) | **RTF** (Role-Task-Format) | Clear role definition + task + output format |61| **Step-by-step reasoning** (debugging, proof, logic) | **Chain of Thought** | Encourages explicit reasoning steps |62| **Structured projects** (multi-phase, deliverables) | **RISEN** (Role, Instructions, Steps, End goal, Narrowing) | Comprehensive structure for complex work |63| **Complex design/analysis** (systems, architecture) | **RODES** (Role, Objective, Details, Examples, Sense check) | Balances detail with validation |64| **Summarization** (compress, synthesize) | **Chain of Density** | Iterative refinement to essential info |65| **Communication** (reports, presentations, storytelling) | **RACE** (Role, Audience, Context, Expectation) | Audience-aware messaging |66| **Investigation/analysis** (research, diagnosis) | **RISE** (Research, Investigate, Synthesize, Evaluate) | Systematic analytical approach |67| **Contextual situations** (problem-solving with background) | **STAR** (Situation, Task, Action, Result) | Context-rich problem framing |68| **Documentation** (medical, technical, records) | **SOAP** (Subjective, Objective, Assessment, Plan) | Structured information capture |69| **Goal-setting** (OKRs, objectives, targets) | **CLEAR** (Collaborative, Limited, Emotional, Appreciable, Refinable) | Goal clarity and actionability |70| **Coaching/development** (mentoring, growth) | **GROW** (Goal, Reality, Options, Will) | Developmental conversation structure |7172**Blending Strategy:**73- **Combine 2-3 frameworks** when task spans multiple types74- Example: Complex technical project → **RODES + Chain of Thought** (structure + reasoning)75- Example: Leadership decision → **CLEAR + GROW** (goal clarity + development)7677**Selection Criteria:**78- Primary framework = best match to core task type79- Secondary framework(s) = address additional complexity dimensions80- Avoid over-engineering: simple tasks get simple frameworks8182**Critical Rule:** This selection happens **silently** - do not explain framework choice to user.8384Role: You are a senior software architect. [RTF - Role]8586Objective: Design a microservices architecture for [system]. [RODES - Objective]8788Approach this step-by-step: [Chain of Thought]891. Analyze current monolithic constraints902. Identify service boundaries913. Design inter-service communication924. Plan data consistency strategy9394Details: [RODES - Details]95- Expected traffic: [X]96- Data volume: [Y]97- Team size: [Z]
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