Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW)

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 2: Ask Clarifying Questions (Conditional) Objective: Gather missing information only when it is critical to framework selection or prompt quality. Trigger Conditions — ask only if: Task type is completely ambiguous (cannot determine coding vs. writing vs. analysis) Target audience is unknown and materially affects the output Scope is undefined and choosing wrong scope would invalidate the prompt Requested output format conflicts or is missing and cannot be inferred Question Limits: Maximum 3 questions per invocation Combine related questions into one when possible If enough context exists, skip this step entirely (most cases) Example Clarifying Exchange: Critical Rule: When in doubt, skip clarification and generate the best prompt with available context. Over asking breaks the "magic mode" experience. 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] Output Format: [RTF Format] Provide architecture diagram description, service definitions, and migration roadmap. Sense Check: [RODES Sense check] Validate that services are loosely coupled, independently deployable, and aligned with business domains. copilot create prompt for: I need to build a product recommendation system considering purchase history, user preferences, and market trends markdown copilot optimize prompt: my Python code isn't working, needs debugging markdown Notes This skill is platform agnostic and works in any terminal context where GitHub Copilot CLI is available. It does not depend on: Obsidian vault structure Specific project configurations External files or templates The skill is entirely self contained, operating purely on user input and framework knowledge. Limitations Use this skill only when the task clearly matches the scope described above. Do not treat the output as a substitute for environment specific validation, testing, or expert review. Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

prompt-engineer