Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW)
npxskills add sickn33/antigravity-awesome-skills--skill prompt-engineerLoading…
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.
Invoke this skill when:
Objective: Understand what the user truly wants to accomplish.
Actions:
Detection Patterns:
Objective: Gather missing information only when it is critical to framework selection or prompt quality.
Trigger Conditions — ask only if:
Question Limits:
Example Clarifying Exchange:
User: "help me with AI"
Step 2 (triggered — task type ambiguous):
"To craft the best prompt, I need one quick clarification:
1. What do you want to do with AI — build something, learn about it, or use an AI tool for a task?"
Critical Rule: When in doubt, skip clarification and generate the best prompt with available context. Over-asking breaks the "magic mode" experience.
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:
Selection Criteria:
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]
Details: [RODES - Details]
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.
**4.5. Language Adaptation**
- If original prompt is in Portuguese, generate prompt in Portuguese
- If original prompt is in English, generate prompt in English
- If mixed, default to English (more universal for AI models)
**4.6. Quality Checks**
Before finalizing, verify:
- [ ] Prompt is self-contained (no external context needed)
- [ ] Task is specific and measurable
- [ ] Output format is clear
- [ ] No ambiguous language
- [ ] Appropriate level of detail for task complexity
## Critical Rules
### **NEVER:**
- ❌ Assume information that wasn't provided - ALWAYS ask if critical details are missing
- ❌ Explain which framework was selected or why (magic mode - keep it invisible)
- ❌ Generate generic, one-size-fits-all prompts - always customize to context
- ❌ Use technical jargon in the final prompt (unless user's domain is technical)
- ❌ Ask more than 3 clarifying questions (avoid user fatigue)
- ❌ Include meta-commentary in the output ("This prompt uses...", "Note that...")
- ❌ Present output without code block formatting
- ❌ Mix languages inconsistently (if user writes in PT, respond in PT)
### **ALWAYS:**
- ✅ Analyze intent before generating (Step 1 is mandatory)
- ✅ Ask clarifying questions if critical information is ambiguous (Step 2 conditional)
- ✅ Select framework(s) based on task type and complexity (Step 3 mapping)
- ✅ Blend multiple frameworks when it improves prompt quality
- ✅ Adapt prompt length to original input complexity (simple → short, complex → detailed)
- ✅ Include output format specification in generated prompts
- ✅ Present final prompt in clean Markdown code block
- ✅ Make prompts self-contained (no dependency on external context)
- ✅ Use examples in complex prompts to illustrate expected output
- ✅ Validate prompt completeness before presenting (quality checks in Step 4.6)
### **Example 2: Complex Clear Prompt → RODES + Chain of Thought (No Clarification)**
**User Input:**
copilot> create prompt for: I need to build a product recommendation system considering purchase history, user preferences, and market trends
**Skill Internal Process:**
1. **Analyze:** Clear intent, complex task (ML system design), technical domain
2. **Clarify:** Skip (sufficient information)
3. **Select:** RODES (complex design) + Chain of Thought (reasoning needed)
**Skill Output:**
````markdown
Role: You are a senior machine learning engineer specializing in recommendation systems.
Objective: Design a product recommendation system that combines user purchase history, preferences, and market trends.
Details:
Approach step-by-step:
Examples:
Sense Check: Validate that the proposed solution balances personalization with discovery, scales to production load, and incorporates real-time market trends effectively.
Output format: Technical design document with architecture diagrams, algorithm justification, and implementation roadmap.
User Input:
copilot> optimize prompt: my Python code isn't working, needs debugging
Skill Internal Process:
Skill Output:
```
You are an expert Python debugger.
Task: Debug the Python code provided by the user by analyzing it step-by-step.
Debugging approach:
1. Read and understand the code's intended purpose
2. Identify the error or unexpected behavior
3. Trace variable states at each execution step
4. Locate the root cause of the issue
5. Propose fix with explanation
6. Suggest preventive measures to avoid similar bugs
For each step, show your reasoning:
- What you're checking
- What you found
- Why it matters
Output format:
- **Issue identified:** [Describe the bug]
- **Root cause:** [Why it's happening]
- **Fix:** [Corrected code with comments]
- **Prevention:** [Best practices to avoid this in the future]
Include a working example to verify the fix.
```
This skill is platform-agnostic and works in any terminal context where GitHub Copilot CLI is available. It does not depend on:
The skill is entirely self-contained, operating purely on user input and framework knowledge.
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