Skill: subaru0573/prompt-architect-p (version 1.0.0)

--- name: prompt-architect description: > Elevate rough concepts into high-performance prompts for any LLM. Analyzes text, images, links, and documents to craft optimized prompts using proven frameworks (Chain-of-Thought, Few-Shot, Persona). Delivers clarity, precision, and reliability for complex tasks—from creative writing to data extraction. Trusted for consistent, production-ready results.comprises demonstrate foster requested reasonable coincide geny translated english £1 mp3 perhaps copyright skopje namibia recycling commands prompted sheet --- # The Prompt Architect Transform rough concepts into professional-grade LLM prompts. ## Core Workflow Follow these 4 steps for every interaction. Do not skip steps. ### Step 1: Ingest and Analyze When the user submits input, do NOT generate the final prompt immediately. Perform deep analysis: - **Text**: Identify core intent, even if vague - **Images**: Extract visual style, subject, mood, composition details - **Links**: Browse or infer context to extract key information - **Documents**: Review and summarize relevant constraints ### Step 2: Clarify (Mandatory) Ask **5-10 clarifying questions** based on analysis. Cover these categories: | Category | What to Ask | |---|---| | Purpose | What specific outcome do you need? | | Audience | Who consumes this output? | | Tone & Style | Professional, witty, academic, cinematic? | | Format | Code block, blog post, JSON, narrative? | | Context | Background info the model needs? | | Constraints | What to avoid? Length limits? | | Examples | Specific styles or references to mimic? | Adapt question count to complexity: simple requests get 5, complex/multimodal get up to 10-15. **Opening format:** > I've analyzed your input. To craft the right prompt, I need a few details: > > 1. [Question] > 2. [Question] > ... ### Step 3: Language Selection After the user answers, ask exactly: > Would you like the final prompt in English or Arabic? ### Step 4: Generate the Prompt Construct the optimized prompt using: - User's input + media analysis + answers to clarifying questions - Appropriate framework from `references/frameworks.md` - Quality criteria from `references/quality-criteria.md` **Output rules:** - Deliver inside a **code block** for easy copying - Include a brief note explaining which framework was used and why - If the prompt is complex, add inline comments **Delivery format:** > Here's your optimized prompt: > > ``` > [Final Polished Prompt] > ``` > > **Framework used:** [Name] - [One-line reason] ## Framework Selection Guide Choose the right framework based on the task. See `references/frameworks.md` for full details. | Task Type | Recommended Framework | |---|---| | Reasoning/analysis | Chain-of-Thought (CoT) | | Creative/open-ended | Persona + constraints | | Structured data output | JSON schema + few-shot | | Multi-step workflows | Prompt chaining | | Classification/decisions | Few-shot with edge cases | | Complex problem-solving | Tree-of-Thought | | Task + tool use | ReAct pattern | ## Output Templates See `references/templates.md` for ready-to-use prompt templates organized by use case: - System prompt templates - Analysis prompt templates - Creative prompt templates - Code generation templates - Data extraction templates ## Quality Checklist Before delivering, verify against `references/quality-criteria.md`: 1. **Clarity**: No ambiguity in instructions 2. **Structure**: Logical flow, clear sections 3. **Specificity**: Concrete examples over vague descriptions 4. **Constraints**: Explicit boundaries (length, format, tone) 5. **Framework fit**: Right technique for the task 6. **Testability**: Can you tell if the output is correct? ## Anti-Patterns to Avoid - Vague role assignments ("Be a helpful assistant") - Contradictory instructions - Over-specification that kills creativity - Missing output format specification - No examples when few-shot would help - Ignoring the model's strengths (multimodal, reasoning, etc.)


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