prompt-architect
GitHub将粗糙想法转化为专业级LLM提示词。通过深度分析输入、强制澄清需求，并运用CoT等框架优化生成高质量提示词。适用于创建、优化或调试提示词场景。
Trigger Scenarios
Install
npx skills add NeverSight/learn-skills.dev --skill prompt-architect -g -y
SKILL.md
Frontmatter
{
"name": "prompt-architect",
"description": "Transform rough ideas into professional-grade LLM prompts. Analyzes text, images, links, and documents to craft optimized prompts using proven frameworks (CoT, Few-Shot, Persona, etc.).\nUSE WHEN: user wants to improve a prompt, create a prompt from scratch, optimize an existing prompt, convert a vague idea into a structured prompt, analyze why a prompt isn't working, or asks \"write me a prompt for...\", \"improve this prompt\", \"prompt engineer this\".\nDON'T USE WHEN: user wants to execute the prompt itself (just run it), wants general writing help without prompt context, asks for code\/articles\/tweets (use appropriate skill instead), or wants to chat about prompt engineering theory without producing a prompt.\nEDGE CASES: - \"Fix this prompt\" → this skill (optimization) - \"Write me a blog post\" → NOT this skill (content creation, not prompt creation) - \"Write me a prompt that generates blog posts\" → this skill - \"Why isn't my prompt working?\" → this skill (diagnosis + fix) - \"اكتب لي برومبت\" → this skill - \"حسن هالبرومبت\" → this skill - \"اكتب لي مقال\" → NOT this skill (use katib-al-maqalat)\nINPUTS: Rough idea, existing prompt, images, links, documents, or any combination. OUTPUTS: Optimized prompt in a code block, ready to copy. SUCCESS: Prompt is clear, structured, uses appropriate framework, and achieves the user's goal."
}
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:
- [Question]
- [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:
- Clarity: No ambiguity in instructions
- Structure: Logical flow, clear sections
- Specificity: Concrete examples over vague descriptions
- Constraints: Explicit boundaries (length, format, tone)
- Framework fit: Right technique for the task
- 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.)
Version History
- e0220ca Current 2026-07-05 22:17
