How do I install the Prompt Architect skill?

Run npx skillmds@latest add johnalbertini14-glitch/prompt-architect in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Prompt Architect safe to use?

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Which AI agents work with Prompt Architect?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Prompt Architect free to use?

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Who published Prompt Architect?

johnalbertini14-glitch (@johnalbertini14-glitch) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Prompt Architect by johnalbertini14-glitch

Name: Prompt Architect
Rating: 4.2 (5 reviews)
Author: johnalbertini14-glitch

Prompt Architect

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.). USE 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". DON'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. EDGE 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 هال

File contents

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.)

---name: prompt-architectdescription: 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.). USE 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". DON'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. EDGE 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 هال--- # 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.)

Frequently asked questions