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    neversight

    prompt-design

    neversight/prompt-design
    AI & ML
    2
    1 installs

    About

    SKILL.md

    Install

    Install via Skills CLI

    or add to your agent
    • Claude Code
      Claude Code
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      OpenClaw
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    ├─
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    └─

    About

    Design MCP prompts to expose reusable prompt templates. Use when creating parameterized prompts in xmcp.

    SKILL.md

    Create xmcp Prompt

    You are helping the user create a new xmcp prompt. Follow this interactive workflow.

    Step 1: Gather Information

    Before generating any code, ask the user these questions using AskUserQuestion:

    1. Prompt name (if not provided): What should the prompt be named? (Use kebab-case)

    2. Prompt type: Ask which type of prompt they need:

      • Simple - Static prompt text without parameters
      • Parameterized - Prompt with user-provided parameters (most common)
      • Multi-content - Returns multiple content blocks (text, images, etc.)
    3. Parameters (if parameterized): What inputs should the prompt accept?

      • Parameter name
      • Type (string, number, enum)
      • Description
      • Whether it's optional
      • Any validation or default values
    4. Role: What role should the prompt assume?

      • user - Prompt from user perspective (most common)
      • assistant - Prompt as assistant response template
    5. Use case: What is the prompt designed for?

      • Code review
      • Documentation generation
      • Data analysis
      • Content creation
      • Other specific task

    Step 2: Generate the Prompt

    Create the prompt file in src/prompts/:

    File Location

    src/prompts/{prompt-name}.ts
    

    Prompt Structure Reference

    Every xmcp prompt has three main exports:

    // 1. Schema (optional) - Define parameters with Zod
    export const schema = { /* ... */ };
    
    // 2. Metadata (optional) - Prompt configuration
    export const metadata: PromptMetadata = { /* ... */ };
    
    // 3. Handler (required) - Default export function
    export default function handler(params?) { /* ... */ }
    

    Quick Reference

    Essential Imports

    import { type PromptMetadata } from "xmcp";
    
    // For parameterized prompts
    import { z } from "zod";
    import { type InferSchema, type PromptMetadata } from "xmcp";
    

    Metadata Fields

    Field Type Required Description
    name string No* Unique prompt identifier
    title string No Human-readable title
    description string No What this prompt does
    role string No "user" or "assistant" (default: user)

    *When metadata is omitted, xmcp uses the filename as the prompt name.

    Handler Return Types

    Return Type Use Case
    string Simple text prompt
    { type: "text", text: string } Explicit text content
    { type: "image", data: base64, mimeType: string } Image content
    Array<Content> Multiple content blocks

    Use Cases for Server-Exposed Prompts

    MCP prompts are useful for:

    1. Standardized workflows - Consistent code review, documentation patterns
    2. Complex templates - Multi-step analysis prompts with structure
    3. Domain expertise - Specialized prompts for specific domains
    4. Reusable patterns - Common tasks that benefit from parameterization
    5. Client integration - Prompts that MCP clients can discover and use

    Detailed Templates

    For complete code templates including:

    • Simple prompt patterns
    • Parameterized prompts with Zod schemas
    • Multi-role conversation prompts
    • Autocompletion support
    • Code review and documentation examples

    Read: references/patterns.md


    Checklist After Generation

    1. File created in src/prompts/{prompt-name}.ts
    2. If using metadata, ensure it has name, title, and description
    3. Schema uses .describe() for all parameters (if parameterized)
    4. Handler returns appropriate content format
    5. Role is set appropriately for the use case

    Suggest running pnpm build to verify the prompt compiles correctly.

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    neversight/skills_feed