agentleFS
ficaviolaodorata520ficaviolaodorata520/met-museum-mcp-server/skills/add-prompt/SKILL.md

add-prompt

Skill0 starsChanged 6 months agoApache-2.0View on GitHub

Scaffold a new MCP prompt template. Use when the user asks to add a prompt, create a reusable message template, or define a prompt for LLM interactions.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

1 rule. About 0 are common, 0 are distinctive, and 1 is very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈0

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 1

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Very specific:

    If the repo already uses src/mcp-server/prompts/definitions/index.ts, add the export to that barrel instead:

    line 89

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

Context

Prompts use the prompt() builder from @cyanheads/mcp-ts-core. Each prompt lives in src/mcp-server/prompts/definitions/ with a .prompt.ts suffix. The standard registration pattern uses a definitions/index.ts barrel that collects all prompts into an allPromptDefinitions array for createApp(). Fresh scaffolds start with direct imports in src/index.ts — the barrel is introduced as definitions grow. Match the pattern already used by the project you're editing.

Prompts are pure message templates — no Context, no auth, no side effects. generate can be sync or async (returns PromptMessage[] | Promise<PromptMessage[]>).

Steps

  1. Gather the prompt's name, purpose, and arguments from the user's request — ask only if genuinely absent
  2. Create the file at src/mcp-server/prompts/definitions/{{prompt-name}}.prompt.ts
  3. Register the prompt in the project's existing createApp() prompt list (directly in src/index.ts for fresh scaffolds, or via a barrel if the repo already has one)
  4. Run bun run devcheck to verify

Template

/**
 * @fileoverview {{PROMPT_DESCRIPTION}}
 * @module mcp-server/prompts/definitions/{{PROMPT_NAME}}
 */

import { prompt, z } from '@cyanheads/mcp-ts-core';

export const {{PROMPT_EXPORT}} = prompt('{{prompt_name}}', {
  description: '{{PROMPT_DESCRIPTION}}',
  // args is optional — omit entirely for prompts with no parameters.
  // When present, all fields need .describe(). Only JSON-Schema-serializable types allowed.
  args: z.object({
    // All fields need .describe()
  }),
  generate: (args) => [
    {
      role: 'user',
      content: {
        type: 'text',
        text: `{{PROMPT_TEMPLATE_TEXT}}`,
      },
    },
  ],
});

Multi-message prompt

generate: (args) => [
  {
    role: 'user',
    content: {
      type: 'text',
      text: `Here is the ${args.type} to review:\n\n${args.content}`,
    },
  },
  {
    role: 'assistant',
    content: {
      type: 'text',
      text: 'I will analyze this carefully. Let me start with...',
    },
  },
],

Registration

// src/index.ts (fresh scaffold default)
import { createApp } from '@cyanheads/mcp-ts-core';
import { {{PROMPT_EXPORT}} } from './mcp-server/prompts/definitions/{{prompt-name}}.prompt.js';

await createApp({
  tools: [/* existing tools */],
  resources: [/* existing resources */],
  prompts: [{{PROMPT_EXPORT}}],
});

If the repo already uses src/mcp-server/prompts/definitions/index.ts, add the export to that barrel instead:

export { {{PROMPT_EXPORT}} } from './{{prompt-name}}.prompt.js';

Checklist

  • File created at src/mcp-server/prompts/definitions/{{prompt-name}}.prompt.ts
  • Prompt name passed to prompt() uses snake_case
  • description field set (lint warns if absent, but devcheck won't hard-fail — verify it's present)
  • All Zod args fields have .describe() annotations — or args omitted entirely for no-parameter prompts
  • args fields use only JSON-Schema-serializable Zod types (no z.date(), z.transform(), z.bigint(), z.symbol(), z.custom(), etc.)
  • JSDoc @fileoverview and @module header present
  • generate function present and returns at least one { role, content: { type: 'text', text } } message
  • No side effects — prompts are pure templates
  • Registered in the project's existing createApp() prompt list (directly or via barrel)
  • bun run devcheck passes

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.