Using Skills with Any LLM

The same skill works with any LLM. Aerostack generates the right protocol format automatically — you publish once, your skill is accessible from Claude, ChatGPT, and Gemini without rebuilding anything.

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The Problem with Today’s Tool Ecosystem

Most AI tools are tied to a single LLM platform:

Skill format	Claude	ChatGPT	Gemini
OpenAI Actions (GPT Store)	❌	✅	❌
MCP server	✅	✅*	❌
Gemini Extensions	❌	❌	✅

*OpenAI added MCP support in 2026, so MCP servers now work with both Claude and ChatGPT. Gemini still requires its own format.

Aerostack handles the translation layer — one skill definition, every format.

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Option 1: MCP (Claude, Cursor, Windsurf, ChatGPT)

The primary way to use skills is through your workspace gateway. Configure it once in your editor — all installed skills appear as MCP tools:

{
  "mcpServers": {
    "my-workspace": {
      "url": "https://gateway.aerostack.dev/ws/my-workspace",
      "headers": { "Authorization": "Bearer mwt_…" }
    }
  }
}

Works with:

• Claude (via Claude Code, claude.ai with MCP beta)
• Cursor
• Windsurf
• VS Code Copilot
• ChatGPT (via MCP connector, 2026+)
• Any editor that supports MCP

Install a skill → it appears through this URL immediately.

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Option 2: OpenAI Function Format

If you’re using the OpenAI API directly in your code (not via an editor), fetch your skills as OpenAI function definitions:

GET https://gateway.aerostack.dev/ws/{workspaceSlug}/openai-tools
Authorization: Bearer mwt_...

Returns:

[
  {
    "type": "function",
    "function": {
      "name": "github__create_issue",
      "description": "[github] Create a new issue on a GitHub repository",
      "parameters": {
        "type": "object",
        "properties": {
          "owner": { "type": "string", "description": "Repository owner" },
          "repo": { "type": "string", "description": "Repository name" },
          "title": { "type": "string", "description": "Issue title" },
          "body": { "type": "string", "description": "Issue body (markdown)" }
        },
        "required": ["owner", "repo", "title"]
      }
    }
  }
]

Use in your OpenAI API calls:

import OpenAI from 'openai';

const client = new OpenAI();

// Fetch your installed skills in OpenAI format
const toolsResponse = await fetch('https://gateway.aerostack.dev/ws/my-workspace/openai-tools', {
  headers: { Authorization: `Bearer ${process.env.MWT_TOKEN}` }
});
const tools = await toolsResponse.json();

// Use in ChatCompletion
const response = await client.chat.completions.create({
  model: 'gpt-4o',
  tools,
  messages: [{ role: 'user', content: 'Create a GitHub issue for the login bug' }]
});

// When OpenAI calls a tool, route it back through Aerostack gateway
if (response.choices[0].finish_reason === 'tool_calls') {
  const toolCall = response.choices[0].message.tool_calls[0];
  const result = await callAerostackGateway(toolCall.function.name, JSON.parse(toolCall.function.arguments));
  // ...
}

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Option 3: Gemini Tool Declarations

For Google Gemini API users:

GET https://gateway.aerostack.dev/ws/{workspaceSlug}/gemini-tools
Authorization: Bearer mwt_...

Returns Gemini-compatible function declarations:

[
  {
    "name": "github__create_issue",
    "description": "[github] Create a new issue on a GitHub repository",
    "parameters": {
      "type": "OBJECT",
      "properties": {
        "owner": { "type": "STRING", "description": "Repository owner" },
        "repo": { "type": "STRING", "description": "Repository name" },
        "title": { "type": "STRING", "description": "Issue title" }
      },
      "required": ["owner", "repo", "title"]
    }
  }
]

Use with the Gemini API:

import { GoogleGenerativeAI } from '@google/generative-ai';

const genAI = new GoogleGenerativeAI(process.env.GEMINI_API_KEY);

// Fetch tools in Gemini format
const toolsResponse = await fetch('https://gateway.aerostack.dev/ws/my-workspace/gemini-tools', {
  headers: { Authorization: `Bearer ${process.env.MWT_TOKEN}` }
});
const functions = await toolsResponse.json();

const model = genAI.getGenerativeModel({
  model: 'gemini-1.5-pro',
  tools: [{ functionDeclarations: functions }]
});

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Option 4: Install per Editor (Manual)

If you prefer to install skills directly into your editor’s config rather than via a shared workspace:

# Cursor
aerostack skill install @johndoe/github-skill --editor cursor
# → writes entry to ~/.cursor/mcp.json

# Windsurf
aerostack skill install @johndoe/github-skill --editor windsurf
# → writes entry to ~/.codeium/windsurf/mcp_config.json

# Claude Code
aerostack skill install @johndoe/github-skill --editor claude-code
# → writes entry to ~/.claude/mcp.json

# VS Code
aerostack skill install @johndoe/github-skill --editor vscode
# → writes entry to .vscode/mcp.json

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Which Approach to Use

Situation	Recommended approach
Using Claude Code, Cursor, or Windsurf	Workspace gateway (MCP)
Building with the OpenAI API in code	/openai-tools endpoint
Building with the Gemini API in code	/gemini-tools endpoint
Team setup where everyone needs the same skills	Shared workspace gateway
Personal setup, specific editor	--editor flag

The workspace gateway (Option 1) is the most powerful because:

• One config, all editors
• Add skills without touching editor configs
• Works with MCP-compatible LLMs automatically

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Publish Once, Reach All LLMs

When you publish a skill on Aerostack, the tool definitions in your tools[] array use JSON Schema — the same schema format used by MCP, OpenAI, and Gemini (with minor formatting differences). Aerostack generates the format-specific responses at request time.

You write the tool once. Every LLM can use it.