Skills ai-model-nodejs
Use this skill when developing Node.js backend services or CloudBase cloud functions (Express/Koa/NestJS, serverless, backend APIs) that need AI capabilities. Features text generation (generateText), streaming (streamText), AND image generation (generateImage) via @cloudbase/node-sdk ≥3.16.0. Built-in models include Hunyuan (hunyuan-2.0-instruct-20251111 recommended), DeepSeek (deepseek-v3.2 recommended), and hunyuan-image for images. This is the ONLY SDK that supports image generation. NOT for browser/Web apps (use ai-model-web) or WeChat Mini Program (use ai-model-wechat).
git clone https://github.com/openclaw/skills
T=$(mktemp -d) && git clone --depth=1 https://github.com/openclaw/skills "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/binggg/cloudbase/references/ai-model-nodejs" ~/.claude/skills/clawdbot-skills-ai-model-nodejs && rm -rf "$T"
skills/binggg/cloudbase/references/ai-model-nodejs/SKILL.mdWhen to use this skill
Use this skill for calling AI models in Node.js backend or CloudBase cloud functions using
@cloudbase/node-sdk.
Use it when you need to:
- Integrate AI text generation in backend services
- Generate images with Hunyuan Image model
- Call AI models from CloudBase cloud functions
- Server-side AI processing
Do NOT use for:
- Browser/Web apps → use
skillai-model-web - WeChat Mini Program → use
skillai-model-wechat - HTTP API integration → use
skillhttp-api
Available Providers and Models
CloudBase provides these built-in providers and models:
| Provider | Models | Recommended |
|---|---|---|
| , , , | ✅ |
| , , | ✅ |
Installation
npm install @cloudbase/node-sdk
⚠️ AI feature requires version 3.16.0 or above. Check with
npm list @cloudbase/node-sdk.
Initialization
In Cloud Functions
const tcb = require('@cloudbase/node-sdk'); const app = tcb.init({ env: '<YOUR_ENV_ID>' }); exports.main = async (event, context) => { const ai = app.ai(); // Use AI features };
Cloud Function Configuration for AI Models
⚠️ Important: When creating cloud functions that use AI models (especially
generateImage() and large language model generation), set a longer timeout as these operations can be slow.
Using MCP Tool
:createFunction
Set the
timeout parameter in the func object:
- Parameter:
(number)func.timeout - Unit: seconds
- Range: 1 - 900
- Default: 20 seconds (usually too short for AI operations)
Recommended timeout values:
- Text generation (
): 60-120 secondsgenerateText - Streaming (
): 60-120 secondsstreamText - Image generation (
): 300-900 seconds (recommended: 900s)generateImage - Combined operations: 900 seconds (maximum allowed)
In Regular Node.js Server
const tcb = require('@cloudbase/node-sdk'); const app = tcb.init({ env: '<YOUR_ENV_ID>', secretId: '<YOUR_SECRET_ID>', secretKey: '<YOUR_SECRET_KEY>' }); const ai = app.ai();
generateText() - Non-streaming
const model = ai.createModel("hunyuan-exp"); const result = await model.generateText({ model: "hunyuan-2.0-instruct-20251111", // Recommended model messages: [{ role: "user", content: "你好,请你介绍一下李白" }], }); console.log(result.text); // Generated text string console.log(result.usage); // { prompt_tokens, completion_tokens, total_tokens } console.log(result.messages); // Full message history console.log(result.rawResponses); // Raw model responses
streamText() - Streaming
const model = ai.createModel("hunyuan-exp"); const res = await model.streamText({ model: "hunyuan-2.0-instruct-20251111", // Recommended model messages: [{ role: "user", content: "你好,请你介绍一下李白" }], }); // Option 1: Iterate text stream (recommended) for await (let text of res.textStream) { console.log(text); // Incremental text chunks } // Option 2: Iterate data stream for full response data for await (let data of res.dataStream) { console.log(data); // Full response chunk with metadata } // Option 3: Get final results const messages = await res.messages; // Full message history const usage = await res.usage; // Token usage
generateImage() - Image Generation
⚠️ Image generation is only available in Node SDK, not in JS SDK (Web) or WeChat Mini Program.
const imageModel = ai.createImageModel("hunyuan-image"); const res = await imageModel.generateImage({ model: "hunyuan-image", prompt: "一只可爱的猫咪在草地上玩耍", size: "1024x1024", version: "v1.9", }); console.log(res.data[0].url); // Image URL (valid 24 hours) console.log(res.data[0].revised_prompt);// Revised prompt if revise=true
Image Generation Parameters
interface HunyuanGenerateImageInput { model: "hunyuan-image"; // Required prompt: string; // Required: image description version?: "v1.8.1" | "v1.9"; // Default: "v1.8.1" size?: string; // Default: "1024x1024" negative_prompt?: string; // v1.9 only style?: string; // v1.9 only revise?: boolean; // Default: true n?: number; // Default: 1 footnote?: string; // Watermark, max 16 chars seed?: number; // Range: [1, 4294967295] } interface HunyuanGenerateImageOutput { id: string; created: number; data: Array<{ url: string; // Image URL (24h valid) revised_prompt?: string; }>; }
Type Definitions
interface BaseChatModelInput { model: string; // Required: model name messages: Array<ChatModelMessage>; // Required: message array temperature?: number; // Optional: sampling temperature topP?: number; // Optional: nucleus sampling } type ChatModelMessage = | { role: "user"; content: string } | { role: "system"; content: string } | { role: "assistant"; content: string }; interface GenerateTextResult { text: string; // Generated text messages: Array<ChatModelMessage>; // Full message history usage: Usage; // Token usage rawResponses: Array<unknown>; // Raw model responses error?: unknown; // Error if any } interface StreamTextResult { textStream: AsyncIterable<string>; // Incremental text stream dataStream: AsyncIterable<DataChunk>; // Full data stream messages: Promise<ChatModelMessage[]>;// Final message history usage: Promise<Usage>; // Final token usage error?: unknown; // Error if any } interface Usage { prompt_tokens: number; completion_tokens: number; total_tokens: number; }