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ai-model-nodejsAI model Node.js 控制

Agent Skill

ai-model-nodejs 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

941

周安装

40

GitHub Stars

997

下载量

330
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

3

许可证

MIT

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:ai-model-nodejs(AI model Node.js 控制)
来源仓库:https://github.com/tencentcloudbase/cloudbase-mcp
仓库路径:skills/ai-model-nodejs
安装命令:
npx skills add https://github.com/tencentcloudbase/cloudbase-mcp --skill ai-model-nodejs
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。不同来源提供的安装方式可能略有差异;本站展示可直接复制的安装命令,安装前请核对来源页面。

skills.shnpx skills
npx skills add https://github.com/tencentcloudbase/cloudbase-mcp --skill ai-model-nodejs

简介

ai-model-nodejs 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中围绕仓库状态、代码变更或协作事项进行整理。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装,需结合原始 README 核验具体用法。
  • 安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。
  • 当前分类为 AI 工具,暂无更多功能细节可参考。

SKILL.md

When 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 ai-model-web skill
  • WeChat Mini Program → use ai-model-wechat skill
  • HTTP API integration → use http-api skill

Available Providers and Models

CloudBase provides these built-in providers and models:

ProviderModelsRecommended
hunyuan-exphunyuan-turbos-latest, hunyuan-t1-latest, hunyuan-2.0-thinking-20251109, hunyuan-2.0-instruct-20251111hunyuan-2.0-instruct-20251111
deepseekdeepseek-r1-0528, deepseek-v3-0324, deepseek-v3.2deepseek-v3.2

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: func.timeout (number)
  • Unit: seconds
  • Range: 1 - 900
  • Default: 20 seconds (usually too short for AI operations)

Recommended timeout values:

  • Text generation (generateText): 60-120 seconds
  • Streaming (streamText): 60-120 seconds
  • Image generation (generateImage): 300-900 seconds (recommended: 900s)
  • 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;
}

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

04

需要参考平台分布和安装热度时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Claude Code

30.06%
按下载量换算99

trae

25.23%
按下载量换算83

Antigravity

18.26%
按下载量换算60

Codex

12.89%
按下载量换算43

OpenCode

7.17%
按下载量换算24

Gemini CLI

3.32%
按下载量换算11

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

操作浏览器

该 Skill 可能涉及浏览器控制能力,使用时可能读取或操作网页内容,需要在受控环境中确认权限边界。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。

来源信息

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