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Cloudbase

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TencentCloudBase
cloudbase

Use this skill when you develop, design, build, deploy, debug, migrate, or troubleshoot CloudBase (腾讯云开发, 云开发, TCB, 微信云开发) projects — Web, 微信小程序, 小程序, uni-app, mobile (iOS, Android, Flutter, React Native). Covers UI (页面, 界面, 表单, dashboard, prototype, 原型); auth (登录, 注册, OAuth, publishable key); databases (NoSQL 文档数据库, MySQL 关系型数据库, PostgreSQL/CloudBase PG, app.rdb(), queryPgDatabase/managePgDatabase, CRUD, security rules); 云函数/cloud functions (serverless, scf_bootstrap); CloudRun (云托管, Dockerfile); 云存储; built-in AI (内置大模型, AI 对话, streaming, 流式输出, 图片生成, generateText, streamText, createModel, generateImage, TokenHub, Hunyuan, DeepSeek, GLM, Kimi, Token Credits 资源包, 小程序成长计划); third-party/custom model onboarding (第三方大模型接入, 大模型调用, LLM API); AI agent (智能体, AG-UI, LangGraph); ops troubleshooting (巡检, 诊断, 日志); spec workflow (需求文档, 技术方案, requirements, tasks.md). Do NOT use for non-CloudBase projects, pure frontend without CloudBase, or self-hosted backends without CloudBase.

Overview

PublisherTencentCloudBase
RepositoryCloudBase-AI-Toolkit
Skill namecloudbase
Stars
1.1K
Forks
141
Bundled files
6
LicenseMIT
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 6 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by TencentCloudBase on GitHub. Read the source before you install it.

Installation

Install the Cloudbase AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/TencentCloudBase/CloudBase-AI-Toolkit.git /tmp/CloudBase-AI-Toolkit
mkdir -p .claude/skills
cp -r /tmp/CloudBase-AI-Toolkit/config/source/guideline/cloudbase .claude/skills/cloudbase
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cloudbase in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cloudbase on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cloudbase is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

CloudBase Development Guidelines

Step 0 — Confirm the site (domestic vs international)

CloudBase has two independent account systems: 国内站 (domestic, cloud.tencent.com) and 国际站 (international, tencentcloud.com). Environments, consoles, API keys, and login state do not cross over. A wrong-site login usually looks like "logged in, but no environments visible" rather than a clear error — so settle the site before installing MCP, logging in, or binding an env.

Infer it when you can (console domain, envId, an existing error); otherwise ask the user once. Do not guess.

国内站 domestic国际站 international
Remote MCP (preferred)https://tcb-api.cloud.tencent.com/mcp/v1https://tcb-api.tencentcloud.com/mcp/v1
Local stdio MCPdefault — nothing to setTCB_SITE=intl + TCB_REGION=ap-singapore
tcb CLIdefaultTCB_IS_INTL=true (or tcb config set isIntl true)
Consoletcb.cloud.tencent.comtcb.tencentcloud.com
Default regionap-shanghaiap-singapore
NoSQL / document DB toolsavailablenot available
  • International users: connect the international remote MCP endpoint directly — https://tcb-api.tencentcloud.com/mcp/v1. It is a first-class hosted endpoint; OAuth covers the login. The site is decided by the host, so there is no site query parameter to pass.
  • Domestic remote MCP is the same shape at https://tcb-api.cloud.tencent.com/mcp/v1 — that stays the default for domestic users.
  • TCB_IS_INTL (CLI) and TCB_SITE (MCP) are different variable names for different tools. Set the one matching the tool in use; setting the wrong one silently does nothing.

Details and copy-paste configs: references/mcp-setup.md. CLI specifics: references/tooling-fallback.md.

Workflow

1. Exploration  →  Read the matching skill completely before writing any code.
                   Search with searchKnowledgeBase(mode="skill"), then Read full SKILL.md.
2. Implementation
   ├── 2a. Resource preparation → Prefer MCP; if MCP tools are missing in THIS session,
   │     configure MCP for next session and use `tcb` CLI now (see tooling-fallback.md)
   └── 2b. Frontend implementation → Write code, install deps, start server, test
3. Close-out  →  Run cloudbase-code-review, fix errors, declare done
                   (after a verified deploy: optionally offer Deployment Share once — see references/deployment-workflow.md §5)

Key constraints: Stage 2a must precede frontend code. Stage 3 is mandatory.

Activation Contract

Routing uses stable skill ids (auth-tool-cloudbase, auth-web-cloudbase, http-api-cloudbase, …) across source, generated artifacts, and installs.

Standalone skill fallback

If only one published skill is exposed:

  • Prefer local relative paths (references/<skill-id>/SKILL.md or sibling skill directories) when those files exist in the workspace.
  • Do not fetch sibling skill markdown from remote raw URLs into the agent context.
  • If a required sibling skill is missing locally, ask the user to install the full CloudBase skills pack or IDE plugin (npx skills add tencentcloudbase/cloudbase-skills), then continue using local files only.

Follow relative references/... paths from the current skill. If MCP is unavailable in this session, follow references/tooling-fallback.md: configure MCP via references/mcp-setup.md for the next session, and use tcb CLI via the cloudbase-cli skill (read core.md + the matching domain reference — not tcb deploy) to finish login/manage now. If npm/npx are missing, follow the “No npm/npx” section in tooling-fallback.md.

Global rules before action

  • Identify the scenario, then read the matching skill before writing code or calling CloudBase APIs.
  • Prefer semantic sources for toolkit maintenance; express runtime routing in stable skill ids.
  • Prefer MCP or mcporter for management tasks when those tools are available in this session; inspect tool schemas before execution. If they are not available yet, do not stall — use the CLI fallback in references/tooling-fallback.md.
  • UI tasks: read ui-design first and output the design spec before interface code.
  • Auth tasks: read auth-tool-cloudbase first and enable providers before frontend implementation.
  • Keep auth domains separate: management login uses auth (or tcb login when MCP auth is unavailable); app-side auth uses queryAppAuth / manageAppAuth.

Universal guardrails

  • After 2–3 failed attempts on the same path, stop and reroute (platform skill, runtime, auth domain, permission model, SDK boundary).
  • Always specify EnvId explicitly; do not rely on CLI-selected or implicit env state.
  • When the environment identifier is an alias, nickname, or other short form, do not pass it directly to auth.set_env, SDK init, console URLs, or generated config. First resolve it to the canonical full EnvId with queryEnv(action=list, alias=..., aliasExact=true). If multiple environments match or no exact alias exists, stop and clarify with the user.
  • When writing MCP/tool results to a file, pass serialized text (JSON.stringify(result, null, 2)), not raw objects. If a write tool says content expected a string but received an object, do not retry with the same raw object. Serialize the object first, then retry once with the serialized text, and make sure the retried call actually passes the serialized string rather than the original object.
  • Keep scenario-specific pitfalls in child skills — do not expand this entry file.
  • First frontend deploy must use manageApps(action="createApp", ...). manageHosting is only for incremental updates of projects originally deployed via hosting.

Engineering constitution (applies to every scenario)

These rules override convenience. Full rationale lives in web-development.

  • Prepare backend resources before writing frontend code. Prefer MCP for auth providers, tables, storage domains, and security rules; if MCP tools are missing in this session, use tcb CLI after configuring MCP for the next session (references/tooling-fallback.md).
  • Do NOT use any to bypass type errors. Prefer unknown + type guards / precise interfaces.
  • Self-verify before claiming done. Static (tsc / lint / build / tests) and runtime (agent-browser for user-visible flows). Name gaps explicitly if a layer cannot run.
  • Do not paper over failures. No empty try/catch, no deleting failing tests to go green.
  • ai.createModel(...) / wx.cloud.extend.AI.createModel(provider) takes a GroupName, not a vendor/model id. Legal: "cloudbase", "hunyuan-exp", or "custom-<name>". Model ids go in generateText / streamText model field. See ai-model-web / ai-model-nodejs / ai-model-wechat.
  • Low-capability STOP card: For PostgreSQL / CloudBase PG / app.rdb() / queryPgDatabase / managePgDatabase, route to postgresql-development-cloudbase — do not use NoSQL/manageMysqlDatabase for that path. For Web auth guards, use auth.getSession() and require data.session; do not use deprecated getLoginState() / auth.getUser() as login proof.

High-priority routing

ScenarioRead firstThen readDo NOT route to firstMust check before action
Minimal Web BaaS demo (fast path)minimal-web-baas-demoweb-development, no-sql-web-sdk, postgresql-developmentcloud-functions, cloudrun, spec-workflow, ui-designBaaS-first Web SDK CRUD, MCP schema only, zero cloud functions unless secrets/cron/rules-cannot-express
Web login / registration / auth UIauth-tool-cloudbaseauth-web, web-developmentcloud-functions, http-apiProvider status and publishable key
WeChat mini program + CloudBaseminiprogram-developmentauth-wechat, no-sql-wx-mp-sdkauth-web, web-developmentWhether the project really uses CloudBase / wx.cloud
Native App / Flutter / React Nativehttp-api-cloudbaseauth-tool, relational-database-toolauth-web, cloudbase-document-database-web-sdk, web-developmentSDK boundary, OpenAPI, auth method
Web projects + NoSQL Databaseweb-developmentno-sql-web-sdk, auth-webrelational-database-tool, http-apiLogin state and database access permission model
CloudBase PostgreSQL / PGpostgresql-development-cloudbaseauth-tool, auth-web-cloudbase, web-development, miniprogram-development, cloud-storage-web, http-apirelational-database-tool, no-sql-web-sdkPG schema, usernamePassword login, backend/RLS permission model
MySQL Database (relational)relational-database-mcp-cloudbaserelational-database-web, http-apino-sql-web-sdk, web-developmentDistinguish MCP management vs app code access
Cloud Functionscloud-functionsauth-tool, ai-model-nodejscloudrun-development, auth-webEvent vs HTTP function, runtime, scf_bootstrap
CloudRun backendcloudrun-developmentauth-tool, relational-database-toolcloud-functionsContainer boundary, Dockerfile, CORS
AI Agent (智能体开发)cloudbase-agentcloud-functions, cloudrun-developmentcloud-functions, cloudrun-developmentAG-UI protocol, scf_bootstrap, SSE streaming
AI model call (大模型调用 / 文本生成 / 图片生成 / 流式对话)ai-model-webai-model-nodejs, ai-model-wechatcloudbase-agent, cloud-functions, cloudrun-development先跑「调用前必须的资格检查」：DescribeActivityInfo（小程序成长计划） + DescribeEnvPostpayPackage（Token Credits 资源包）
UI generationui-designweb-development, miniprogram-developmentcloud-functionsDesign specification first
AI Model (Web)web-developmentai-model-web, ui-designai-model-wechat, http-apiPlatform and streaming interaction mode
Resource health inspection / troubleshootingops-inspectorcloud-functions, cloudrun-developmentui-design, spec-workflowCLS enabled; use queryEnv(action=metrics) for QPS/CPU (never callCloudApi); time range for logs
Spec workflow / architecture designspec-workflowcloudbaseweb-development, cloud-functionsRequirements, design, tasks confirmed

Routing reminders

  • Web auth failures: usually skipped provider config, not missing frontend snippets.
  • Native App failures: usually Web SDK paths, not missing HTTP API knowledge.
  • Mini program failures: treating wx.cloud like Web auth/SDK.
  • CloudBase PG failures: falling back to MySQL/NoSQL, skipping username-password readiness, or guessing raw HTTP instead of app.rdb() / documented OpenAPI.
  • AI model failures: usually missing Token Credits / Growth Plan — run DescribeEnvPostpayPackage / DescribeActivityInfo before changing code.

MCP + CLI prerequisite

Prefer CloudBase MCP for management/deploy when tools are loaded in the current session. Setup: references/mcp-setup.md. First-session / unavailable path: references/tooling-fallback.md.

  • Preferred install: npx plugins add TencentCloudBase/cloudbase-plugin -y --scope user. Supported --target IDs: claude-code, cursor, codex, grok, kimi, github-copilot, vscode. See references/mcp-setup.md.
  • Verify with npx mcporter list | grep cloudbase or the IDE MCP panel. If npm/npx are missing, see references/tooling-fallback.md (install Node LTS or use IDE marketplace MCP). If MCP is missing or not yet visible after config, still proceed: finish install/config, tell the user a restart unlocks MCP next time, and use tcb CLI now via cloudbase-cli domain skills — do not recommend tcb deploy.
  • Prefer device-code login via MCP auth when available; otherwise tcb login. Do not hard-code secrets.

On-demand references

Load only when needed (do not expand this entry):

  • references/tooling-fallback.md — MCP vs tcb CLI decision tree for first session / missing tools
  • references/deployment-workflow.md — deploy backend/frontend, manageApps vs hosting, URL/docs updates, optional post-deployment Deployment Share offer (§5)
  • references/console-links.md — console hash paths after creating resources
  • references/scenarios.md — user-need → CloudBase capability mapping
  • references/mcp-setup.md — Plugin install (global default + targets), IDE MCP / mcporter config and auth examples
  • references/activation-map.yaml — canonical routing contract source

Reference index

All packaged reference files (required for skill lint reachability):

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cloudbase AI skill do?

Use this skill when you develop, design, build, deploy, debug, migrate, or troubleshoot CloudBase (腾讯云开发, 云开发, TCB, 微信云开发) projects — Web, 微信小程序, 小程序, uni-app, mobile (iOS, Android, Flutter, React Native). Covers UI (页面, 界面, 表单, dashboard, prototype, 原型); auth (登录, 注册, OAuth, publishable key); databases (NoSQL 文档数据库, MySQL 关系型数据库, PostgreSQL/CloudBase PG, app.rdb(), queryPgDatabase/managePgDatabase, CRUD, security rules); 云函数/cloud functions (serverless, scf_bootstrap); CloudRun (云托管, Dockerfile); 云存储; built-in AI (内置大模型, AI 对话, streaming, 流式输出, 图片生成, generateText, streamText, createModel,...

Why use Cloudbase on TypingMind?

Because you install it once and use it with any model. Cloudbase is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cloudbase in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TencentCloudBase/CloudBase-AI-Toolkit/tree/main/config/source/guideline/cloudbase. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cloudbase?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cloudbase?

As many as you like. As long as a model supports skills, you can use Cloudbase with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cloudbase AI skill free?

Yes. It is published on GitHub by TencentCloudBase under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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