ai-model-nodejs

## Standalone Install Note

If this environment only installed the current skill, start from the CloudBase main entry and use the published `cloudbase/references/...` paths for sibling skills.

- CloudBase main entry: `https://cnb.cool/tencent/cloud/cloudbase/cloudbase-skills/-/git/raw/main/skills/cloudbase/SKILL.md`
- Current skill raw source: `https://cnb.cool/tencent/cloud/cloudbase/cloudbase-skills/-/git/raw/main/skills/cloudbase/references/ai-model-nodejs/SKILL.md`

Keep local `references/...` paths for files that ship with the current skill directory. When this file points to a sibling skill such as `auth-tool` or `web-development`, use the standalone fallback URL shown next to that reference.

## When to use this skill

Use this skill for **calling AI models from Node.js backends, cloud functions, or CloudRun services** via `@cloudbase/node-sdk`.

> 🧭 **Runtime-plane fit.** This is the right skill when the AI call truly belongs on the server: image generation (the only SDK that supports it), long-running agent jobs, orchestration across multiple tools, scheduled tasks, or flows that must keep secrets server-side. **If the user is building a Web page / frontend AI chat UI, do NOT wrap this SDK behind a backend proxy** — route to `ai-model-web` and call the model directly from the browser. For WeChat Mini Programs use `ai-model-wechat`. Routing is decided by runtime plane first; the concrete model (`deepseek-*`, `glm-*`, `hunyuan-*`, `kimi-*`, …) only affects the `model` field.

**Use it when you need to:**

- Integrate AI text generation into a backend service
- Generate images with the Hunyuan Image model
- Call AI models from CloudBase cloud functions or CloudRun
- Do server-side AI processing (agent orchestration, batch jobs, scheduled tasks)

**Do NOT use for:**

- Browser/Web apps → use the `ai-model-web` skill
- WeChat Mini Program → use the `ai-model-wechat` skill
- Runtimes without a CloudBase SDK (Python, Go, PHP, curl, etc.) → use the `http-api` skill (it now includes the `ai_model` OpenAPI spec for direct HTTP calls to the AI model endpoint; do NOT wrap this SDK behind an HTTP proxy)

---

## ⛔ STOP — `ai.createModel(...)` argument is **not** a vendor / model name

Read this before writing any `createModel(...)` line. Agents frequently hallucinate this argument. There are **exactly three** legal shapes. Anything else is a bug.

| ✅ Legal `ai.createModel(...)` argument | When to use it |
|----------------------------------------|----------------|
| `"cloudbase"` | **The main managed group for server-side projects** (TokenHub-backed, multi-vendor pool). Vendor + concrete model go into the **`model` field** of `generateText` / `streamText`, e.g. `{ model: "deepseek-v4-flash" }`. **No model is enabled by default — always check `DescribeAIModels` first and, if the target model is missing, enable it with `UpdateAIModel` before calling the SDK.** |
| `"hunyuan-exp"` | Only if `DescribeAIModels` explicitly returns this legacy builtin group for the current env. |
| `"custom-<your-name>"` | A user-defined GroupName you onboarded via `CreateAIModel`. **Must** start with `custom-` (e.g. `custom-kimi`, `custom-openai-compat`). |

> Image generation is a separate entry point: `ai.createImageModel("hunyuan-image")`. Do not mix it with `createModel(...)`.

### ❌ Do NOT write any of these — they are all wrong

```js
ai.createModel("deepseek")                 // wrong — that's a vendor, not a GroupName
ai.createModel("deepseek-v4-flash")        // wrong — model id goes in the `model` field
ai.createModel("hunyuan") / "hunyuan-2.0-instruct-20251111"  // wrong — vendor / model name
ai.createModel("glm") / "kimi" / "minimax"  // wrong — vendor names
ai.createModel("openai") / "moonshot"       // wrong — vendor names
ai.createModel("custom")                   // wrong — placeholder; use your real custom-<name>
ai.createModel(modelName)                  // wrong — do not reuse the variable that holds the model id
```

### ✅ Correct pattern — GroupName vs Model are two different fields

```js
const model = ai.createModel("cloudbase");          // ← GroupName
await model.generateText({
  model: "deepseek-v4-flash",                       // ← concrete model id
  messages: [...]
});
```

### Decision procedure (when the user names a specific model)

1. The user says "use DeepSeek v3.2" / "use hunyuan instruct" / "use Kimi k2.6" / "use GLM-5" / …
2. `createModel("cloudbase")` stays the same.
3. Put the model id into the **`model` field**: `{ model: "deepseek-v3.2" }`, `{ model: "hunyuan-2.0-instruct-20251111" }`, `{ model: "kimi-k2.6" }`, `{ model: "glm-5" }`, …
4. **Never assume the model is already enabled.** Before calling the SDK, verify it is present in `DescribeAIModels({ GroupName: "cloudbase" }).Models[]`. If missing, call `DescribeManagedAIModelList` to confirm the exact `Model` name the platform supports (case-sensitive — do **not** guess the spelling) and then enable it via `UpdateAIModel` with `Status: 1` (remember `Models` is a full replacement).

> If you are about to type `ai.createModel(` and the thing inside the parentheses is a vendor name, a model name, or a guess — **stop**. It is almost certainly one of the three legal values above.

---

## Mandatory Two-Step Preflight (before any SDK code)

Before calling any AI API on the server, **run the two-step preflight**: ① eligibility, ② group readiness. **Text generation and image generation draw from the same Token Credits resource pack**, and both must complete the preflight before code is emitted.

### Step 0: obtain the environment ID

Call the MCP tool `envQuery` with `action=info` and read `EnvId` from the response.

---

### Preflight ① — Eligibility (Token Credits resource pack)

Call the MCP tool:

```
callCloudApi(service="tcb", action="DescribeEnvPostpayPackage", params={ EnvId })
```

**Pass conditions (all required):**
- `envPostpayPackageInfoList` contains at least one entry
- That entry's `postpayPackageId` starts wi