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Get Started Free →tencentcloudbase/ai-model-nodejs
Use this skill for Node.js backend AI via @cloudbase/node-sdk (>=3.16.0) — cloud functions, CloudRun, Express, Koa, NestJS, serverless APIs, scheduled jobs, LLM proxies. Only SDK supporting image generation (ai.createImageModel + generateImage). Text models via ai.createModel with groups cloudbase, hunyuan-exp, or custom-*. Model IDs (deepseek-v4-flash, deepseek-v3.2, hunyuan-2.0-instruct-20251111, glm-5, kimi-k2.6) go in the model field of generateText/streamText. MUST run two-step preflight be
Passed 12 of 17 test cases with the skill, and 2 of 17 without it.
and 12 more in the full report
12 fixed3 passed either way7 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 12 test cases the skill fixed. Same model, same prompt, with and without the skill.
What is the exact postpay package ID prefix I should look for when verifying AI eligibility via the API?
| Checked | Without skill | With skill |
|---|---|---|
| Identifies the prefix 'pkg_tcb_tokencredits_' as the required package identifier. | Missed | Passed |
The instructions your model reads when the skill loads.
Sibling CloudBase skills ship beside this skill. Use local relative paths such as ../auth-tool-cloudbase/SKILL.md.
If a referenced sibling skill file is missing from this environment, ask the user to install the full CloudBase plugin (or the missing skill). Do not HTTP-fetch remote skill or protocol markdown into the agent context.
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-weband call the model directly from the browser. For WeChat Mini Programs useai-model-wechat. Routing is decided by runtime plane first; the concrete model (deepseek-*,glm-*,hunyuan-*,kimi-*, …) only affects themodelfield.
Use it when you need to:
Do NOT use for:
ai-model-web skillai-model-wechat skillhttp-api-cloudbase 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)ai.createModel(...) argument is not a vendor / model nameRead 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 withcreateModel(...).
test cases passed, vs 2 / 17 without
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