Use this skill for WeChat Mini Program AI via wx.cloud.extend.AI (小程序, 企业微信小程序, wx.cloud apps). Features generateText and streamText with callbacks (onText, onEvent, onFinish). Models via wx.cloud.extend.AI.createModel with groups hunyuan-exp (小程序成长计划), cloudbase (main managed), or custom-*. Model IDs (deepseek-v4-flash, deepseek-v3.2, hunyuan-2.0-instruct-20251111, glm-5, kimi-k2.6) go in the data wrapper model field. API differs from JS/Node SDK — streamText needs data wrapper, generateText returns raw response. MUST run two-step preflight before code — see body. Keywords: Mini Program AI, wx.cloud.extend.AI, 小程序成长计划, ai_miniprogram_inspire_plan, Token Credits 资源包, generateText, streamText, createModel, hunyuan-exp, TokenHub, Hunyuan, DeepSeek, GLM, Kimi, MiniMax. NOT for browser/Web (use ai-model-web), Node.js backend (use ai-model-nodejs), or image generation (use ai-model-nodejs).
Install with Codex or Claude Copy this prompt, paste it into Codex, Claude, or another assistant, and let it review the skill page and install it for you.
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Use this skill for WeChat Mini Program AI via wx.cloud.extend.AI (小程序, 企业微信小程序, wx.cloud apps). Features generateText and streamText with callbacks (onText, onEvent, onFinish). Models via wx.cloud.extend.AI.createModel with groups hunyuan-exp (小程序成长计划), cloudbase (main managed), or custom-*. Model IDs (deepseek-v4-flash, deepseek-v3.2, hunyuan-2.0-instruct-20251111, glm-5, kimi-k2.6) go in the data wrapper model field. API differs from JS/Node SDK — streamText needs data wrapper, generateText returns raw response. MUST run two-step preflight before code — see body. Keywords: Mini Program AI, wx.cloud.extend.AI, 小程序成长计划, ai_miniprogram_inspire_plan, Token Credits 资源包, generateText, streamText, createModel, hunyuan-exp, TokenHub, Hunyuan, DeepSeek, GLM, Kimi, MiniMax. NOT for browser/Web (use ai-model-web), Node.js backend (use ai-model-nodejs), or image generation (use ai-model-nodejs).
version
2.23.2
alwaysApply
false
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-wechat/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 or , use the standalone fallback URL shown next to that reference.
auth-tool
web-development
When to use this skill
Use this skill for calling AI models in WeChat Mini Program using wx.cloud.extend.AI.
Use it when you need to:
Integrate AI text generation in a Mini Program
Stream AI responses with callback support
Call Hunyuan models from the WeChat environment
Do NOT use for:
Browser/Web apps → use ai-model-web skill
Node.js backend or cloud functions → use ai-model-nodejs skill
Image generation → use ai-model-nodejs skill (not available in Mini Program)
Runtimes without a CloudBase SDK (native apps, Python, etc.) → use http-api skill (it now includes the ai_model OpenAPI spec for direct HTTP calls)
⛔ STOP — wx.cloud.extend.AI.createModel(provider) 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 createModel(provider) argument
When to use it
"hunyuan-exp"
The Mini Program 成长计划 (ai_miniprogram_inspire_plan) is enrolled for the current env. Default model: hunyuan-2.0-instruct-20251111.
"cloudbase"
Default fallback. Main managed group (TokenHub-backed, multi-vendor pool). Vendor + concrete model go into the model field, e.g. { model: "deepseek-v4-flash" }.
"custom-<your-name>"
A user-defined GroupName you onboarded via CreateAIModel. Must start with custom- (e.g. custom-kimi, custom-openai-compat).
❌ Do NOT write any of these — they are all wrong
wx.cloud.extend.AI.createModel("deepseek") // wrong — vendor, not GroupName
wx.cloud.extend.AI.createModel("deepseek-v4-flash") // wrong — model id goes in `model`
wx.cloud.extend.AI.createModel("hunyuan") // wrong — vendor family
wx.cloud.extend.AI.createModel("hunyuan-2.0-instruct-20251111") // wrong — model name
wx.cloud.extend.AI.createModel("glm") / "kimi" / "minimax"// wrong — vendor names
wx.cloud.extend.AI.createModel("custom") // wrong — placeholder
wx.cloud.extend.AI.createModel(modelName) // wrong — do not reuse the model-id variable
✅ Correct pattern — provider vs model are two different fields
// Growth Plan branchconst model = wx.cloud.extend.AI.createModel("hunyuan-exp"); // ← provider / GroupNameawait model.streamText({
data: { model: "hunyuan-2.0-instruct-20251111", messages: [...] } // ← concrete model id
});
// Token Credits branchconst model = wx.cloud.extend.AI.createModel("cloudbase");
await model.streamText({
data: { model: "deepseek-v4-flash", messages: [...] }
});
Decision procedure (when the user names a specific model)
The user says "use DeepSeek v3.2" / "use hunyuan thinking" / "use Kimi k2.6" / …
First run the eligibility decision tree below — the correct provider may be "hunyuan-exp" (if the env is on Growth Plan and the user asked for a hunyuan-* model) or "cloudbase" (anything else in the managed catalog).
Put the model id into the model field inside data: { model: "deepseek-v3.2" }, { model: "hunyuan-2.0-instruct-20251111" }, { model: "kimi-k2.6" }, …
Before using the model id, make sure it is present in DescribeAIModels({ GroupName: "cloudbase" }).Models[]. If not, enable it via UpdateAIModel.
If you are about to type wx.cloud.extend.AI.createModel( and the thing inside the parentheses is a vendor name or a model id — stop. It is almost certainly one of the three legal values above.
Mandatory Two-Step Preflight
You MUST NOT jump straight into wx.cloud.extend.AI.createModel(...). Before writing any business code, confirm billing eligibility and group readiness in this fixed order: ① eligibility → ② group readiness. Do not swap the two.
Preflight ① · Billing Eligibility (two parallel billing paths)
The Mini Program side has two billing paths: 小程序成长计划 (checked first; if enrolled, use hunyuan-exp) and Token Credits 资源包 (generic fallback; if available, use the cloudbase main managed group).
Fetch envId via the MCP tool envQuery action=info.
Pick the branch by user intent:
User intent
Eligibility to check first
createModel provider on hit
Model selection
Guidance on miss
No model specified / default call
Check 小程序成长计划 enrollment first; if not enrolled, fall back to Token Credits resource pack
Enrolled: "hunyuan-exp"; otherwise: "cloudbase"
Enrolled: hunyuan-2.0-instruct-20251111 (the 成长计划 default). Otherwise: pick a text model with the user, then verify/enable it in the "cloudbase" group via DescribeAIModels → DescribeManagedAIModelList → UpdateAIModel
Plan not enrolled → point to https://docs.cloudbase.net/ai/ai-inspire-plan; resource pack missing → purchase link
User requests a hunyuan-* model
小程序成长计划 enrollment
"hunyuan-exp" (plan-exclusive Token pack billing)
hunyuan-2.0-instruct-20251111 if present; otherwise verify via DescribeAIModels({ GroupName: "hunyuan-exp" }).Models[] and UpdateAIModel to enable
Not enrolled → enroll first, or switch to "cloudbase" + a non-hunyuan model
User requests deepseek-* / glm-* / kimi-* / minimax-* / other non-hunyuan managed models
Token Credits 资源包 activation
"cloudbase"
Do NOT assume the model is already enabled. DescribeAIModels → if missing, DescribeManagedAIModelList for the canonical Model string → UpdateAIModel with Status: 1 (full-replacement Models[])
Resource pack not activated → purchase link
User requests a third-party / self-hosted (non-managed) model
Skip billing eligibility and go to "Custom onboarding"
Custom GroupName (must start with custom-)
Registered via CreateAIModel.Models[]
Offer both console + CreateAIModel paths
Check 小程序成长计划 enrollment:
callCloudApi({
service: "tcb",
action: "DescribeActivityInfo",
params: {
ActivityNames: ["ai_miniprogram_inspire_plan"], // PascalCase preferred; switch to camelCase if InvalidParameter is returned
},
})
Hit criterion: the response's attendRecords contains at least one entry where activityName === "ai_miniprogram_inspire_plan" and envId matches the current environment. On hit, default to createModel("hunyuan-exp") + hunyuan-2.0-instruct-20251111; billing uses the plan-exclusive Token pack pkg_hunyuan_token_la_inspire_100m.
On miss: do NOT silently fall back. Tell the user "the current environment is not enrolled in 小程序成长计划", surface the enrollment entry https://docs.cloudbase.net/ai/ai-inspire-plan, and ask whether to enroll and retry, or to switch to the Token Credits resource pack path with a non-hunyuan model.
Check the Token Credits resource pack (when the path leads to the "cloudbase" main managed group):
Hit criterion:envPostpayPackageInfoList contains an entry whose postpayPackageId starts with pkg_tcb_tokencredits_, has status ∉ [3, 4] (not expired, not disabled), and versionSwitchStatus is not in a blocking state.
On miss: surface the purchase link (replace {envId} with the real ID — never leave the placeholder):
Preflight ② · Group Readiness (mandatory for every Mini Program AI call)
Passing eligibility does not mean the target model is callable. No model is enabled by default in the "cloudbase" main managed group — you must first call DescribeAIModels to see what is enabled, then (if missing) DescribeManagedAIModelList for the authoritative supported-model catalog and UpdateAIModel with Status: 1 to enable it. The "hunyuan-exp" group's readiness is driven by 成长计划 enrollment — enrollment alone makes hunyuan-2.0-instruct-20251111 available, but any other hunyuan SKU still has to be checked against DescribeAIModels({ GroupName: "hunyuan-exp" }).Models[] and enabled via UpdateAIModel if missing.
Query the groups and switches currently configured in the environment (tcb Action DescribeAIModels, Version 2018-06-08):
Returns AIModelGroups: AIModelGroup[]. Each AIModelGroup has GroupName (e.g. cloudbase / hunyuan-exp / your custom group), Type (builtin / custom), Models: [{ Model, EnableMCP, Tags }], and Status (1=on / 2=off). Group readiness = all three of: the GroupName exists + Status === 1 + the target Model is present in Models[].
If the target model is not in the DescribeAIModels response, query the platform catalog + pricing via DescribeManagedAIModelList — it returns ManagedAIModelGroup[] including ModelSpec (context length, etc.) and ModelChargingInfo (Uniform / Tiered pricing). Pick the target model, then enable it via UpdateAIModel:
callCloudApi({
service: "tcb",
action: "UpdateAIModel",
params: {
EnvId: "<envId>",
GroupName: "cloudbase",
Status: 1, // 1=on, 2=offModels: [
{ Model: "deepseek-v4-flash", EnableMCP: false },
{ Model: "deepseek-v3.2", EnableMCP: false }, // append the new model to enable
],
// ⚠️ `Models` is a FULL REPLACEMENT, not incremental; merge the old list + new entries before passing.
},
})
Once both steps pass, only THEN write wx.cloud.extend.AI.createModel("<GroupName>") in the Mini Program code, and pass a model value that exists in that group's Models[].
Order is fixed. Without eligibility, no enabled model will bill; without group readiness, even with eligibility you will receive ModelNotEnabled-class errors. Both must be done before business code.
API casing tip:tcb public-service Actions officially use PascalCase (EnvId, GroupName, ActivityNames); some docs show camelCase. On the first call, if you hit InvalidParameter, switch casing and retry, then freeze the working form in your project's wrapper.
Available Providers and Models
The provider argument of wx.cloud.extend.AI.createModel(provider) equals the GroupName returned by DescribeAIModels. Only three kinds of values are legal. Run the decision tree before choosing.
A. 小程序成长计划 exclusive (default when enrolled)
createModel provider
Default model
Other available models
Notes
"hunyuan-exp"
hunyuan-2.0-instruct-20251111
Additional hunyuan SKUs (e.g. instruct / thinking / turbos / role variants) — query at runtime via DescribeAIModels({ GroupName: "hunyuan-exp" }).Models[], do NOT hard-code
Legacy Type=builtin GroupName; billed via pkg_hunyuan_token_la_inspire_100m; do NOT use without 成长计划 enrollment
B. Main managed group (Token Credits pack scenario, recommended default)
The "cloudbase" GroupName is backed by Tencent Cloud TokenHub, a unified managed pool that covers multiple first-party and third-party vendors — including the Hunyuan family (HY 2.0 Instruct, HY 2.0 Think, Hunyuan-role, Hy3 preview, …), DeepSeek family (DeepSeek-V4-Pro, DeepSeek-V4-Flash, Deepseek-v3.2, Deepseek-v3.1, Deepseek-r1-0528, Deepseek-v3-0324, …), Zhipu GLM (GLM-5, GLM-5-Turbo, GLM-5.1, GLM-5V-Turbo), Kimi (K2.5, K2.6), MiniMax (M2.5, M2.7) and more. The roster evolves over time, so do not hard-code the list in application code — always discover it at runtime.
createModel provider
Model readiness
How to enable a model
Notes
"cloudbase"
No model is enabled by default — always check DescribeAIModels({ GroupName: "cloudbase" }).Models[] first
1) Fetch the authoritative catalog + pricing via DescribeManagedAIModelList (do NOT guess the Model string). 2) Call UpdateAIModel with Status: 1 and a full-replacement Models[] that includes the target model
Unified managed group (Type=builtin), Remark "腾讯云开发", depends on a pkg_tcb_tokencredits_* resource pack
⚠️ Common Mini Program mistake: writing createModel("deepseek") / createModel("hunyuan") / createModel("glm") / createModel("kimi") / createModel("minimax") / createModel("custom"). All wrong — those are vendor / model names, not provider / GroupName. The provider must be one of the GroupName values returned by DescribeAIModels. New projects always use the unified "cloudbase" managed group and select the concrete vendor model via the model field.
C. Not in the managed catalog → Custom onboarding
Models involving third-party / self-hosted / OpenAI-compatible endpoints (anything not appearing in A/B) do NOT go through the billing paths above. You must register a Type=custom GroupName via "Custom onboarding" first. See the next section.
Custom Onboarding (when not in the managed catalog)
When the user specifies a model that is neither in 成长计划 (hunyuan-exp) nor in the main managed group (cloudbase) catalog (e.g. enterprise-hosted OpenAI-compatible endpoints, third-party model services), pick one of the two paths below. Use neutral phrasing such as "third-party / self-hosted / OpenAI-compatible endpoint" — do not name specific competitor brands.
Path 1 · Register in the console
Point the user to the CloudBase console AI model page:
Replace {envId} with the real environment ID and let the user fill in model name, endpoint, API key, etc.
Path 2 · Register via callCloudApi + CreateAIModel
The tcb Action CreateAIModel (Version 2018-06-08) creates a Type=custom AI model group in the current environment:
callCloudApi({
service: "tcb",
action: "CreateAIModel",
params: {
EnvId: "<current envId>",
GroupName: "custom-openai-compat", // ⚠️ MUST start with "custom-" (e.g. custom-kimi, custom-moonshot) to avoid colliding with built-in / vendor GroupNames; this becomes the value passed to createModel(provider)BaseUrl: "https://api.example.com/v1",
Models: [
{ Model: "gpt-4o-mini", EnableMCP: false },
{ Model: "gpt-4o", EnableMCP: false },
],
Remark: "Internal OpenAI-compatible endpoint",
Status: 1, // 1=on, 2=offSecret: {
// Key / ApiKey: pick one; OpenAI-compatible endpoints usually use ApiKeyApiKey: "<vendor-api-key>",
},
},
})
After registration:
Run DescribeAIModels to confirm the GroupName exists with Status=1 and the target Model appears in Models[].
In the Mini Program, call wx.cloud.extend.AI.createModel("custom-openai-compat") and pass a registered model name (e.g. "gpt-4o-mini") as the model field.
To add or modify models later, use UpdateAIModel (remember Models is a full replacement; Status uses 1/2 as on/off). To delete an entire custom group, use DeleteAIModel (custom groups only; batch via GroupNames.N).
All calls still hit the environment's billing path. If such custom models also need Token settlement, eligibility must be verified first.
⚠️ Different from JS/Node SDK: the return value is the raw model response.
Prerequisite: the "Mandatory Two-Step Preflight" has been completed and the target model has been confirmed enabled via DescribeAIModels (or enabled via UpdateAIModel if missing). The example below assumes the current environment is enrolled in 小程序成长计划 and uses createModel("hunyuan-exp") + hunyuan-2.0-instruct-20251111. If the eligibility branch landed on the resource pack, swap the provider to "cloudbase" and set the model to whatever the user chose and you have just enabled via UpdateAIModel — never assume deepseek-v4-flash is already on.
const model = wx.cloud.extend.AI.createModel("hunyuan-exp");
const res = await model.generateText({
model: "hunyuan-2.0-instruct-20251111", // plan-enrolled defaultmessages: [{ role: "user", content: "hi" }],
});
// ⚠️ Return value is the RAW model response, NOT wrapped like JS/Node SDKconsole.log(res.choices[0].message.content); // access via choices arrayconsole.log(res.usage); // token usage
streamText() - Streaming
⚠️ Different from JS/Node SDK: parameters MUST be wrapped in a data object; callbacks are supported.
Prerequisite: the "Mandatory Two-Step Preflight" has been completed and the target model has been enabled. The example below uses the 成长计划 branch; for the resource pack branch, swap createModel("hunyuan-exp") to createModel("cloudbase") and the model to whatever the user chose and you have just enabled via UpdateAIModel (no model is enabled by default).
const model = wx.cloud.extend.AI.createModel("hunyuan-exp");
// ⚠️ Parameters MUST be wrapped in a `data` objectconst res = await model.streamText({
data: { // ⚠️ Required wrappermodel: "hunyuan-2.0-instruct-20251111", // plan-enrolled defaultmessages: [{ role: "user", content: "hi" }]
},
onText: (text) => { // Optional: incremental text callbackconsole.log("New text:", text);
},
onEvent: ({ data }) => { // Optional: raw event callbackconsole.log("Event:", data);
},
onFinish: (fullText) => { // Optional: completion callbackconsole.log("Done:", fullText);
}
});
// Async iteration is also availableforawait (let str of res.textStream) {
console.log(str);
}
// Check for completion via eventStreamforawait (let event of res.eventStream) {
console.log(event);
if (event.data === "[DONE]") { // ⚠️ Check for [DONE] to stopbreak;
}
}
Error Handling Pattern
Prerequisite: the "Mandatory Two-Step Preflight" has been completed. For the resource pack branch, use "cloudbase" + the specific text model you just verified/enabled via DescribeAIModels / UpdateAIModel — no model is enabled by default.
const model = wx.cloud.extend.AI.createModel("cloudbase");
try {
const res = await model.generateText({
model: "deepseek-v4-flash",
messages: [{ role: "user", content: "Write a welcome message" }],
});
console.log(res.choices[0].message.content);
} catch (error) {
console.error("Mini Program AI request failed", error);
}
API Comparison: JS/Node SDK vs WeChat Mini Program
Run the two-step preflight before writing business code. Fixed order: ① DescribeActivityInfo / DescribeEnvPostpayPackage for billing eligibility → ② DescribeAIModels for group readiness (if needed, DescribeManagedAIModelList for catalog + pricing, then UpdateAIModel to enable the target model). Only after both pass should you write wx.cloud.extend.AI.createModel(...).
createModel(provider) accepts only three kinds of values — "hunyuan-exp" (成长计划 exclusive legacy group), "cloudbase" (main managed group, default for new projects), or the custom-onboarding GroupName (MUST start with custom-, e.g. custom-kimi, custom-openai-compat, to avoid colliding with built-in / vendor names). Never write createModel("deepseek") (unless DescribeAIModels truly returns a legacy builtin group named deepseek), createModel("hunyuan"), createModel("kimi"), or createModel("custom") — these are model/vendor names or placeholders, not GroupNames.
The model field must come from DescribeAIModels. Pass a value that actually exists in the Models[].Model list of the chosen group. The main managed group only has deepseek-v4-flash enabled by default; to use others, call UpdateAIModel first.
Hunyuan models are strictly bound to 成长计划. To use a hunyuan-* model, the 成长计划 must be enrolled. When not enrolled, guide the user to https://docs.cloudbase.net/ai/ai-inspire-plan, or switch to "cloudbase" + deepseek-v4-flash. Do not bypass the check and call anyway.
Check pricing before enabling more models.DescribeManagedAIModelList returns ModelChargingInfo (Uniform flat price / Tiered tiered pricing) + ModelSpec.ContextLength. Confirm before calling UpdateAIModel. Models is a full replacement — merge the old list + the new entry before passing.
Check base library version. 3.7.1+ is required; on older versions wx.cloud.extend.AI is undefined — do not debug it as a model issue.
Use callbacks for UI updates.onText is well-suited for progressively refreshing chat bubbles; manually concatenating from eventStream tends to drop separators.
Check for [DONE]. When iterating eventStream, stop only when event.data === "[DONE]", otherwise the stream waits forever for the next frame.
Remember the data wrapper.streamText parameters MUST be wrapped in data: { ... } — unlike JS/Node SDK. Forgetting it yields a parameter error.
Distinguish "not-eligible / group-not-ready" from "call failure". The former should guide the user into enrollment / purchase / UpdateAIModel flows; the latter is about debugging prompts, parameters, or the network. The error messages and next actions are completely different.
Do not hardcode third-party model API keys in the Mini Program. For models outside the managed catalog, use CreateAIModel (Secret.ApiKey) so the key is stored on the CloudBase side; keep only the GroupName in the Mini Program.
TypeScript: do NOT use any to silence type errors. If the wx.cloud.extend.AI surface is missing types, declare a precise interface for the slice you actually use, or augment via a local .d.ts. Never : any, as any, @ts-ignore, @ts-nocheck.
Self-verify before claiming done. Build the Mini Program, open it in the WeChat DevTools simulator, exercise the real streamText / generateText flow end-to-end, and confirm: (a) the text chunks arrive via onText, (b) [DONE] terminates the stream, (c) no new console errors. "It should work" without an actual run is not acceptable evidence.
