AI Model Wechat
majiayu000/claude-skill-registry
A skill your agent uses when developing WeChat Mini Programs (小程序, 企业微信小程序, wx.cloud-based apps) that need AI capabilities.
A skill your agent uses for WeChat Mini Program AI via wx.cloud.extend.AI (小程序, wx.cloud apps).
$ npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill ai-model-wechat -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install TencentCloudBase/CloudBase-AI-Toolkit ai-model-wechat --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/TencentCloudBase/CloudBase-AI-Toolkit.git skills-src && mkdir -p .claude/skills && cp -r skills-src/config/source/skills/ai-model-wechat .claude/skills/ai-model-wechat && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "ai-model-wechat" agent skill from https://github.com/TencentCloudBase/CloudBase-AI-Toolkit/tree/main/config/source/skills/ai-model-wechat into .claude/skills/ai-model-wechat/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-model-wechat", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/TencentCloudBase/CloudBase-AI-Toolkit/tree/main/config/source/skills/ai-model-wechatType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill ai-model-wechat -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install TencentCloudBase/CloudBase-AI-Toolkit ai-model-wechat --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TencentCloudBase/CloudBase-AI-Toolkit.git skills-src && mkdir -p .agents/skills && cp -r skills-src/config/source/skills/ai-model-wechat .agents/skills/ai-model-wechat && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "ai-model-wechat" agent skill from https://github.com/TencentCloudBase/CloudBase-AI-Toolkit/tree/main/config/source/skills/ai-model-wechat into .agents/skills/ai-model-wechat/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-model-wechat", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill ai-model-wechat -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install TencentCloudBase/CloudBase-AI-Toolkit ai-model-wechat --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TencentCloudBase/CloudBase-AI-Toolkit.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/config/source/skills/ai-model-wechat .cursor/skills/ai-model-wechat && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "ai-model-wechat" agent skill from https://github.com/TencentCloudBase/CloudBase-AI-Toolkit/tree/main/config/source/skills/ai-model-wechat into .cursor/skills/ai-model-wechat/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-model-wechat", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/TencentCloudBase/CloudBase-AI-Toolkit.git --path config/source/skills/ai-model-wechat--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill ai-model-wechat -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install TencentCloudBase/CloudBase-AI-Toolkit ai-model-wechat --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TencentCloudBase/CloudBase-AI-Toolkit.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/config/source/skills/ai-model-wechat .gemini/skills/ai-model-wechat && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "ai-model-wechat" agent skill from https://github.com/TencentCloudBase/CloudBase-AI-Toolkit/tree/main/config/source/skills/ai-model-wechat into .gemini/skills/ai-model-wechat/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-model-wechat", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install TencentCloudBase/CloudBase-AI-Toolkit ai-model-wechatInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill ai-model-wechat -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/TencentCloudBase/CloudBase-AI-Toolkit.git skills-src && mkdir -p .github/skills && cp -r skills-src/config/source/skills/ai-model-wechat .github/skills/ai-model-wechat && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "ai-model-wechat" agent skill from https://github.com/TencentCloudBase/CloudBase-AI-Toolkit/tree/main/config/source/skills/ai-model-wechat into .github/skills/ai-model-wechat/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-model-wechat", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill ai-model-wechat -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install TencentCloudBase/CloudBase-AI-Toolkit ai-model-wechat --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/TencentCloudBase/CloudBase-AI-Toolkit.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/config/source/skills/ai-model-wechat .opencode/skills/ai-model-wechat && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "ai-model-wechat" agent skill from https://github.com/TencentCloudBase/CloudBase-AI-Toolkit/tree/main/config/source/skills/ai-model-wechat into .opencode/skills/ai-model-wechat/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "ai-model-wechat", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
ai-model-wechatA skill your agent uses for WeChat Mini Program AI via wx.cloud.extend.AI (小程序, wx.cloud apps).
AI Model Wechat is an agent skill from TencentCloudBase/CloudBase-AI-Toolkit. Use this skill for WeChat Mini Program AI via wx.cloud.extend.AI (小程序, wx.cloud apps). Covers generateText and streamText with callbacks (onText, onEvent, onFinish); streamText needs a data wrapper, generateText returns the raw response. Models via wx.cloud.extend.AI.createModel with groups hunyuan-exp (小程序成长计划), cloudbase (main managed), or custom-; model id goes in the data wrapper model field. MUST run two-step preflight before code — see body. NOT for browser/Web (use ai-model-web), Node.js backend (use…
Its SKILL.md is about 6.4k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Media & Creative, covering Messaging and chat bots and Image generation. It works with WeChat and Node.js. The repository describes itself as: Backend for AI coding agents on CloudBase — database, auth, functions via Plugin, Skills & MCP. The licence is MIT.
4 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit 21af91c. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are typescript and javascript).
From the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
docs.cloudbase.netbuy.cloud.tencent.comtcb.cloud.tencent.comFrom URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
AI Model Wechat loads about 6.4k tokens when it runs. Until then it costs about 148 tokens; SKILL.md has 2,345 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from TencentCloudBase/CloudBase-AI-Toolkit at commit 21af91c, republished under its MIT licence (© TencentCloudBase). 2,345 words, ~6,412 tokens.
.claude/skills/ai-model-wechat/SKILL.md (or your agent's skills folder).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 in WeChat Mini Program using wx.cloud.extend.AI.
Use it when you need to:
Do NOT use for:
ai-model-web skillai-model-nodejs skillai-model-nodejs skill (not available in Mini Program)http-api-cloudbase skill (it now includes the ai_model OpenAPI spec for direct HTTP calls)wx.cloud.extend.AI.createModel(provider) 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 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). |
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// Growth Plan branch
const model = wx.cloud.extend.AI.createModel("hunyuan-exp"); // ← provider / GroupName
await model.streamText({
data: { model: "hunyuan-2.0-instruct-20251111", messages: [...] } // ← concrete model id
});
// Token Credits branch
const model = wx.cloud.extend.AI.createModel("cloudbase");
await model.streamText({
data: { model: "deepseek-v4-flash", messages: [...] }
});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).model field inside data: { model: "deepseek-v3.2" }, { model: "hunyuan-2.0-instruct-20251111" }, { model: "kimi-k2.6" }, …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.
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.
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 queryEnv 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 |
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.
"cloudbase" main managed group):callCloudApi({
service: "tcb",
action: "DescribeEnvPostpayPackage",
params: {
EnvId: "<current envId>",
},
})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):
https://buy.cloud.tencent.com/lowcode?buyType=resPack&envId={envId}&resourceType=tokenPassing 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.
tcb Action DescribeAIModels, Version 2018-06-08):callCloudApi({
service: "tcb",
action: "DescribeAIModels",
params: { EnvId: "<envId>" },
})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[].
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=off
Models: [
{ 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.
},
})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:
tcbpublic-service Actions officially use PascalCase (EnvId,GroupName,ActivityNames); some docs show camelCase. On the first call, if you hitInvalidParameter, switch casing and retry, then freeze the working form in your project's wrapper.
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.
| 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 |
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 theGroupNamevalues returned byDescribeAIModels. New projects always use the unified"cloudbase"managed group and select the concrete vendor model via themodelfield.
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.
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:
https://tcb.cloud.tencent.com/dev?envId={envId}#/aiReplace {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=off
Secret: {
// Key / ApiKey: pick one; OpenAI-compatible endpoints usually use ApiKey
ApiKey: "<vendor-api-key>",
},
},
})After registration:
DescribeAIModels to confirm the GroupName exists with Status=1 and the target Model appears in Models[].wx.cloud.extend.AI.createModel("custom-openai-compat") and pass a registered model name (e.g. "gpt-4o-mini") as the model field.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).// app.js
App({
onLaunch: function() {
wx.cloud.init({ env: "<YOUR_ENV_ID>" });
}
})⚠️ 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 viaUpdateAIModelif missing). The example below assumes the current environment is enrolled in 小程序成长计划 and usescreateModel("hunyuan-exp")+hunyuan-2.0-instruct-20251111. If the eligibility branch landed on the resource pack, swap the provider to"cloudbase"and set themodelto whatever the user chose and you have just enabled viaUpdateAIModel— never assumedeepseek-v4-flashis already on.
const model = wx.cloud.extend.AI.createModel("hunyuan-exp");
const res = await model.generateText({
model: "hunyuan-2.0-instruct-20251111", // plan-enrolled default
messages: [{ role: "user", content: "hi" }],
});
// ⚠️ Return value is the RAW model response, NOT wrapped like JS/Node SDK
console.log(res.choices[0].message.content); // access via choices array
console.log(res.usage); // token usage⚠️ 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")tocreateModel("cloudbase")and themodelto whatever the user chose and you have just enabled viaUpdateAIModel(no model is enabled by default).
const model = wx.cloud.extend.AI.createModel("hunyuan-exp");
// ⚠️ Parameters MUST be wrapped in a `data` object
const res = await model.streamText({
data: { // ⚠️ Required wrapper
model: "hunyuan-2.0-instruct-20251111", // plan-enrolled default
messages: [{ role: "user", content: "hi" }]
},
onText: (text) => { // Optional: incremental text callback
console.log("New text:", text);
},
onEvent: ({ data }) => { // Optional: raw event callback
console.log("Event:", data);
},
onFinish: (fullText) => { // Optional: completion callback
console.log("Done:", fullText);
}
});
// Async iteration is also available
for await (let str of res.textStream) {
console.log(str);
}
// Check for completion via eventStream
for await (let event of res.eventStream) {
console.log(event);
if (event.data === "[DONE]") { // ⚠️ Check for [DONE] to stop
break;
}
}Prerequisite: the "Mandatory Two-Step Preflight" has been completed. For the resource pack branch, use
"cloudbase"+ the specific text model you just verified/enabled viaDescribeAIModels/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);
}| Feature | JS/Node SDK | WeChat Mini Program |
|---|---|---|
| Namespace | app.ai() | wx.cloud.extend.AI |
| generateText params | Direct object | Direct object |
| generateText return | { text, usage, messages } | Raw: { choices, usage } |
| streamText params | Direct object | ⚠️ Wrapped in data: {...} |
| streamText return | { textStream, dataStream } | { textStream, eventStream } |
| Callbacks | Not supported | onText, onEvent, onFinish |
| Image generation | Node SDK only | Not available |
interface WxStreamTextInput {
data: { // ⚠️ Required wrapper object
model: string;
messages: Array<{
role: "user" | "system" | "assistant";
content: string;
}>;
};
onText?: (text: string) => void; // incremental text callback
onEvent?: (prop: { data: string }) => void; // raw event callback
onFinish?: (text: string) => void; // completion callback
}interface WxStreamTextResult {
textStream: AsyncIterable<string>; // incremental text stream
eventStream: AsyncIterable<{ // raw event stream
event?: unknown;
id?: unknown;
data: string; // "[DONE]" when complete
}>;
}// Raw model response (OpenAI-compatible format)
interface WxGenerateTextResponse {
id: string;
object: "chat.completion";
created: number;
model: string;
choices: Array<{
index: number;
message: {
role: "assistant";
content: string;
};
finish_reason: string;
}>;
usage: {
prompt_tokens: number;
completion_tokens: number;
total_tokens: number;
};
}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.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-* 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.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.wx.cloud.extend.AI is undefined — do not debug it as a model issue.onText is well-suited for progressively refreshing chat bubbles; manually concatenating from eventStream tends to drop separators.[DONE]. When iterating eventStream, stop only when event.data === "[DONE]", otherwise the stream waits forever for the next frame.data wrapper. streamText parameters MUST be wrapped in data: { ... } — unlike JS/Node SDK. Forgetting it yields a parameter error.UpdateAIModel flows; the latter is about debugging prompts, parameters, or the network. The error messages and next actions are completely different.CreateAIModel (Secret.ApiKey) so the key is stored on the CloudBase side; keep only the GroupName in the Mini Program.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.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.© TencentCloudBase, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in config/source/skills/ai-model-wechat of TencentCloudBase/CloudBase-AI-Toolkit.
Open the folder on GitHubat commit 21af91c
We found 7 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in TencentCloudBase/CloudBase-AI-Toolkit, which our catalogue first saw on October 7, 2026.
AI Model Wechat next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| AI Model Wechat this skillTencentCloudBase/CloudBase-AI-Toolkit | 1.1k | 2 repos | ~6.4k | Automated safety check: Pass | MIT | |
| AI Model Wechatmajiayu000/claude-skill-registry | 666 | 2 repos | ~1.4k | Automated safety check: Pass | MIT | |
| AI Model Webmajiayu000/claude-skill-registry | 666 | 2 repos | ~1.2k | Automated safety check: Pass | MIT | |
| AI Model Nodejsmajiayu000/claude-skill-registry | 666 | 2 repos | ~1.7k | Automated safety check: Pass | MIT | |
| Md2wechatgeekjourneyx/md2wechat-skill | 3.7k | — | ~3.8k | Automated safety check: Pass | Custom licence | |
| Lingzaoatian-create/lingzao-skill | 295 | 1 repos | ~8.8k | Automated safety check: Pass | MIT |
majiayu000/claude-skill-registry
A skill your agent uses when developing WeChat Mini Programs (小程序, 企业微信小程序, wx.cloud-based apps) that need AI capabilities.
majiayu000/claude-skill-registry
A skill your agent uses when developing browser/Web applications (React/Vue/Angular, static websites, SPAs) that need AI capabilities.
majiayu000/claude-skill-registry
A skill your agent uses when developing Node.js backend services or CloudBase cloud functions (Express/Koa/NestJS, serverless, backend APIs) that need AI capabilities.
geekjourneyx/md2wechat-skill
Convert Markdown to WeChat Official Account HTML. An agent skill from geekjourneyx/md2wechat-skill.
atian-create/lingzao-skill
Use Lingzao creator-content tools for Xiaohongshu/XHS, Douyin, and WeChat official-account public content.
dracohu2025-cloud/draco-skills-collection
从飞书文档或本地 Markdown 提炼文章主题与风格,调用 OpenRouter 的 Nano Banana / Gemini Flash Image 生成 2.35:1 的微信公众号封面图,并可选上传为微信封面素材。
TencentCloudBase/CloudBase-AI-Toolkit
A skill your agent uses for Node.js backend AI via @cloudbase/node-sdk (=3.16.0) — cloud functions, CloudRun, Express/Koa/NestJS, serverless APIs, scheduled jobs, LLM proxies, agent orchestration.
TencentCloudBase/CloudBase-AI-Toolkit
CloudBase official HTTP API client guide. An agent skill from TencentCloudBase/CloudBase-AI-Toolkit.
TencentCloudBase/CloudBase-AI-Toolkit
Author or revise a cloud-api-operations recipe (config/source/skills/cloud-api-operations/references/recipes/).
TencentCloudBase/CloudBase-AI-Toolkit
Analyze, standardize, validate, and sync locally maintained skills into agent skill directories with a skills CLI-aligned workflow.
TencentCloudBase/CloudBase-AI-Toolkit
Build production-ready AI agent backends using the CloudBase Agent Python SDK — create agents with LangGraph/CrewAI/LlamaIndex, serve them via FastAPI with AG-UI protocol streaming +…
TencentCloudBase/CloudBase-AI-Toolkit
A skill your agent uses 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…
A skill your agent uses for WeChat Mini Program AI via wx.cloud.extend.AI (小程序, wx.cloud apps). AI Model Wechat is an agent skill from TencentCloudBase/CloudBase-AI-Toolkit.cloud apps).
AI Model Wechat fits situations like: weChat Mini Program AI via wx.cloud.extend.AI (小程序; tasks that involve Messaging and chat bots; tasks that involve Image generation.
Run `npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill ai-model-wechat -a claude-code`. Or copy the skill folder (config/source/skills/ai-model-wechat in TencentCloudBase/CloudBase-AI-Toolkit) into .claude/skills/ai-model-wechat in your project. Claude Code loads it when a task matches its description.
Run `npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill ai-model-wechat -a codex`. Or copy the skill folder (config/source/skills/ai-model-wechat in TencentCloudBase/CloudBase-AI-Toolkit) into .agents/skills/ai-model-wechat in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill ai-model-wechat -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ai-model-wechat, .gemini/skills/ai-model-wechat, .github/skills/ai-model-wechat and .opencode/skills/ai-model-wechat in your project.
SKILL.md names no scripts, command-line tools or credentials: AI Model Wechat is instructions for the agent only. Our summary lists: Node.js.
SKILL.md names 3 domains. In commands or code: docs.cloudbase.net, buy.cloud.tencent.com and tcb.cloud.tencent.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
AI Model Wechat is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 6.4k tokens (SKILL.md is roughly 26k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with AI Model Wechat: AI Model Wechat (majiayu000/claude-skill-registry, 666 stars), AI Model Web (majiayu000/claude-skill-registry, 666 stars), AI Model Nodejs (majiayu000/claude-skill-registry, 666 stars) and Md2wechat (geekjourneyx/md2wechat-skill, 3.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
TencentCloudBase (a GitHub organization) maintains it in TencentCloudBase/CloudBase-AI-Toolkit, which has 1,133 GitHub stars. The repository holds 49 skills in this directory. The repository was last updated on October 7, 2026.
Source: TencentCloudBase/CloudBase-AI-Toolkit on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.