CloudBase AI Toolkit is an integration layer that lets AI coding tools use Tencent CloudBase as an application backend, including databases, authentication, functions, storage, and deployments. Developers use it to have coding agents build and operate CloudBase-backed applications through skills, plugins, commands, and chat-accessible tools. The catalogue entries are the toolkit’s own skills, agents, hooks, plugins, rules, instructions, and MCP tools.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add TencentCloudBase/CloudBase-AI-Toolkit --skill ai-model-wechatgit clone --depth 1 https://github.com/TencentCloudBase/CloudBase-AI-ToolkitWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/tencentcloudbase/cloudbase-ai-toolkit/ai-model-wechat)<a href="https://agentmods.dev/skills/tencentcloudbase/cloudbase-ai-toolkit/ai-model-wechat"><img src="https://agentmods.dev/badge/skills/tencentcloudbase/cloudbase-ai-toolkit/ai-model-wechat/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/tencentcloudbase/cloudbase-ai-toolkit/ai-model-wechat"><img src="https://agentmods.dev/badge/skills/tencentcloudbase/cloudbase-ai-toolkit/ai-model-wechat.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 231 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00157 | $0.06926 |
| Opus 5.5 | $0.00063 | $0.02770 |
| Sonnet 5.5 | $0.00031 | $0.01385 |
| Haiku 4.5 | $0.00016 | $0.00693 |
Grade A, and why
ai-model-wechat scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
3 near-identical copies found in the catalogue:
- ai-model-wechat — 100% identical, 0 lines differ
- ai-model-wechat — 97% identical, 64 lines differ
- ai-model-wechat — 95% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 452 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sibling skills (local only)
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.
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-webskill - Node.js backend or cloud functions → use
ai-model-nodejsskill - Image generation → use
ai-model-nodejsskill (not available in Mini Program) - Runtimes without a CloudBase SDK (native apps, Python, etc.) → use
http-api-cloudbaseskill (it now includes theai_modelOpenAPI 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 four legal shapes. Anything else is a bug.
✅ Legal createModel(provider) argument |
When to use it |
|---|---|
"cloudbase" |
Default, including 小程序成长计划. Main managed group (TokenHub-backed, multi-vendor pool). Growth Plan quota draws on cloudbase + hy3 — enable hy3 first. Vendor + concrete model go into the model field, e.g. { model: "deepseek-v4-flash" }. |
"hunyuan-v3" |
Legacy plan group, being retired. Only hy3 is available and no new models are added. Existing plan-enrolled code keeps working; new code uses cloudbase + hy3. |
"hunyuan-exp" |
Older legacy builtin group — only if DescribeAIModels still returns it. Its default hunyuan-2.0-instruct-20251111 went offline on 2026-05-31; never target that id. |
"custom-<your-name>" |
A user-defined GroupName you onboarded via CreateAIModel. Must start with custom- (e.g. custom-kimi, custom-openai-compat). |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed 7132e126a8ce
- 6d ago Changed f80d86fc4c69
- 7d ago Changed · +3 lines · +13 tokens per session a704d3519d23
- 13d ago Changed df33e88ece44
- 15d ago Changed d6d7b740045e
- 19d ago Changed · +2 lines 5c6820ce7271
- 21d ago Changed 4ef6f25402fb
- 25d ago Changed · +1 lines ad16ae0f66be
- 1mo ago Changed 4cd87f4b496a
- 1mo ago First seen · 446 lines · 144 tokens per session scan A e881ed48be31
ai-model-wechat is a skill published in the GitHub repository TencentCloudBase/CloudBase-AI-Toolkit (1,156 stars, last pushed today), licensed MIT. It adds 157 tokens to every session and 6,926 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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