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 agentmods add skills/tencentcloudbase/cloudbase-skills/ai-model-wechatnpx skills add TencentCloudBase/cloudbase-skills --skill ai-model-wechatgit clone --depth 1 https://github.com/TencentCloudBase/cloudbase-skillsWrote 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-skills/ai-model-wechat)<a href="https://agentmods.dev/skills/tencentcloudbase/cloudbase-skills/ai-model-wechat"><img src="https://agentmods.dev/badge/skills/tencentcloudbase/cloudbase-skills/ai-model-wechat.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00144 | $0.06629 |
| Opus 5 | $0.00072 | $0.03315 |
| Sonnet 5 | $0.00029 | $0.01326 |
| Haiku 4.5 | $0.00014 | $0.00663 |
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.
This is a copy
100% identical to ai-model-wechat — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 446 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 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
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 · -110 tokens per session 87bc5e4b34f4
- 2d ago Changed a444bcf0787f
- 5d ago First seen · 446 lines · 254 tokens per session scan A 5b240974fdaa
ai-model-wechat is a skill published in the GitHub repository TencentCloudBase/cloudbase-skills (30 stars, last pushed today), licensed MIT. It adds 144 tokens to every session and 6,629 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to ai-model-wechat, differing in 0 lines, and is treated as a copy.
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