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 zhinjs/zhin --skill wecomgit clone --depth 1 https://github.com/zhinjs/zhinWrote 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/zhinjs/zhin/wecom)<a href="https://agentmods.dev/skills/zhinjs/zhin/wecom"><img src="https://agentmods.dev/badge/skills/zhinjs/zhin/wecom/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/zhinjs/zhin/wecom"><img src="https://agentmods.dev/badge/skills/zhinjs/zhin/wecom.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00067 | $0.00568 |
| Opus 5.5 | $0.00027 | $0.00227 |
| Sonnet 5.5 | $0.00013 | $0.00114 |
| Haiku 4.5 | $0.00007 | $0.00057 |
Grade A, and why
wecom 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 26d ago.
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.
What it actually says
企业微信技能
企业微信（WeCom）是腾讯面向企业的 IM 平台，具备组织架构（部门树）、应用消息推送等能力。
核心原则
组织架构是核心
企业微信的用户隶属于部门树，查询操作需要从组织架构出发：
wecom_list_departments → 获取部门列表（默认从根部门 ID=1 开始）
wecom_get_dept_users(dept_id) → 获取部门下的用户列表
wecom_get_user(user_id) → 获取用户详情
消息发送
wecom_send_text 通过应用消息接口发送文本消息，需要知道接收者的 userid。
群消息通过 webhook 回调自动处理，不需要主动调用发送工具。
工具分类
组织架构
| 工具 | 用途 | 说明 |
|---|---|---|
wecom_list_departments |
部门列表 | 默认从根部门 ID=1 开始 |
wecom_get_dept_users |
部门用户列表 | 返回简单用户信息 |
wecom_get_user |
用户详情 | 需要通讯录权限 |
消息
| 工具 | 用途 | 说明 |
|---|---|---|
wecom_send_text |
发送文本消息 | 发给指定用户，通过应用消息接口 |
易错点
- 部门查询从根部门 ID=1 开始。不知道部门 ID 时先
list_departments获取整棵部门树。 - 消息发送需要 userid，不是用户名或昵称。仅有昵称时先通过
get_dept_users查找。 - 企业微信不支持机器人撤回消息，一旦发出无法撤回。
- Access Token 有效期 7200 秒，适配器会自动刷新。
What ships with it
4 files 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.
- 26d ago First seen · 69 lines · 67 tokens per session scan A 94243b6c0708
wecom is a skill published in the GitHub repository zhinjs/zhin (136 stars, last pushed today), licensed MIT. It adds 67 tokens to every session and 568 once invoked, about $0.0003 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 2026-09-21.
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