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 AgiWish/hermes-skills-zh --skill wechat-reply-zhgit clone --depth 1 https://github.com/AgiWish/hermes-skills-zhWrote 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/agiwish/hermes-skills-zh/wechat-reply-zh)<a href="https://agentmods.dev/skills/agiwish/hermes-skills-zh/wechat-reply-zh"><img src="https://agentmods.dev/badge/skills/agiwish/hermes-skills-zh/wechat-reply-zh/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/agiwish/hermes-skills-zh/wechat-reply-zh"><img src="https://agentmods.dev/badge/skills/agiwish/hermes-skills-zh/wechat-reply-zh.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 | $0.00051 | $0.00834 |
| Opus 5 | $0.00026 | $0.00417 |
| Sonnet 5 | $0.00010 | $0.00167 |
| Haiku 4.5 | $0.00005 | $0.00083 |
Grade A, and why
wechat-reply-zh 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 11d 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
微信职场回复 (wechat-reply-zh)
When to Use
当用户说以下任意内容时激活：
- "帮我回这条微信"、"怎么回复这个"
- 粘贴了一段微信消息并询问如何回复
- "老板发来消息说…"、"客户问我…"
/wechat-reply-zh [消息内容]
Quick Reference
/wechat-reply-zh [收到的消息]
可选参数：
--from=上级 # 对方是你的上级（默认：根据内容判断）
--from=同级 # 对方是同事
--from=下级 # 对方是你的下属
--from=客户 # 对方是外部客户/甲方
--tone=正式 # 正式语气
--tone=友好 # 友好但专业（默认）
--tone=简短 # 简短确认型
Procedure
-
识别对方身份
- 从消息内容和用户描述判断发送方身份
- 不确定时，默认按「上级」处理（偏谨慎）
- 如用户明确说明了身份，按说明处理
-
判断消息类型 识别消息属于哪类：
- 任务指派：布置工作、要求完成某事
- 催进度：询问某项工作进展
- 问题咨询：寻求建议或信息
- 通知告知：单向传达信息
- 敏感/棘手：需要拒绝、延期、解释失误
-
生成回复
上级-任务指派：
好的，[重复核心任务确认理解]，我会在[时间]前完成并同步进展。上级-催进度：
[任务名]目前[当前状态]，预计[时间]完成。[如有风险，一句话说明]客户-问题咨询：
您好，关于[问题]，[简洁回答]。如需进一步了解，欢迎随时告知。棘手场景（需要拒绝/延期）：
[表示理解对方诉求] + [说明客观原因，不推卸] + [给出替代方案或新时间节点] -
输出格式
- 微信消息不超过 100 字为宜
- 不用 Markdown 格式
- 必要时提供 2-3 个不同语气版本供选择
Pitfalls
- 不要替用户承诺具体时间，除非用户提供了时间信息
- 棘手消息不要给出过于强硬或过于软弱的回复
- 回复客户时避免暴露内部问题细节
- 对上级消息，不要过度解释或辩解
Verification
- 回复长度是否适合微信场景（不超过 150 字）
- 语气是否与对方身份匹配
- 是否避免了承诺用户未确认的时间/结果
- 棘手场景是否包含了替代方案
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.
- 11d ago First seen · 93 lines · 51 tokens per session scan A 233860f15c5a
wechat-reply-zh is a skill published in the GitHub repository AgiWish/hermes-skills-zh (5 stars, last pushed 3mo ago), licensed MIT. It adds 51 tokens to every session and 834 once invoked, about $0.0003 per session on Opus 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 2026-08-31.
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