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/mateaix/mateclaw/channel_messagenpx skills add mateaix/mateclaw --skill channel_messagegit clone --depth 1 https://github.com/mateaix/mateclawWrote 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/mateaix/mateclaw/channel_message)<a href="https://agentmods.dev/skills/mateaix/mateclaw/channel_message"><img src="https://agentmods.dev/badge/skills/mateaix/mateclaw/channel_message.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 | $0.00078 | $0.00837 |
| Opus 5 | $0.00039 | $0.00418 |
| Sonnet 5 | $0.00016 | $0.00167 |
| Haiku 4.5 | $0.00008 | $0.00084 |
Grade A, and why
channel_message 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 3d 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
渠道消息推送
何时使用
仅在以下情况使用，这是单向推送，不会收到回复：
应该使用
- 用户明确要求"向某个渠道 / 会话发送消息"
- 异步任务完成后主动通知用户
- 定时提醒、告警、状态更新
- 将后台任务结果推送回指定会话
不应使用
- 当前对话中的正常回复（直接回复即可，不要重复推送）
- 需要等待用户回复的双向交互
- 目标渠道或会话不明确时（先询问用户）
支持渠道
wecom（企业微信）、dingtalk、feishu、telegram、discord、qq、slack、weixin
注意：只有机器人收到过消息的会话才能主动推送——平台的推送句柄是在收到入站消息时记录的。如果目标会话不在列表里，需要先让对方在该会话中给机器人发一条消息。
工作流程
第一步：查询目标会话
list_channel_sessions(channelType="wecom")
channelType可选，不传则列出当前工作区所有可推送会话- 返回每个会话的
conversation_id、渠道名称、用户名、最后活跃时间 - 有多个候选会话时，优先选最后活跃时间最近的
第二步：发送消息
send_channel_message(
conversationId="wecom:xxxx",
message="✅ 数据分析已完成，结果已保存到 report.xlsx"
)
conversationId必须来自list_channel_sessions的返回结果，不要凭空猜测message为消息正文（纯文本 / Markdown，取决于渠道能力），超过 4096 字符会被截断
常见场景示例
温度告警推送到企业微信
list_channel_sessions(channelType="wecom")
# 从结果中选目标会话，例如 conversation_id 为 wecom:DeBaDe 的会话，然后：
send_channel_message(
conversationId="wecom:DeBaDe",
message="【温度告警】中控测试会议室 当前温度 29.3℃，已超过 28℃，请及时处理"
)
任务完成通知到钉钉
list_channel_sessions(channelType="dingtalk")
send_channel_message(
conversationId="dingtalk:sw:xxxx",
message="✅ 周报生成完成，已写入知识库"
)
常见错误
- 没有先查会话就发送：
conversationId必须先通过list_channel_sessions获取 - 把正常对话回复当成推送：当前会话直接回复不需要用本技能
- 期望收到回复：
send_channel_message是单向推送，不返回用户回复 - 目标会话不存在：说明机器人从未在该会话收到过消息，请先让用户在目标会话里给机器人发一条消息
- 渠道未启用 / 不支持主动推送：按报错提示先在渠道管理中启用对应渠道
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.
- 3d ago First seen · 88 lines · 78 tokens per session scan A a0dd092ead5f
channel_message is a skill published in the GitHub repository mateaix/mateclaw (1,061 stars, last pushed 3d ago), licensed Apache-2.0. It adds 78 tokens to every session and 837 once invoked, about $0.0004 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 2026-08-30.
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