QwenPaw is a personal AI assistant that runs on a local machine or in the cloud and connects to multiple chat applications. It provides memory, file workspaces, multiple agents, skills, plugins, and integrations with language-model providers and external tools. The catalogue entries are skills that extend its capabilities.
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 agentscope-ai/QwenPaw --skill channel_message-zhgit clone --depth 1 https://github.com/agentscope-ai/QwenPawWrote 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/agentscope-ai/qwenpaw/channel_message-zh)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw/channel_message-zh"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/channel_message-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/agentscope-ai/qwenpaw/channel_message-zh"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/channel_message-zh.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00066 | $0.01585 |
| Opus 5 | $0.00033 | $0.00792 |
| Sonnet 5 | $0.00013 | $0.00317 |
| Haiku 4.5 | $0.00007 | $0.00159 |
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 12d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- channel_message — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
频道消息推送
什么时候用
通常只有在用户明确要求向某个 channel / 会话发送消息，或你需要主动推送通知（如任务完成、提醒、告警）时，使用本 skill。
这是单向发送，不会返回回复。
应该使用
- 用户明确要求发往某个 channel / 会话
- 任务完成后主动通知用户
- 定时提醒、告警、状态更新
- 将异步结果推送回某个已有会话
- 用户明确要求"处理完后通知我"
不应使用
- 如果只是当前会话中的正常回复，不要使用
qwenpaw channels send - 需要和用户进行双向对话并立即等待回复
- 还不知道目标 session 是哪个
- 想当然猜测
target-user或target-session
决策规则
- 通常只有在用户明确要求发往某个 channel / 会话，或需要主动通知时才使用
- 发送前必须先查 session
- 不要猜
target-user和target-session - 如果查到多个 session，优先使用最近活跃的
channel send是单向推送，不会返回用户回复
最常用命令
1) 先查询可用 sessions
qwenpaw chats list --agent-id <your_agent> --channel <channel>
也可以按用户筛选：
qwenpaw chats list --agent-id <your_agent> --user-id <user_id>
2) 发送消息
qwenpaw channels send \
--agent-id <your_agent> \
--channel <channel> \
--target-user <user_id> \
--target-session <session_id> \
--text "..."
最小工作流
1. 判断：是否为用户明确要求发送，或是否需要主动通知
2. qwenpaw chats list 查询目标 session
3. 从结果中获取 user_id 和 session_id
4. 若有多个 session，优先选最近活跃的
5. qwenpaw channels send 发送消息
6. 结束（无回复）
关键规则
必填参数
qwenpaw channels send 必须同时提供：
--agent-id--channel--target-user--target-session--text
必须先查询
发送前先执行：
qwenpaw chats list --agent-id <your_agent> --channel <channel>
从结果中获取：
user_id→--target-usersession_id→--target-session
如果有多个候选 session，优先选择 updated_at 最近的会话。
单向推送
qwenpaw channels send 只负责发送，不等待回复。
简短示例
用户明确要求发往某个 channel
qwenpaw chats list --agent-id notify_bot --channel feishu
qwenpaw channels send \
--agent-id notify_bot \
--channel feishu \
--target-user manager_id \
--target-session manager_session \
--text "周报已生成，请查收"
任务完成通知
qwenpaw chats list --agent-id task_bot --channel console
qwenpaw channels send \
--agent-id task_bot \
--channel console \
--target-user alice \
--target-session alice_console_001 \
--text "✅ 任务已完成"
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.
- 12d ago First seen · 251 lines · 66 tokens per session scan A 2888b0f1873d
channel_message is a skill published in the GitHub repository agentscope-ai/QwenPaw (34,809 stars, last pushed today), licensed Apache-2.0. It adds 66 tokens to every session and 1,585 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 2026-08-30.
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