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 chat_with_agent-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/chat_with_agent-zh)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw/chat_with_agent-zh"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/chat_with_agent-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/chat_with_agent-zh"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/chat_with_agent-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 | $0.00030 | $0.01284 |
| Opus 5 | $0.00015 | $0.00642 |
| Sonnet 5 | $0.00006 | $0.00257 |
| Haiku 4.5 | $0.00003 | $0.00128 |
Grade A, and why
chat_with_agent 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 13d 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:
- chat_with_agent — 100% identical, 7 lines differ
How it starts
The opening of the file, as written. The whole thing — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
与 Agent 对话
什么时候用
当你需要向另一个 agent 询问问题、寻求帮助、请求方案、请求复核、请求决策支持,或者进行任何形式的交流时,使用本 skill。
如果用户明确要求与某个 agent 对话,也应使用本 skill。
应该使用
- 需要另一个 agent 的专长、判断或第二意见
- 需要向某个 agent 请求方案、复核或建议
- 用户明确要求某个 agent 参与、协助或回答
- 需要继续某个已有的 agent 会话,并保留上下文
不应使用
- 你自己可以直接完成,且用户没有明确要求调用其他 agent
- 只是普通问答,不需要专门 agent
- 目标 agent 不明确,应该先追问或先查可用 agents
- 刚收到某个 agent 的消息,又立刻回调同一个 agent,可能造成循环
决策规则
- 如果用户明确要求某个 agent,优先按要求执行,但仍要先查 agent,不要猜 ID
- 如果你自己能完成,就不要调用其他 agent
- 需要保留上下文续聊时,必须传
session_id - 默认优先使用
list_agents()和chat_with_agent(...)进行前台对话,不要绕到别的方式 - 如果任务需要后台执行,使用
submit_to_agent(...)提交,再用check_agent_task(...)查询状态
使用流程
请严格按照以下流程顺序使用本 skill:
- 确保你的工具列表中包含
list_agents()和chat_with_agent(...)两个内建工具
- 这两个工具是与其他 agent 对话的基础,不要删除或禁用它们
- 如果你没有这两个工具,请告诉用户你需要它们来与其他 agent 对话,并请求用户添加
- 使用
list_agents()工具查看当前可用的 Agent,并从中选择一个 agent 提取其 id
- 请根据用户的需求和对应 Agent 的描述来选择最合适的 Agent
- 如果找不到合适的 Agent 且你不是 Default Agent,就使用 Default Agent
- 否则告诉用户没有合适的 Agent 可用,并建议他们创建一个新的 Agent 或调整现有 Agent 的描述以便更好地匹配需求
- 调用
chat_with_agent(...)发起前台求助,其中需要传递的关键参数包括
to_agent: 对话目标 Agent 的 ID,注意是 ID 不是名字text: 你要对目标 Agent 说的内容session_id: (可选)如果你需要与同一个 Agent 进行多轮对话,从第二轮开始传递相同的session_id来保持上下文连续timeout: (可选)预估任务需要的前台等待时间,避免过早超时
- 如果任务适合后台执行,请使用新的后台工具路径
submit_to_agent(...):提交后台任务,需要to_agent、text,以及可选的session_id/task_timeout- 省略
task_timeout时,默认执行预算为 3600 秒(1 小时) - 预计超过 1 小时的任务,请显式传入正数秒
- 也接受数字字符串,例如
"7200"
- 省略
check_agent_task(...):通过task_id查询任务状态,完成时返回最终结果- 超时后任务会以失败结束,错误类似
Task timed out after Ns(API 载荷中带code=timeout)
- 超时后任务会以失败结束,错误类似
最小调用示例
新对话
list_agents()
chat_with_agent(
to_agent="<target_agent_id>",
text="[Agent <your_agent_id> requesting] 我需要你帮我判断这个问题的处理方向。",
)
续接已有对话
chat_with_agent(
to_agent="<target_agent_id>",
text="[Agent <your_agent_id> requesting] 请基于刚才的结论继续展开第 2 点。",
session_id="<previous_session_id>",
)
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.
- 13d ago First seen · 123 lines · 30 tokens per session scan A 021f1f24b240
chat_with_agent is a skill published in the GitHub repository agentscope-ai/QwenPaw (34,809 stars, last pushed yesterday), licensed Apache-2.0. It adds 30 tokens to every session and 1,284 once invoked, about $0.0002 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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