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-engit 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-en)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw/chat_with_agent-en"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/chat_with_agent-en/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-en"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/chat_with_agent-en.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 | $0.00029 | $0.01219 |
| Opus 5 | $0.00015 | $0.00609 |
| Sonnet 5 | $0.00006 | $0.00244 |
| Haiku 4.5 | $0.00003 | $0.00122 |
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 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:
- chat_with_agent — 94% 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.
Chat with Agent
When to Use
Use this skill when you need to ask another agent a question, seek help, request a plan, request a review, request decision support, or engage in any form of communication. If the user explicitly asks to talk to a specific agent, you should also use this skill.
Should Use
- You need another agent's expertise, judgment, or second opinion
- You need to request a plan, review, or recommendation from an agent
- The user explicitly asks a specific agent to participate, assist, or answer
- You need to continue an existing agent session while preserving context
Should Not Use
- You can complete the task yourself and the user has not explicitly asked to involve another agent
- It is a simple Q&A that does not require a specialized agent
- The target agent is unclear — you should ask for clarification or list available agents first
- You just received a message from an agent and are about to immediately call back the same agent, which may cause a loop
Decision Rules
- If the user explicitly requests a specific agent, follow that request — but still look up the agent first; do not guess the ID
- If you can complete the task yourself, do not call another agent
- When continuing a conversation, you must pass
session_id - By default, prefer using
list_agents()andchat_with_agent(...)for foreground conversations — do not resort to other methods - If the task should run in the background, use
submit_to_agent(...)to submit it, thencheck_agent_task(...)to check the status
Usage Flow
Follow this flow strictly when using this skill:
- Ensure your tool list includes both
list_agents()andchat_with_agent(...)built-in tools
- These two tools are the foundation for communicating with other agents — do not remove or disable them
- If you do not have these tools, inform the user that you need them to talk to other agents, and ask the user to add them
- Use
list_agents()to view the currently available agents, and select one by extracting its 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.
- 12d ago First seen · 123 lines · 29 tokens per session scan A 4919a6606081
chat_with_agent is a skill published in the GitHub repository agentscope-ai/QwenPaw (34,741 stars, last pushed yesterday), licensed Apache-2.0. It adds 29 tokens to every session and 1,219 once invoked, about $0.0001 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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