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 multi_agent_collaboration-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/multi_agent_collaboration-en)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw/multi_agent_collaboration-en"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/multi_agent_collaboration-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/multi_agent_collaboration-en"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/multi_agent_collaboration-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 | $0.00047 | $0.03246 |
| Opus 5 | $0.00023 | $0.01623 |
| Sonnet 5 | $0.00009 | $0.00649 |
| Haiku 4.5 | $0.00005 | $0.00325 |
Grade A, and why
multi_agent_collaboration 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:
- multi_agent_collaboration — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 477 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Multi-Agent Collaboration
When to Use
Use this skill when you need another agent's expertise, context, workspace content, or collaborative support. If the user explicitly asks a specific agent to participate/assist/answer, you should also use this skill.
Should Use
- The current task is clearly better suited for a specialized agent
- You need another agent's workspace / files / context
- You need a second opinion or expert review
- The user explicitly asks for a specific agent to participate or to invoke another agent
Should Not Use
- You can complete the task on your own and the user has not explicitly asked to invoke another agent
- It is just a normal Q&A that does not require a specialized agent
- Information is insufficient -- you should ask the user for clarification first
- You just received a message from Agent B -- do not call Agent B again to avoid loops
Decision Rules
- If the user explicitly requests invoking another agent, prioritize following the request
- Otherwise, if you can do it yourself, do not invoke another agent
- Check agents before invoking -- do not guess IDs
- When context continuation is needed, you must pass
--session-id - Do not call back the source agent
Most Common Commands
1) First Query Available Agents
qwenpaw agents list
2) Start a New Conversation (Real-time Mode)
qwenpaw agents chat \
--from-agent <your_agent> \
--to-agent <target_agent> \
--text "[Agent <your_agent> requesting] ..."
3) Submit a Complex Task (Background Mode)
Complex tasks include: data analysis, report generation, batch processing, external API calls, etc.
qwenpaw agents chat --background \
--from-agent <your_agent> \
--to-agent <target_agent> \
--text "[Agent <your_agent> requesting] ..."
Output:
[TASK_ID: xxx-xxx-xxx]
[SESSION: ...]
4) Query Background Task Status
qwenpaw agents chat --background --task-id <task_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 · 477 lines · 47 tokens per session scan A fb0a2a2e5ed5
multi_agent_collaboration is a skill published in the GitHub repository agentscope-ai/QwenPaw (34,809 stars, last pushed yesterday), licensed Apache-2.0. It adds 47 tokens to every session and 3,246 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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