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 make_plan-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/make_plan-zh)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw/make_plan-zh"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/make_plan-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/make_plan-zh"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/make_plan-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 | $0.00039 | $0.01467 |
| Opus 5 | $0.00019 | $0.00733 |
| Sonnet 5 | $0.00008 | $0.00293 |
| Haiku 4.5 | $0.00004 | $0.00147 |
Grade A, and why
make_plan 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
制定计划
当你需要向更强 Agent 发起一次外部计划请求时,使用本 Skill。
本 Skill 的目标不是把任务外包出去,而是:
- 向更强 Agent 要一份计划
- 这份计划必须是明确的执行步骤
- 由你自己按计划执行
- 不要求被求助的 Agent 直接动手执行任务
调用步骤:
- 使用
list_agents()查可用 Agent 的 ID
- 可根据返回的描述信息选择最合适的 Agent 来求助
- 如果找不到非常合适的 Agent,就选择 default
- 注意区分 Agent 的 ID 和 Name,后续调用需要的是 ID
- 使用
chat_with_agent(...)向更强 Agent 请求“制定计划”
to_agent传目标 Agent IDtext中明确给出提示仅需给出可按步骤执行的计划,不要执行
- 收到回复后,记录对话的
session_id,并提炼出真正可执行的步骤,并由你自己执行
- 如果有不清楚的地方可以继续追问细化计划,但仍然要强调只补计划,不要代执行
- 追问仍使用
chat_with_agent(...),并传入记录的session_id以保持上下文
推荐调用骨架:
list_agents()
chat_with_agent(
to_agent="stronger_agent",
text="[Agent <auto> requesting] 请帮我为下面任务制定执行计划。你不需要执行任务,只需要输出明确、可落地、按顺序排列的步骤。",
)
适用场景
以下场景适合直接发起外部计划请求:
- 任务需要多步拆解
- 步骤之间有依赖关系
- 需要明确先后顺序、检查点或验证点
- 涉及多个模块、文件、系统或角色
- 用户明确要求先给出计划
- 你希望先拿到一份更完整、更稳妥的执行路径
不要这样用
以下情况不要用本 Skill:
- 你其实是想让对方直接替你做任务
- 真正缺的是一个小事实,而不是计划
- 真正需要的是架构判断或方案比较
- 你连任务目标都没理解清楚
向更强 Agent 要什么
调用 chat_with_agent(...) 时要明确说明:
- 你需要的是计划,不是代执行
- 步骤必须具体,不能只给抽象建议
- 最好包含验证方式与完成标志
求助模板
请帮我为下面任务制定一份执行计划。 你不需要执行任务本身,只需要输出计划。
任务: [要做什么]
目标: [最终想达到什么结果]
约束:
- [...]
- [...]
计划要求:
- 拆成明确、可执行的步骤
- 标明推荐顺序
- 必要时指出依赖、检查点或验证方式
- 如果有明显风险,补充关键注意点
输出格式: [例如:输出 5-8 个编号步骤,每步 1-3 句]
chat_with_agent 请求示例:
chat_with_agent(
to_agent="strong_agent",
text="""
请帮我为下面任务制定一份执行计划。
你不需要执行任务本身,只需要输出计划。
任务:
修改一个多模块功能。
目标:
以较低风险完成改动,并避免遗漏联动点。
约束:
- 尽量减少返工
- 需要保留可验证的中间检查点
计划要求:
1. 拆成可落地的步骤
2. 标明推荐顺序
3. 标出关键依赖和检查点
4. 尽量包含验证方式
输出格式:
请输出 3-5 个编号步骤,每步尽量具体。
""",
)
chat_with_agent 回复示例:
[SESSION: xxx]
下面是我为你制定的执行计划:
1. ...
2. ...
- 其中
xxx是本次对话的session_id,你需要记录下来以便后续追问细化,一般是一个较长的字符串,例如local_agent:to:strong_agent:1776406127168:e11b08d8
chat_with_agent 追问示例(假设你想让对方细化第 3 步):
chat_with_agent(
to_agent="strong_agent",
text="请基于刚才的计划,再细化第 3 步。仍然只需要补计划,不需要代执行任务。",
session_id="xxx",
)
收到计划后怎么用
把更强 Agent 的回复当作“执行计划输入”,不是“任务已经完成”。
你应该做的是:
- 提炼出真正可执行的步骤
- 判断是否需要按你的环境做微调
- 由你自己执行这些步骤
- 遇到新不确定性时,再继续细化计划
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.
- 10d ago First seen · 185 lines · 39 tokens per session scan A f03e62e7c3e6
make_plan is a skill published in the GitHub repository agentscope-ai/QwenPaw (35,089 stars, last pushed today), licensed Apache-2.0. It adds 39 tokens to every session and 1,467 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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