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 file_reader-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/file_reader-zh)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw/file_reader-zh"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/file_reader-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/file_reader-zh"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/file_reader-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 | $0.00045 | $0.00455 |
| Opus 5 | $0.00023 | $0.00228 |
| Sonnet 5 | $0.00009 | $0.00091 |
| Haiku 4.5 | $0.00005 | $0.00046 |
Grade A, and why
file_reader 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- file_reader — 100% identical, 8 lines differ
What it actually says
文件读取工具箱
当用户要求读取或总结本地文本文件时使用此技能。PDF、Office 文档、图片、音频和视频不在此技能范围内，应由其专用技能/工具处理。
快速类型检查
读取前先进行类型探测:
file -b --mime-type "/path/to/file"
如果文件较大，避免输出全部内容；提取小部分相关内容并进行总结。
文本文件（使用 read_file）
适用于: .txt、.md、.json、.yaml/.yml、.csv/.tsv、.log、.sql、ini、toml、py、js、html、xml 源代码。
步骤:
- 使用
read_file获取内容。 - 总结关键部分或展示用户请求的相关片段。
- 对于 JSON/YAML，列出顶层键和重要字段。
- 对于 CSV/TSV，展示表头和前几行，然后总结各列。
大型日志文件
如果文件很大，使用 tail 窗口:
tail -n 200 "/path/to/file.log"
总结最近的错误/警告和值得注意的模式。
超出范围
以下内容不在此技能中处理（由其他技能负责）:
- Office (docx/xlsx/pptx)
- 图片
- 音频/视频
安全与行为规范
- 绝不执行不可信的文件。
- 优先读取所需的最小部分。
- 如果缺少所需工具，说明限制并请用户提供其他格式。
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 · 59 lines · 45 tokens per session scan A 1921533df90e
file_reader is a skill published in the GitHub repository agentscope-ai/QwenPaw (35,089 stars, last pushed today), licensed Apache-2.0. It adds 45 tokens to every session and 455 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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