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 pdf-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/pdf-zh)<a href="https://agentmods.dev/skills/agentscope-ai/qwenpaw/pdf-zh"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/pdf-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/pdf-zh"><img src="https://agentmods.dev/badge/skills/agentscope-ai/qwenpaw/pdf-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 | $0.00095 | $0.02369 |
| Opus 5 | $0.00048 | $0.01184 |
| Sonnet 5 | $0.00019 | $0.00474 |
| Haiku 4.5 | $0.00010 | $0.00237 |
Grade A, and why
pdf 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 11d 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:
- pdf — 100% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 330 lines — stays where its author put it; the contents beside it link to each section on GitHub.
重要: 所有
scripts/路径均相对于此技能目录。 运行方式:cd {this_skill_dir} && python scripts/...或使用execute_shell_command的cwd参数。
PDF 处理指南
前置要求
- pypdf:核心 PDF 读写功能
- pdfplumber:文本和表格提取
- reportlab:PDF 创建
- pdftotext (poppler-utils):命令行文本提取
- pdftoppm (poppler-utils):PDF 转图片
- qpdf:PDF 操作(合并、拆分、旋转、解密)
概述
本指南涵盖了使用 Python 库和命令行工具进行 PDF 处理的基本操作。有关高级功能、JavaScript 库和详细示例,请参阅 REFERENCE.md。如果需要填写 PDF 表单,请阅读 FORMS.md 并按照其中的说明操作。
快速入门
from pypdf import PdfReader, PdfWriter
# 读取 PDF
reader = PdfReader("document.pdf")
print(f"Pages: {len(reader.pages)}")
# 提取文本
text = ""
for page in reader.pages:
text += page.extract_text()
Python 库
pypdf - 基本操作
合并 PDF
from pypdf import PdfWriter, PdfReader
writer = PdfWriter()
for pdf_file in ["doc1.pdf", "doc2.pdf", "doc3.pdf"]:
reader = PdfReader(pdf_file)
for page in reader.pages:
writer.add_page(page)
with open("merged.pdf", "wb") as output:
writer.write(output)
拆分 PDF
reader = PdfReader("input.pdf")
for i, page in enumerate(reader.pages):
writer = PdfWriter()
writer.add_page(page)
with open(f"page_{i+1}.pdf", "wb") as output:
writer.write(output)
提取元数据
reader = PdfReader("document.pdf")
meta = reader.metadata
print(f"Title: {meta.title}")
print(f"Author: {meta.author}")
print(f"Subject: {meta.subject}")
print(f"Creator: {meta.creator}")
旋转页面
reader = PdfReader("input.pdf")
writer = PdfWriter()
page = reader.pages[0]
page.rotate(90) # 顺时针旋转 90 度
writer.add_page(page)
with open("rotated.pdf", "wb") as output:
writer.write(output)
pdfplumber - 文本和表格提取
提取带布局的文本
import pdfplumber
with pdfplumber.open("document.pdf") as pdf:
for page in pdf.pages:
text = page.extract_text()
print(text)
提取表格
with pdfplumber.open("document.pdf") as pdf:
for i, page in enumerate(pdf.pages):
tables = page.extract_tables()
for j, table in enumerate(tables):
print(f"Table {j+1} on page {i+1}:")
for row in table:
print(row)
What ships with it
11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- forms.md 11 KB
- LICENSE.txt 1.4 KB
- reference.md 16 KB
- scripts/check_bounding_boxes.py 2.7 KB runs code
- scripts/check_fillable_fields.py 268 B runs code
- scripts/convert_pdf_to_images.py 1008 B runs code
- scripts/create_validation_image.py 1.2 KB runs code
- scripts/extract_form_field_info.py 4.2 KB runs code
- scripts/extract_form_structure.py 3.9 KB runs code
- scripts/fill_fillable_fields.py 3.7 KB runs code
- scripts/fill_pdf_form_with_annotations.py 3.2 KB runs code
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.
- 11d ago First seen · 330 lines · 95 tokens per session scan A d81e0d27245f
pdf is a skill published in the GitHub repository agentscope-ai/QwenPaw (34,741 stars, last pushed today), licensed Apache-2.0. It adds 95 tokens to every session and 2,369 once invoked, about $0.0005 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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