Skill flagged — suspicious patterns detected
ClawHub Security flagged this skill as suspicious. Review the scan results before using.
Paper Viz
v1.0.0从论文 PDF、实验截图或表格图片中提取实验结果,自动匹配图表类型,调用 Python 生成确定性图表,并导出 PNG、PDF 和 LaTeX;默认在用户指定输出根目录下自动创建与论文同名的文件夹保存结果。
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MIT-0
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LicenseMIT-0 · Free to use, modify, and redistribute. No attribution required.
Security Scan
OpenClaw
Suspicious
medium confidencePurpose & Capability
The SKILL.md expects the agent to 'run Python to generate figures' and to perform OCR/PDF parsing, but the registry metadata lists no required binaries or dependencies. For this functionality the skill would legitimately need Python and libraries (pdfminer/pyMuPDF, pytesseract/OpenCV, matplotlib/seaborn, pandas, etc.). The absence of declared runtime requirements is an incoherence.
Instruction Scope
Instructions explicitly direct the agent to read local PDFs, screenshots and other files, create folders, write JSON, generate and execute Python plotting scripts, and save outputs. The Execution Policy instructs the agent to 'Do not ask for step-by-step confirmation' and to prefer actual execution over just suggesting steps — this grants broad discretion to access and modify local files and to run code automatically, increasing risk if not sandboxed or confirmed by the user.
Install Mechanism
No install spec and no code files (instruction-only) — low-install risk. However, the skill's behavior implies installing or using Python packages and possibly external OCR/binary tools (tesseract, poppler). The lack of an install mechanism or dependency list is a practical gap and makes it unclear how the agent should satisfy runtime requirements.
Credentials
The skill requests no environment variables or credentials, which is proportionate to its stated purpose. However, it does request access to local files and the current working directory; while expected for this use case, users should be aware this gives the skill read/write access to local filesystem locations it is allowed to access.
Persistence & Privilege
always: false (no forced persistence) and autonomous invocation is default. The combination of 'do not ask for step-by-step confirmation' and preference for actual execution means the skill may run file reads and Python code autonomously — not a permission change per se, but a behavioral risk worth noting.
What to consider before installing
What to consider before installing:
- The skill will try to read local PDFs/images, write files, and run Python code automatically unless blocked. That is expected for a visualization tool, but it also means generated Python could perform arbitrary actions if executed without review.
- The package metadata lists no required binaries or dependencies, yet the skill needs Python and likely OCR/PDF and plotting libraries. Ask the publisher for an explicit dependency list (Python version, required pip packages, any external binaries like Tesseract/poppler) or provide those yourself.
- Prefer running this in a sandboxed environment: a VM or container with limited file access and no network egress, or require the agent to produce code for your review before execution.
- Provide an explicit output folder (do not rely on default CWD) and request a preview step before plotting if you want manual control.
- If you cannot verify dependencies or code, do not allow autonomous execution; instead require the agent to output experimental_data.json and plotting code for manual inspection and execution.
What would change this assessment: seeing an explicit dependency/install spec, a declared Python binary requirement, or code files with transparent, reviewable plotting scripts and clear sandboxing instructions would move this toward benign. Conversely, any added steps that auto-execute downloaded code or request unrelated credentials would increase suspicion.Like a lobster shell, security has layers — review code before you run it.
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License
MIT-0
Free to use, modify, and redistribute. No attribution required.
