ynu-papergraphgeneration-openclaw

多模态论文可视化引擎 — 从 PDF 或纯文本论文自动生成学术插图。 支持：全篇扫描识别可图化内容、双编码器架构图/算法流程图/动机图生成、 自校核机制、LaTeX/Word 图注输出、Matplotlib 结果图精确绘图。 也包含独立的 PDF → Text 提取工具。

Key things to consider before installing or enabling this skill: - It will call external image/LLM APIs and therefore will send prompts and (potentially large) portions of your paper text to those endpoints. Only configure API keys for services you trust and understand the provider's privacy policy. - The chart generator component accepts a model-produced Matplotlib script and writes it to disk then executes it with subprocess.run. That means arbitrary Python code produced by the LLM (or returned by an external service) can run on your machine. If you proceed, run the skill in a restricted environment (container, VM) or review generated code before execution. - image_generator disables trust of system proxy env vars (session.trust_env = False). This can bypass local proxy/audit infrastructure and change how requests leave your host — be cautious if you rely on corporate proxies or monitoring. - The skill recommends storing keys in ~/.openclaw/openclaw.json; storing API keys locally is convenient but increases persistence of secrets. Prefer using a limited-scope API key, rotate keys regularly, and avoid placing high-privilege credentials here. - The code expects an LLM callback (llm_call_fn) and mentions optional PAPER_DIAGRAM_LLM_* env vars; if you configure an LLM endpoint/keys, the skill may send prompts (scanning/merging/extraction) to that LLM. If you don't intend to share your paper content with an external LLM, do not provide those keys. - There is no automatic installer; you must manually install listed Python packages (requests, pdfminer.six, optionally PyMuPDF/pdfplumber/pypdf, Pillow/pytesseract). Ensure dependencies are installed in an isolated environment to limit impact. What would change this assessment: explicit safeguards around executing generated code (e.g., running generated scripts in a sandbox, requiring an explicit user confirmation step before execution), documented safe networking defaults (respecting proxies), or removal of subprocess execution would reduce concerns. Conversely, evidence of hidden endpoints, credential exfiltration code, or automatic persistence of secrets would raise the verdict to malicious.

Type: OpenClaw Skill Name: ynu-papergraphgeneration-openclaw Version: 1.0.0 The skill bundle contains a significant security risk in scripts/chart_generator.py, which generates and executes arbitrary Python code using subprocess.run based on LLM-provided data, creating a potential Remote Code Execution (RCE) vector. Additionally, scripts/draw.py contains highly specific, hardcoded logic for a particular academic paper ('DeCLIP'), which is unusual for a general-purpose tool. While these behaviors represent high-risk patterns and vulnerabilities, there is no explicit evidence of intentional data exfiltration or malicious persistence.