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。

Security Scan

The skill largely matches its stated purpose (PDF→diagram generation) but includes actions that warrant caution—most notably executing model-generated Python code and sending paper text plus prompts to external image/LLM APIs, with some minor mismatches between declared and used environment variables.

Purpose & Capability

Name/description (paper→diagram generation) align with the code and required binaries: Python and an external image-generation API (BANANA2 / acedata). The primary credential (BANANA2_API_KEY) is appropriate. One minor incoherence: many modules expect an LLM callable (llm_call_fn) and SKILL.md suggests PAPER_DIAGRAM_LLM_URL/KEY, but those LLM env vars are not declared as required — they are optional but used in prompts/fallbacks. Overall capabilities are consistent with the stated purpose.

Instruction Scope

Runtime instructions and code cause network calls to external image-generation APIs and may send substantial paper text and generated prompts. The chart_generator writes and executes arbitrary Python code (via subprocess.run) that originates from model-generated content — this is powerful and can run arbitrary code on the host. image_generator also disables session.trust_env (bypassing system proxy environment variables), which can change networking behavior and has privacy implications. SKILL.md recommends storing API/LLM keys in a config file (~/.openclaw/openclaw.json), which implies persistent local storage of secrets.

Install Mechanism

The skill is instruction-only in registry terms (no install spec) but includes multiple Python scripts that require dependencies (requests, pdfminer.six, optional PyMuPDF/pypdf/pdfplumber, pytesseract/Pillow). Lack of a formal install step means dependencies must be installed manually; there is no remote download or unclear install URL. No high-risk external download is present in the manifest.

Credentials

The required env vars (BANANA2_API_URL, BANANA2_API_KEY) are proportional to the declared image-generation functionality. The code also reads fallback vars (ACEDATA_API_KEY, PAPER_DIAGRAM_API_KEY, PAPER_DIAGRAM_LLM_URL/KEY) and SKILL.md suggests storing LLM keys — these additional credentials are optional but present in prompts and code; users should be aware the skill can use any of these if set. No unrelated cloud credentials (AWS, GCP) are requested.

Persistence & Privilege

The skill does not request 'always: true' and is user-invocable. It does not appear to modify other skills or system-wide agent settings programmatically. It suggests the user add credentials to ~/.openclaw/openclaw.json (a recommendation), but it does not automatically persist or escalate privileges.

What to consider before installing

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.

paper-diagram-skill

从论文（PDF 或纯文本）自动生成学术插图的完整工具链。

工具概览

工具	功能	调用方式
draw.py	论文可视化主流程	python draw.py [args]
pdf_to_text.py	PDF → 纯文本提取	python pdf_to_text.py [args]

---

工具 1：PDF → Text 提取

将 PDF 文件转换为纯文本，供 draw.py 使用。

# 基本用法
python {baseDir}/scripts/pdf_to_text.py "/path/to/paper.pdf"

# 限制页数（取前 20 页）
python {baseDir}/scripts/pdf_to_text.py "/path/to/paper.pdf" --max-pages 20

# 输出到文件
python {baseDir}/scripts/pdf_to_text.py "/path/to/paper.pdf" -o extracted.txt

支持的后端（自动选择）： pdfminer.six → PyMuPDF → pdfplumber → PyPDF2 → pypdf

注意：推荐先安装 pip install pdfminer.six，提取质量最高。

---

工具 2：论文图生成

快速开始

# 方式 A：从 PDF 直接生成（自动提取 + 画图）
python {baseDir}/scripts/draw.py \
  --pdf-path "/path/to/paper.pdf" \
  --user-requirement "Transformer 架构图" \
  --api-url "https://api.example.com/generate" \
  --api-key "sk-your-key"

# 方式 B：扫描模式（自动识别所有可图化内容）
python {baseDir}/scripts/draw.py \
  --pdf-path "/path/to/paper.pdf" \
  --mode scan

# 方式 C：批量生成（选定的图）
python {baseDir}/scripts/draw.py \
  --pdf-path "/path/to/paper.pdf" \
  --mode scan \
  --scan-results "<base64-encoded-results>" \
  --selected-ids "1,3,5" \
  --api-url "..." --api-key "..."

# 方式 D：直接传入论文文本
python {baseDir}/scripts/draw.py \
  --paper-content "论文纯文本内容..." \
  --user-requirement "画一个算法流程图" \
  --style cvpr

核心参数

参数	说明	默认值
--pdf-path	PDF 文件路径（会自动提取文本）	—
--paper-content	直接传入论文纯文本	—
--user-requirement	用户对图片的描述	—
--mode	single(单图) 或 scan(全篇扫描)	single
--api-url	生图 API 地址	环境变量
--api-key	生图 API 密钥	环境变量
--style	学术风格: cvpr neurips icml nature	cvpr
--skip-check	跳过自校核	False
--section-ref	对应论文章节（如 Section 3.1）	—

环境变量

生图 API 凭证按以下优先级自动解析，任意一个有值即可：

优先级	API Key 变量	API URL 变量
1 (推荐)	BANANA2_API_KEY	BANANA2_API_URL
2	ACEDATA_API_KEY	PAPER_DIAGRAM_API_URL
3	PAPER_DIAGRAM_API_KEY	—

未设置 URL 时默认 https://api.acedata.cloud/nano-banana/images

推荐在 ~/.openclaw/openclaw.json 中配置：

{
  "skills": {
    "paper-diagram": {
      "env": {
        "BANANA2_API_URL": "https://api.acedata.cloud/nano-banana/images",
        "BANANA2_API_KEY": "your-api-key-here",
        "PAPER_DIAGRAM_LLM_URL": "https://your-llm-api/v1/chat/completions",
        "PAPER_DIAGRAM_LLM_KEY": "your-llm-key-here",
        "PAPER_DIAGRAM_LLM_MODEL": "gpt-4"
      }
    }
  }
}

生成的图类型

类型	说明	生成方式
teaser	动机/概念对比图	Mermaid → AI 生图
architecture	系统架构图	Mermaid → AI 生图
flowchart	算法流程图	Mermaid → AI 生图
environment	实验环境图	Mermaid → AI 生图
results	结果对比图	Matplotlib 代码绘图（精确数据）

学术风格预设

预设	场景	主色调
cvpr	CV/CG 会议	蓝色系
neurips	ML/NLP 会议	紫橙系
icml	ML 会议	蓝红绿系
nature	Science/Nature 期刊	柔蓝柔绿

输出

• 图片文件：保存到 outputs/ 目录（或设置的输出目录）
• LaTeX 图注：可直接复制到论文
• Word 图注：纯文本格式
• Mermaid 拓扑：可导出用于其他绘图工具

自校核机制

生成图前会自动对比拓扑描述与原文：

• ✅ 一致 → 直接生成
• ⚠️ 小问题 → 询问用户（接受 / 重新生成 / 手动修改）
• ❌ 严重错误 → 建议重新生成

---

推荐工作流

论文 PDF
  │
  ▼
pdf_to_text.py  ──→  纯文本
  │                  │
  ▼                  ▼
draw.py (scan)   draw.py (single)
  │                  │
  ▼                  ▼
扫描报告           指定描述 → 生成单张图
  │
  ▼
用户选择 (e.g. 1,3,5)
  │
  ▼
draw.py (batch)  ──→  多张图 + LaTeX 图注

安装依赖

pip install pdfminer.six requests

可选（提升 PDF 提取质量）：

pip install pymupdf pdfplumber pypdf