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paper-analyzer

@proyecto26 · 收录于 1 周前

Transform academic papers into in-depth technical articles with multiple writing style options. Use the MinerU Cloud API for high-precision PDF parsing, automatically extracting images, tables, and formulas. Optional formula explanations and GitHub code analysis, generating Markdown and HTML formats.

场景论文解读技术写作

适合你，如果需要将学术论文转化为可读的技术文章

/ 下载安装
paper-analyzer.skill双击,或拖进 Claude 桌面版 / Cowork,即完成安装↓ .skill↓ .zip
用别的 agent?下载 .zip 解压,把文件夹放进它的技能目录
Claude Code~/.claude/skills/(项目级 .claude/skills/)
Codex CLI~/.codex/skills/
Cursor自动读取上面两处目录
其他工具见其文档的「skills」目录;两个下载是同一份文件,只是名字不同
/ 通过 npx 安装 校验哈希
npx oh-my-skill add proyecto26/sherlock-ai-plugin/paper-analyzer
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- proyecto26/sherlock-ai-plugin/paper-analyzer
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify proyecto26/sherlock-ai-plugin/paper-analyzer
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
33GitHub stars
~583最小装载
~3.1K含声明引用
~6.7K文本包总量
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怎么用

技能原文 SKILL.md作者撰写 · MIT · 0be95c9

Academic Paper Analyzer – In-Depth Analysis of Academic Papers

Core Capabilities
  • MinerU Cloud API for high-precision PDF parsing
  • Automatic extraction of images, tables, and LaTeX formulas
  • Multiple writing styles: storytelling / academic / concise
  • Optional formula explanations: insert formula images with detailed symbol explanations
  • Optional code analysis: combine explanations with GitHub open-source code
  • Output Markdown + HTML (base64-embedded images)
Prerequisites
MinerU API Token
  1. Visit https://mineru.net and register an account
  2. Obtain an API Token
  3. Set an environment variable (recommended): ```bash export MINERU_TOKEN="your_token_here" ```
Dependency Installation
pip install requests markdown
Workflow
Step 1: PDF Parsing (Using MinerU API)
python scripts/mineru_api.py <pdf_path> <output_dir>

Or pass the token directly:

python scripts/mineru_api.py paper.pdf ./output YOUR_TOKEN

Output:

  • output_dir/*.md – Markdown files (including formulas and tables)
  • output_dir/images/ – High-quality extracted images
Step 2: Extract Paper Metadata
python scripts/extract_paper_info.py <output_dir>/*.md paper_info.json
Step 3: Style Selection (Ask the User)

Before generating the article, you must ask the user to choose the following options:

1. Writing Style (Required)

| Style | Characteristics | Use Cases | |------|-----------------|-----------| | storytelling | Starts from intuition, uses metaphors and examples, narrative-driven | Blogs, tech columns, popular science | | academic | Professional terminology, rigorous expression, preserves original concepts | Academic reports, surveys, research group sharing | | concise | Straight to the point, tables and lists, high information density | Quick reads, paper overviews, technical research |

2. Formula Option (Optional)

| Option | Description | |------|-------------| | with-formulas | Insert formula images and explain symbol meanings in detail | | no-formulas (default) | Pure text description, no formula images |

3. Code Option (Optional, only if the paper has GitHub)

| Option | Description | |------|-------------| | with-code | Clone the repository, include key source code, and explain it alongside the paper | | no-code (default) | No code analysis |

Step 4: Intelligent Article Generation

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API Limits
  • Maximum file size: 200MB
  • Maximum pages per file: 600
  • Supports PDF, DOC, PPT, images, and more
按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →

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