Academic Paper Analyzer – In-Depth Analysis of Academic Papers
SkillDocs & knowledgeTransform 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.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Academic Paper Analyzer – In-Depth Analysis of Academic Papers skill
What this skill tells your AI
The instructions your AI receives, as published by proyecto26/sherlock-ai-plugin in skills/paper-analyzer/SKILL.md and read by ahel’s review.
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
- Visit https://mineru.net and register an account
- Obtain an API Token
- Set an environment variable (recommended):
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
(...)
API Limits
- Maximum file size: 200MB
- Maximum pages per file: 600
- Supports PDF, DOC, PPT, images, and more
Signals
- GitHub stars
- 35
- Forks
- 2
- Last commit
- Aug 2026
Advanced
- Catalog kind
- skill
- Gateway key
paper-analyzer-proyecto26- Source
- github.com/proyecto26/sherlock-ai-plugin