scholargraph（scholargraph 搜索）

ScholarGraph - Academic Literature Intelligence Toolkit

Overview

ScholarGraph is a comprehensive academic literature intelligence toolkit that helps researchers efficiently search, analyze, and manage academic papers using AI-powered tools. Features 11 academic search sources with intelligent domain-based source selection and PDF download capabilities.

Security & Privacy

This skill operates with the following permissions:

• Network Access: Queries academic APIs (arXiv, Semantic Scholar, OpenAlex, PubMed, CrossRef, DBLP, IEEE, CORE, Google Scholar, Unpaywall) and web search services
• File System: Reads/writes configuration files, downloads PDFs, stores knowledge graphs in SQLite database (data/knowledge-graphs.db)
• LLM Integration: Sends custom system prompts to AI providers for structured JSON output (concept extraction, paper analysis, etc.)
• Optional Python: PDF figure extraction (pymupdf) and PPT export (python-pptx) require Python 3.8+

Data Storage: All data is stored locally. No telemetry or analytics are collected.

API Keys: Optional API keys are only used for their respective services and are never transmitted elsewhere.

Source Code: Open source under MIT license at https://github.com/Josephyb97/ScholarGraph

Features

Core Modules (6)

1. Literature Search - Multi-source academic paper discovery (11 sources)

- Free sources: arXiv, Semantic Scholar, OpenAlex (250M+), PubMed (biomedical), CrossRef (150M+ DOI), DBLP (CS), Web Search - API-key sources: IEEE Xplore, CORE, Google Scholar (SerpAPI), Unpaywall (OA PDF) - Adapter-based plugin architecture for easy extension - Complementary search strategy with auto domain detection (biomedical/cs/engineering/physics) - Priority-based source selection per domain - Query expansion for better search results - PDF download with multi-strategy URL resolution

1. Concept Learner - Rapid knowledge framework construction

- Generate structured learning cards - Include code examples and related papers - Support beginner/intermediate/advanced depth levels

1. Knowledge Gap Detector - Proactive blind spot identification

- Analyze knowledge coverage in specific domains - Identify critical, recommended, and optional gaps - Provide learning recommendations and time estimates

1. Progress Tracker - Real-time field monitoring

- Track research topics and keywords - Generate daily/weekly/monthly reports - Monitor trending papers and topics

1. Paper Analyzer - Deep paper analysis

- Extract key contributions and insights - Support quick/standard/deep analysis modes - Generate structured analysis reports

1. Knowledge Graph Builder - Concept relationship visualization

- Build interactive knowledge graphs - Support Mermaid and JSON output formats - Find learning paths between concepts - SQLite-based persistent storage - Bidirectional concept-paper indexing

Advanced Features (9)

1. Review Detector - Automatic review paper identification

- Multi-dimensional scoring (title 30% + citations 25% + abstract 25% + AI 20%) - Chinese and English keyword support - Confidence-based filtering with user confirmation

1. Concept Extractor - Extract concepts from review papers

- AI-powered extraction of 15-30 core concepts - Four-level categorization (foundation/core/advanced/application) - Importance scoring and relationship identification - Cross-review deduplication and merging

1. Review-to-Graph Workflow - End-to-end pipeline

- Search reviews -> Detect -> Confirm -> Analyze -> Extract concepts - Build knowledge graph -> Enrich with key papers -> Index -> Store - Interactive or automatic confirmation mode

1. Knowledge Graph Query - Bidirectional literature indexing

- Concept -> papers: find papers related to a concept - Paper -> concepts: find concepts covered by a paper - Paper recommendations based on multiple concepts - SQLite-optimized high-performance queries

1. Compare Concepts - Compare two concepts

- Identify similarities and differences - Provide use case recommendations

1. Compare Papers - Compare multiple papers

- Find common themes and differences - Generate synthesis analysis

1. Critique - Critical paper analysis

- Identify strengths and weaknesses - Find research gaps and improvement suggestions - Support custom focus areas

1. Learning Path - Find optimal learning paths

- Discover paths between concepts - Generate topological learning order - Visualize with Mermaid diagrams

1. Graph Management - Manage persistent knowledge graphs

- List all saved graphs - View graph statistics - Export graphs to JSON - Visualize with Mermaid

1. Paper Visualization - Interactive paper presentation

- Convert paper analysis to HTML slide presentations - Academic dark/light themes with responsive typography - Keyboard/touch/scroll navigation, edit mode (E key) - PDF figure extraction (pymupdf) and PPT export (python-pptx) - 8+ slides: title, abstract, key points, methodology, experiments, contributions, limitations, references

1. Interactive Knowledge Graph - D3.js force-directed visualization

- Convert knowledge graphs to interactive HTML with D3.js v7 - Node size reflects paper count, edge thickness reflects concept tightness - Zoom/pan, node dragging, click-to-detail panel, search, legend - Paper preview bridge: click "View Presentation" to open paper slides in new tab - Category colors: foundation=#4FC3F7, core=#FFB74D, advanced=#CE93D8, application=#81C784

Technical Features

• 11 Academic Search Sources: arXiv, Semantic Scholar, OpenAlex, PubMed, CrossRef, DBLP, IEEE Xplore, CORE, Google Scholar, Unpaywall, Web Search
• Complementary Search Strategy: Auto-detects query domain and selects optimal source combination
• Adapter Pattern: Plugin-based search source architecture for easy extension
• PDF Download: Multi-strategy URL resolution (direct, Unpaywall, OpenAlex OA, CORE)
• Multi-AI Provider Support: 15+ AI providers including OpenAI, Anthropic, DeepSeek, Qwen, Zhipu AI, etc.
• SQLite Persistence: Knowledge graphs stored in SQLite database via bun:sqlite
• Bidirectional Indexing: Concept-paper and paper-concept bidirectional query support
• Rate Limiting: Per-source rate limiting with automatic retry and delay
• Interactive HTML Output: Paper slide presentations, D3.js knowledge graph visualizations
• Multiple Output Formats: Markdown, JSON, Mermaid, HTML, PPTX
• TypeScript + Bun: Fast and type-safe runtime
• CLI + API: Both command-line and programmatic interfaces

Installation

# Clone repository
git clone https://github.com/Josephyb97/ScholarGraph.git
cd ScholarGraph

# Install dependencies
bun install

# Initialize configuration
bun run cli.ts config init

Configuration

Set up your AI provider:

# Using OpenAI
export AI_PROVIDER=openai
export OPENAI_API_KEY="your-api-key"

# Using DeepSeek
export AI_PROVIDER=deepseek
export DEEPSEEK_API_KEY="your-api-key"

# Using Qwen (通义千问)
export AI_PROVIDER=qwen
export QWEN_API_KEY="your-api-key"

Academic Source API Keys (optional, expand search coverage)

export NCBI_API_KEY="your-key"           # PubMed high-speed access (10 req/s)
export IEEE_API_KEY="your-key"           # IEEE Xplore engineering papers
export CORE_API_KEY="your-key"           # CORE open access full text
export UNPAYWALL_EMAIL="your@email.com"  # Unpaywall OA PDF resolver
export CROSSREF_MAILTO="your@email.com"  # CrossRef polite pool (higher rate)
export SERPAPI_KEY="your-key"            # Google Scholar (via SerpAPI)
export SERPER_API_KEY="your-key"         # Web search via Serper

Usage Examples

Search Literature

# Auto-select best sources based on query domain
lit search "transformer attention" --limit 20

# Specify domain for optimized source selection
lit search "CRISPR gene editing" --domain biomedical

# Use specific sources (comma-separated)
lit search "deep learning" --source semantic_scholar,arxiv,openalex --sort citations

# Search and download PDFs
lit search "attention is all you need" --download --limit 3

Download PDFs

# Search and download PDFs
lit download "transformer" --limit 5 --output ./papers

Learn Concepts

lit learn "BERT" --depth advanced --papers --code --output bert-card.md

Detect Knowledge Gaps

lit detect --domain "Deep Learning" --known "CNN,RNN" --output gaps.md

Analyze Papers

lit analyze "https://arxiv.org/abs/1706.03762" --mode deep --output analysis.md

Build Knowledge Graph

lit graph transformer attention BERT GPT --format mermaid --output graph.md

Compare Concepts

lit compare concepts CNN RNN --output comparison.md

Compare Papers

lit compare papers "url1" "url2" "url3" --output comparison.md

Critical Analysis

lit critique "paper-url" --focus "novelty,scalability" --output critique.md

Find Learning Path

lit path "Machine Learning" "Deep Learning" --concepts "Neural Networks" --output path.md

Search Review Papers

lit review-search "attention mechanism" --limit 10

Build Knowledge Graph from Reviews

# From search query (interactive mode)
lit review-graph "deep learning" --output dl-graph --enrich

# From specific URL
lit review-graph "https://arxiv.org/abs/xxxx" --output my-graph --enrich

# Auto-confirm mode (non-interactive)
lit review-graph "transformer" --output tf-graph --enrich --auto-confirm

Query Knowledge Graph

# Find papers by concept
lit query concept "transformer" --graph dl-graph --limit 20

# Find concepts by paper
lit query paper "https://arxiv.org/abs/1706.03762" --graph dl-graph

Manage Knowledge Graphs

# List all graphs
lit graph-list

# View graph statistics
lit graph-stats dl-graph

# Visualize graph
lit graph-viz dl-graph --format mermaid --output graph.md

# Export graph
lit graph-export dl-graph --output dl-graph.json

Paper Visualization

# Generate interactive HTML presentation
lit paper-viz "https://arxiv.org/abs/1706.03762" --output attention.html

# With theme and PPT export
lit paper-viz "https://arxiv.org/abs/1706.03762" --mode deep --theme academic-light --ppt

# Manually provide figures
lit paper-viz "https://example.com/paper" --figures ./my-figures

Interactive Knowledge Graph

# Generate interactive D3.js graph from existing knowledge graph
lit graph-interactive dl-graph --output dl-interactive.html

# Without paper data (lighter weight)
lit graph-interactive my-graph --no-paper-viz

Use Cases

1. Quick Field Onboarding

• Learn core concepts
• Detect prerequisite gaps
• Build knowledge graph
• Plan learning path

2. Deep Paper Understanding

• Analyze paper in depth
• Perform critical analysis
• Learn new concepts from paper
• Compare with related papers

3. Research Progress Tracking

• Monitor research topics
• Track latest papers
• Generate progress reports

4. Concept Comparison

• Compare technical approaches
• Evaluate different models
• Build comparison graphs

5. Review-Driven Knowledge Building

• Search and identify review papers
• Extract concepts from reviews
• Build persistent knowledge graphs
• Query concept-paper relationships

6. Paper Visualization & Graph Exploration

• Analyze paper and generate interactive HTML presentation
• Build knowledge graph from reviews
• Generate interactive D3.js graph with paper preview
• Click nodes to view paper details and open presentations

Project Structure

ScholarGraph/
├── cli.ts                      # Unified CLI entry
├── config.ts                   # Configuration management
├── README.md                   # Project documentation
├── CHANGELOG.md                # Version history
├── SKILL.md                    # This file
│
├── shared/                     # Shared modules
│   ├── ai-provider.ts          # AI provider abstraction
│   ├── types.ts                # Type definitions
│   ├── validators.ts           # Parameter validation
│   ├── errors.ts               # Error handling
│   └── utils.ts                # Utility functions
│
├── literature-search/          # Literature search module
│   └── scripts/
│       ├── search.ts           # Search engine core
│       ├── types.ts            # Type definitions
│       ├── query-expander.ts   # Query expansion
│       ├── search-strategy.ts  # Complementary search strategy
│       ├── pdf-downloader.ts   # PDF download module
│       └── adapters/           # Search source adapters
│           ├── base.ts         # Adapter interface & base class
│           ├── registry.ts     # Adapter registry
│           ├── index.ts        # Barrel export
│           ├── arxiv-adapter.ts
│           ├── semantic-scholar-adapter.ts
│           ├── web-adapter.ts
│           ├── openalex-adapter.ts
│           ├── pubmed-adapter.ts
│           ├── crossref-adapter.ts
│           ├── dblp-adapter.ts
│           ├── ieee-adapter.ts
│           ├── core-adapter.ts
│           ├── unpaywall-adapter.ts
│           └── google-scholar-adapter.ts
│
├── concept-learner/            # Concept learning module
├── knowledge-gap-detector/     # Gap detection module
├── progress-tracker/           # Progress tracking module
├── paper-analyzer/             # Paper analysis module
│
├── review-detector/            # Review paper identification
│   └── scripts/
│       ├── detect.ts           # Multi-dimensional scoring
│       └── types.ts
│
├── concept-extractor/          # Concept extraction from reviews
│   └── scripts/
│       ├── extract.ts          # AI-powered extraction
│       └── types.ts
│
├── knowledge-graph/            # Knowledge graph module
│   └── scripts/
│       ├── graph.ts            # Graph building core
│       ├── indexer.ts          # Bidirectional indexing
│       ├── storage.ts          # SQLite persistence
│       └── enricher.ts         # Key paper association
│
├── paper-viz/                  # Paper visualization
│   └── scripts/
│       ├── types.ts            # Presentation data interfaces
│       ├── slide-builder.ts    # PaperAnalysis → slides
│       ├── html-generator.ts   # Self-contained HTML generation
│       ├── pdf-figure-extractor.ts  # PDF figure extraction (pymupdf)
│       └── ppt-exporter.ts     # PPT export (python-pptx)
│
├── graph-viz/                  # Interactive knowledge graph
│   └── scripts/
│       ├── types.ts            # D3 graph data interfaces
│       ├── graph-data-adapter.ts # KnowledgeGraph → D3 data
│       ├── html-generator.ts   # Interactive HTML (D3.js v7)
│       └── paper-viz-bridge.ts # Graph → paper presentation bridge
│
├── workflows/                  # End-to-end workflows
│   └── review-to-graph.ts      # Review to graph pipeline
│
├── data/                       # Data directory (auto-created)
│   └── knowledge-graphs.db     # SQLite database
│
├── downloads/                  # PDF downloads (auto-created)
│   └── pdfs/
│       └── metadata.json       # Download index
│
└── test/                       # Tests and documentation
    ├── ADVANCED_FEATURES.md
    ├── TEST_RESULTS.md
    └── scripts/

Supported AI Providers

International

• OpenAI
• Anthropic (Claude)
• Azure OpenAI
• Groq
• Together AI
• Ollama (local)

China

• 通义千问 (Qwen/DashScope)
• DeepSeek
• 智谱 AI (GLM)
• MiniMax
• Moonshot (Kimi)
• 百川 AI (Baichuan)
• 零一万物 (Yi)
• 豆包 (Doubao)

Output Formats

Markdown Reports

• Concept cards with definitions, components, history, applications
• Gap reports with analysis and recommendations
• Progress reports with trending topics
• Paper analyses with methods, experiments, contributions
• Comparison analyses with similarities and differences
• Critical analyses with strengths, weaknesses, and suggestions

JSON Data

Structured data for programmatic processing

Mermaid Diagrams

Interactive knowledge graphs and learning paths

Interactive HTML

• Paper slide presentations with keyboard/scroll/touch navigation
• D3.js force-directed knowledge graph with zoom, search, and paper panel

Requirements

• Bun 1.3+ or Node.js 18+
• AI provider API key
• Internet connection for paper search
• Python 3.8+ (optional, for PDF figure extraction and PPT export)

License

MIT License

Links

• GitHub: https://github.com/Josephyb97/ScholarGraph
• Issues: https://github.com/Josephyb97/ScholarGraph/issues
• Discussions: https://github.com/Josephyb97/ScholarGraph/discussions

Version

Current version: 1.0.0

Author

ScholarGraph Team

Design Inspirations:

• frontend-slides - Paper slide presentation design reference
• Argo Scholar - Interactive knowledge graph design reference

*For detailed documentation, see README.md* *For advanced features, see test/ADVANCED_FEATURES.md* *For test results, see test/TEST_RESULTS.md*