ClawHub

ScholarGraph

Academic literature intelligence toolkit for multi-source paper search, analysis, and knowledge graph building with AI assistance.

Audits

Pass
ClawScanReview

Agentic behavior and permission review.

Static analysisReview

Pattern checks against bundled files.

VirusTotalPass

Multi-engine malware detections and file reputation.

Install

openclaw skills install scholargraph

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

  2. Concept Learner - Rapid knowledge framework construction

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

  3. 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

  4. Progress Tracker - Real-time field monitoring

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

  5. Paper Analyzer - Deep paper analysis

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

  6. 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

  2. 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

  3. 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

  4. 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

  5. Compare Concepts - Compare two concepts

    • Identify similarities and differences
    • Provide use case recommendations

  6. Compare Papers - Compare multiple papers

    • Find common themes and differences
    • Generate synthesis analysis

  7. Critique - Critical paper analysis

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

  8. Learning Path - Find optimal learning paths

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

  9. Graph Management - Manage persistent knowledge graphs

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

  10. 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

  11. 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

Version

Current version: 1.0.0

Author

ScholarGraph Team

Design Inspirations:

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