# Scholargraph > Academic literature intelligence toolkit for multi-source paper search, analysis, and knowledge graph building with AI assistance. - Skill: `leoyeai-openclaw-master-skills/scholargraph` (Agent Skill, multi-file: 72 files) - Install (CLI): `npx skillmds@latest add leoyeai-openclaw-master-skills/scholargraph` - Raw SKILL.md: https://api.skillmd.com/api/skills/leoyeai-openclaw-master-skills/scholargraph/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Research & Search - Author: LeoYeAI (https://skillmd.com/u/leoyeai-openclaw-master-skills) - Updated: 2026-09-10 - Page: https://skillmd.com/skills/leoyeai-openclaw-master-skills/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) 7. **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 8. **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 9. **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 10. **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 11. **Compare Concepts** - Compare two concepts - Identify similarities and differences - Provide use case recommendations 12. **Compare Papers** - Compare multiple papers - Find common themes and differences - Generate synthesis analysis 13. **Critique** - Critical paper analysis - Identify strengths and weaknesses - Find research gaps and improvement suggestions - Support custom focus areas 14. **Learning Path** - Find optimal learning paths - Discover paths between concepts - Generate topological learning order - Visualize with Mermaid diagrams 15. **Graph Management** - Manage persistent knowledge graphs - List all saved graphs - View graph statistics - Export graphs to JSON - Visualize with Mermaid 16. **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 17. **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 ```bash # 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: ```bash # 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) ```bash 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 ```bash # 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 ```bash # Search and download PDFs lit download "transformer" --limit 5 --output ./papers ``` ### Learn Concepts ```bash lit learn "BERT" --depth advanced --papers --code --output bert-card.md ``` ### Detect Knowledge Gaps ```bash lit detect --domain "Deep Learning" --known "CNN,RNN" --output gaps.md ``` ### Analyze Papers ```bash lit analyze "https://arxiv.org/abs/1706.03762" --mode deep --output analysis.md ``` ### Build Knowledge Graph ```bash lit graph transformer attention BERT GPT --format mermaid --output graph.md ``` ### Compare Concepts ```bash lit compare concepts CNN RNN --output comparison.md ``` ### Compare Papers ```bash lit compare papers "url1" "url2" "url3" --output comparison.md ``` ### Critical Analysis ```bash lit critique "paper-url" --focus "novelty,scalability" --output critique.md ``` ### Find Learning Path ```bash lit path "Machine Learning" "Deep Learning" --concepts "Neural Networks" --output path.md ``` ### Search Review Papers ```bash lit review-search "attention mechanism" --limit 10 ``` ### Build Knowledge Graph from Reviews ```bash # 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 ```bash # 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 ```bash # 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 ```bash # 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 ```bash # 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](https://github.com/zarazhangrui/frontend-slides) - Paper slide presentation design reference - [Argo Scholar](https://github.com/poloclub/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*