scholargraph

What This Skill Does

ScholarGraph is a sophisticated academic intelligence toolkit designed to bridge the gap between massive volumes of scientific literature and actionable researcher insights. It functions as an AI-powered research assistant capable of searching diverse academic databases like arXiv and Semantic Scholar to surface the most relevant papers. Beyond simple discovery, it enables deep analysis of research materials, identification of knowledge gaps in specific scientific domains, and the construction of interactive knowledge graphs that visualize complex concept relationships. Whether you are a student mapping out a new field of study or a professional researcher looking to synthesize findings across dozens of papers, ScholarGraph provides structured learning paths, proactive progress monitoring, and critical evaluation tools to streamline your workflow.

Installation

To integrate this skill into your environment, use the OpenClaw command line interface:

1. Ensure you have the OpenClaw agent initialized.
2. Run the command: clawhub install openclaw/skills/skills/josephyb97/scholargraph
3. After installation, configure your preferred AI backend by exporting your API key. ScholarGraph supports over 15 providers, including OpenAI, Anthropic, and DeepSeek. For example, to use DeepSeek, set your environment variable: export DEEPSEEK_API_KEY="your-key". You may also need to configure the provider name via export AI_PROVIDER=deepseek.

Use Cases

• Literature Review: Rapidly aggregate top-cited papers on niche AI topics to draft a literature review.
• Skill Acquisition: Generate structured learning cards and step-by-step roadmaps to master a complex academic concept from scratch.
• Gap Analysis: Scan a body of research to identify unexplored areas, helping define your next PhD or research project focus.
• Collaborative Research: Synthesize findings from multiple papers to highlight commonalities and contradictions, providing a unified viewpoint.

Example Prompts

1. "@scholargraph, search for the latest advancements in Transformer architectures from the past three months and provide a summary of the top 3 papers."
2. "@scholargraph, identify the knowledge gaps in current Reinforcement Learning from Human Feedback (RLHF) techniques and suggest three papers I should read to bridge these gaps."
3. "@scholargraph, create a learning path for understanding Diffusion Models, including a Mermaid diagram showing the dependency between concepts."

Tips & Limitations

• Rate Limiting: Because ScholarGraph queries multiple external APIs, ensure your API keys have sufficient credit. The toolkit handles automatic retries, but extremely frequent requests may trigger provider-side throttling.
• Verify Citations: While the AI excels at synthesis, always double-check the specific citations provided, especially for niche papers, as hallucinations can occasionally occur in LLM-generated summaries.
• Modular Use: Don't try to use every module at once. Start by defining your scope with the Concept Learner before moving to deep Paper Analysis to avoid information overload.