An AI-driven academic discovery tool that connects your agent to the Semantic Scholar API for comprehensive literature search and citation analysis.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install semanticscholar-search-skill
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install semanticscholar-search-skill using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Semantic Scholar Search?
Semantic Scholar Search is a specialized academic research tool designed to provide AI agents with direct access to millions of scientific papers. By leveraging the Semantic Scholar API, this skill enables researchers and developers to retrieve deep metadata, including abstracts, author profiles, and real-time citation metrics. It serves as a vital component in the ecosystem of Openclaw Skills for those needing reliable scholarly data.
The skill simplifies the process of navigating complex academic databases by translating natural language queries into structured API requests. Whether you are identifying the latest trends in machine learning or performing a deep dive into citation networks, this tool automates data retrieval to streamline your technical research workflows.
Semantic Scholar Search Use Cases
- Automating literature reviews for scientific papers and technical documentation.
- Identifying influential researchers through author profiles and h-index tracking.
- Analyzing the impact of specific publications via citation and reference mapping.
- Extracting structured metadata like DOIs and abstracts for data analysis pipelines.
- Discovering relevant publication venues and journals for specific research domains.
How Semantic Scholar Search Works
- The user provides a search query, DOI, or author ID via the command interface.
- The skill processes the request and calls the Semantic Scholar API to fetch the most relevant academic records.
- The system extracts high-fidelity metadata including publication years, citation counts, and author details.
- Data is parsed and organized into a structured format suitable for human reading or programmatic use.
- The AI agent delivers the formatted results, which can be exported as JSON for integration with other Openclaw Skills.
Semantic Scholar Search Setup
This skill does not require complex authentication as it utilizes the free tier of the Semantic Scholar API. To begin using it within your environment, ensure you have the necessary agent permissions to make outbound network requests. Use the following command structure to verify your setup:
# Search for academic papers on a specific topic
/semantic-scholar-search --query "deep learning" --format json
Semantic Scholar Search Data Schema & Taxonomy
The skill organizes research data into a clear taxonomy to facilitate easy consumption by other Openclaw Skills.
| Entity | Key Attributes | Data Type |
|---|---|---|
| Paper | title, abstract, paperId, DOI | Object |
| Author | name, authorId, hIndex | Object |
| Metrics | citationCount, referenceCount | Integer |
| Venue | conference, journal, year | String |
Semantic Scholar Search Advanced Features
- Support for DOI and Paper ID lookups for precise document retrieval.
- Exportable JSON output for building automated research databases or knowledge graphs.
- Comprehensive author analytics including total publication counts and influence metrics.
- Seamless cross-compatibility with other Openclaw Skills like PDF extractors and science data tools.
SKILL.md
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