A specialized tool for searching, analyzing, and summarizing peer-reviewed academic papers from open-access repositories with automated credibility scoring.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install scholar-research
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 scholar-research 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 Scholar Research Skill?
The Scholar Research skill is a robust academic assistant designed to streamline the literature review process. By integrating with a vast array of open-access repositories such as arXiv, PubMed, and OpenAlex, it allows developers and researchers using Openclaw Skills to quickly identify high-impact papers. This tool goes beyond simple keyword matching by providing detailed summaries, methodology breakdowns, and automated figure extraction.
The core value of this skill lies in its sophisticated scoring engine, which evaluates papers based on citations, journal impact, and author reputation. This ensures that users can distinguish between groundbreaking peer-reviewed research and early-stage pre-prints, providing a clear roadmap of a field's evolution through generated timelines and credibility distributions.
Scholar Research Skill Use Cases
- Identifying high-impact research papers on specific technical or scientific topics.
- Performing automated credibility assessments for academic citations.
- Generating field evolution timelines to track the history of a specific breakthrough.
- Extracting figures, tables, and methodologies from top-tier research documents.
- Summarizing large volumes of open-access literature for quick literature reviews.
How Scholar Research Skill Works
- Querying across multiple enabled open-access sources and aggregators simultaneously.
- Fetching metadata and full-text PDFs from repositories like CORE and Unpaywall.
- Calculating a comprehensive credibility score (0-100) based on weighted metrics such as citation count and peer-review status.
- Ranking results by combining the calculated score with topical relevance.
- Presenting a curated list of top papers alongside a field-wide timeline and credibility distribution.
- Optionally extracting visual data like figures and tables from the highest-scored papers using computer vision.
Scholar Research Skill Setup
Ensure you have Python 3.8+ installed, then install the required dependencies using the following command:
pip install requests beautifulsoup4 pypdf2 opencv-python transformers matplotlib
After installation, configure your preferred sources and scoring weights in the config.json file. You can add custom university repositories or adjust the priority of factors like citation counts and publication recency to fine-tune your Openclaw Skills experience.
Scholar Research Skill Data Schema & Taxonomy
The skill organizes research data into a structured format for easy review:
| Component | Description | Format |
|---|---|---|
| Metadata | Title, authors, DOI, citations, and journal info | JSON/Table |
| Credibility Score | Weighted score based on quality factors (100 pts + 40 bonus) | Integer |
| Field Timeline | Visual distribution of papers and breakthroughs over years | ASCII Chart |
| Summaries | AI-generated methodology and content overviews | Markdown |
| Extracted Data | Images of figures and tables from top-scored PDFs | Image Files |
| Source Attribution | Links to PDFs, SI, and original repository pages | URL Links |
Scholar Research Skill Advanced Features
- Customizable scoring weights to prioritize factors like recency or reproducibility.
- Preset profiles including strict, balanced, and recent_only for rapid configuration.
- Support for custom user-added sources via API integration within the local configuration.
- Automated retraction status checks to ensure data integrity within Openclaw Skills.
- Figure and table extraction using OpenCV-based figure detection from research PDFs.
- Comprehensive author network analysis and reputation scoring (h-index integration).
SKILL.md
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