Automate the discovery and summarization of robotics research papers from top-tier academic journals and conferences.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install paper-scout
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 paper-scout 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 paper_scout?
paper_scout is a specialized academic research tool designed to bridge the gap between vast citation databases and actionable insights. By leveraging the power of Openclaw Skills, researchers and engineers can efficiently monitor the latest developments in robotics, imitation learning, and human-robot interaction without manual searching. It integrates multiple data sources to provide a unified view of emerging literature, ensuring you never miss a critical publication from high-impact venues like Science Robotics or CVPR.
The skill acts as a bridge for developers and academics who need to stay at the cutting edge of AI and robotics without spending hours on manual database queries. It synthesizes complex abstracts into major innovation points, providing a streamlined workflow for literature review and competitive analysis.
paper_scout Use Cases
- Staying updated on recent publications from specific venues like TRO, IJRR, or Science Robotics.
- Generating weekly literature digests for research teams focusing on reinforcement learning or imitation learning.
- Scouting for state-of-the-art methods in human-robot collaboration for academic projects.
- Synthesizing major innovation points from high-impact conferences like ICLR and ICCV.
How paper_scout Works
- Queries the CrossRef API as the primary engine to retrieve structured metadata from major academic publishers.
- Supplements missing abstracts or newer entries by performing real-time browser-based searches on Google Scholar.
- Normalizes the data by removing duplicates and filtering results based on user-defined publication years and target venues.
- Analyzes the gathered content to extract core innovation points and major contributions.
- Generates a comprehensive Markdown report saved directly to the user's desktop for immediate review.
paper_scout Setup
To integrate this within your Openclaw Skills framework, configure your SKILL.yaml to specify your target keywords and preferred academic venues. Ensure you have the necessary environment for browser automation if using the Google Scholar fallback.
# Configuration example
# Edit your local skill configuration to include keywords like:
# - muscle parameter estimation
# - reinforcement learning
# - human intention prediction
paper_scout Data Schema & Taxonomy
The skill outputs a structured report with the following metadata taxonomy:
| Field | Description |
|---|---|
| Title | The full title of the research paper |
| Venue | The journal or conference (e.g., RAL, CVPR) |
| Year | The publication year |
| Authors | List of primary and contributing authors |
| Abstract | Brief summary of the paper's scope |
| Major Contributions | Synthesized innovation points extracted by the agent |
paper_scout Advanced Features
- Cross-source data integration with automated deduplication logic across CrossRef and Google Scholar.
- Interactive browser sessions for scraping JS-heavy academic search pages when API data is insufficient.
- Customizable date filtering (e.g., since:2024) to focus strictly on the most recent research windows.
- Extensible architecture allowing for future integration of specialized sources like IEEE Xplore or PubMed.
SKILL.md
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