An automated assistant for reading scientific papers, generating structured reports, and scaffolding implementation code for reproduction.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install paper-research-assistant
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-research-assistant 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 Research Assistant?
Paper Research Assistant is a specialized tool designed to bridge the gap between academic theory and technical implementation. By integrating this skill into your Openclaw Skills library, you can automate the tedious process of extracting core contributions, methodology frameworks, and experimental configurations from dense academic PDFs or arXiv links.
The skill excels at synthesizing complex information into structured reports and identifying official external resources such as GitHub repositories and HuggingFace datasets. For developers and researchers, it significantly reduces the time from discovery to implementation by generating framework-specific code skeletons in PyTorch or TensorFlow, making Openclaw Skills a vital asset for any R&D workflow.
Paper Research Assistant Use Cases
- Extracting core contributions and methodology from arXiv papers or local PDFs.
- Generating standardized research reports for team review or personal knowledge management.
- Searching for official codebases, datasets, and licensing information automatically.
- Scaffolding implementation-ready code frameworks to jumpstart paper reproduction.
- Designing comprehensive experimental plans including ablation studies and baseline comparisons.
How Paper Research Assistant Works
- Receives a paper input via a local PDF file path or a direct link to arXiv/journal sites.
- Executes a parsing script to extract metadata, authors, and document structure while identifying the research type.
- Analyzes the text to extract methodology, key formulas, and experimental hyperparameters.
- Compiles the extracted data into a structured Markdown report based on a predefined template.
- Performs automated searches across GitHub and HuggingFace to find supporting resources.
- Generates an executable code skeleton and environment configuration to facilitate the reproduction of results.
Paper Research Assistant Setup
To set up this skill within your Openclaw Skills environment, ensure you have the necessary PDF parsing and API dependencies installed:
# Install core dependencies
pip install pymupdf pdfplumber
# Extract metadata from a research paper
python scripts/parse_paper.py --pdf /path/to/paper.pdf --output /tmp/paper_metadata.json
# Generate a research report using the extracted metadata
python scripts/generate_report.py --metadata /tmp/paper_metadata.json --template references/report_template.md --output /tmp/research_report.md
# Scaffold a reproduction codebase in PyTorch
python scripts/scaffold_code.py --paper-json /tmp/paper_metadata.json --framework pytorch --output-dir /tmp/repo
Paper Research Assistant Data Schema & Taxonomy
The skill organizes research data through a structured pipeline to ensure clarity and reproducibility:
| Data Type | format | Description |
|---|---|---|
| Paper Metadata | JSON | Contains title, authors, abstract, keywords, and identified research category. |
| Research Report | Markdown | A structured document covering contributions, methodology, and experimental setup. |
| Code Framework | Python | Boilerplate code for the model architecture, training loops, and evaluation scripts. |
| Experiment Design | Markdown | Detailed plan for baselines, hyperparameter search, and resource estimation. |
Paper Research Assistant Advanced Features
- Multi-framework support for generating boilerplate code in PyTorch or TensorFlow based on paper descriptions.
- Automated resource validation that checks the availability and licensing of external code and datasets.
- Integration with the arXiv API for seamless metadata fetching without requiring local file uploads.
- Custom report templates that allow users to adapt the output to specific academic or organizational standards.
- Heuristic detection of mathematical formulas and core algorithmic workflows for accurate code scaffolding.
SKILL.md
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