An AI-powered research assistant that transforms academic papers into rigorous peer-review reports and publication-quality architectural diagrams.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install paper-reading
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-reading 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 Analyzer?
The Paper Analyzer is a specialized tool within the ecosystem of Openclaw Skills designed for researchers and engineers who need to quickly digest complex academic literature. It acts as a senior AI researcher, providing a skeptical yet fair analysis of papers from arXiv, PDFs, or pasted text. Beyond simple summaries, it focuses on technical precision, mathematical pipelines, and epistemic discipline, ensuring that users can distinguish between verified facts and reasonable inferences.
By integrating the Paper Analyzer into your workflow, you leverage a high-performance Openclaw Skills agent that can dissect a paper's motivation, technical contribution, and experimental rigor. Whether you are preparing for a literature review or evaluating a new model's deployment readiness, this skill provides the depth of a NeurIPS-level peer reviewer, complete with automated visualization of complex methodologies.
Paper Analyzer Use Cases
- Conducting a deep-dive analysis of new arXiv preprints to understand technical novelty.
- Generating publication-quality architecture diagrams for presentations or internal documentation.
- Performing a critical assessment of a paper's experimental rigor and statistical significance.
- Translating complex English research papers into structured Chinese reports with localized labels.
- Visualizing the workflow and data transformation pipeline of a specific research method.
How Paper Analyzer Works
- The skill parses user intent and CLI-style flags from the message to determine language, output format, and visual requirements.
- It fetches the paper content using specialized tools for PDF extraction or direct HTML fetching from arXiv for richer data.
- A structured, six-section Markdown report is generated, adhering to strict professional peer-review standards and technical honesty.
- If drawing intent is detected, the agent constructs an architecture diagram using a dedicated design system and theme-specific palettes.
- The final outputs, including reports and high-resolution images (HTML, SVG, or PNG), are saved to the local workspace and presented via file links.
Paper Analyzer Setup
To get started with this component of your Openclaw Skills library, ensure your agent has access to the output directory and necessary rendering tools.
# Ensure Playwright is available for high-quality PNG rendering
pip install playwright
playwright install chromium
The skill automatically utilizes the references/ and assets/ directories to manage design templates and language mappings.
Paper Analyzer Data Schema & Taxonomy
The Paper Analyzer organizes its output systematically to ensure clarity and reproducibility across Openclaw Skills workflows. Data is categorized by language and visual intent:
| File Type | Default Path Pattern | Description |
|---|---|---|
| Markdown Report | /outputs/{paper}_report.md |
Comprehensive technical analysis and peer review. |
| Multi-lang Report | /outputs/{paper}_report_{lang}.md |
Translated technical reports (e.g., Chinese). |
| HTML Architecture | /outputs/{paper}_architecture.html |
Interactive diagram with summary cards and fonts. |
| SVG Diagram | /outputs/{paper}_architecture.svg |
Standalone vector graphic of the method pipeline. |
| PNG Render | /outputs/{paper}_architecture.png |
Retina-quality image export for external use. |
Paper Analyzer Advanced Features
- Epistemic Tagging: Every claim is labeled as [paper], [inferred], or [external] to maintain rigorous technical honesty.
- Multi-Format Visualization: Generates diagrams in HTML, SVG, and PNG with support for dark and light technical themes.
- Pipeline Contrast: Ability to visually contrast prior approach failure modes with the new proposed methodology.
- ArXiv Optimization: Automatically fetches HTML versions of papers for faster, more accurate parsing compared to standard PDFs.
- Context-Aware Translation: Inherits report language for diagram labels while maintaining standard technical English terminology.
SKILL.md
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