Automate technical academic paper analysis using dynamic SOPs tailored to specific research types like methods, datasets, or benchmarks.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install paper-summarize-academic
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-summarize-academic 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 Summarize?
Paper Summarize is a sophisticated academic tool designed to provide deep technical analysis of research publications. Unlike generic summarizers, this skill utilizes dynamic Standard Operating Procedure (SOP) selection to adapt its critique based on the specific nature of the paper. Whether you are analyzing a new algorithm, a benchmark dataset, or a multimodal system, this entry in the Openclaw Skills library ensures the output meets the rigorous standards of top-tier conference reviewers.
By synthesizing core contributions, methodology critiques, and experimental assessments, Paper Summarize transforms a raw abstract and title into a structured, searchable knowledge asset. It is built for developers and researchers who need to maintain technical precision while processing large volumes of literature, ensuring that critical details regarding mathematical novelty, computational costs, and dataset biases are never overlooked.
Paper Summarize Use Cases
- Conducting rapid literature reviews for new AI research projects.
- Performing technical due diligence on state-of-the-art (SOTA) model releases.
- Building a local, structured library of paper critiques for academic labs.
- Auditing experimental protocols and dataset fairness in recent publications.
- Generating deep-dive summaries for internal reading groups and technical reports.
How Paper Summarize Works
- The user inputs the paper's metadata including title, authors, abstract, and a specific topic classification.
- The skill dynamically selects the most appropriate analysis template (SOP) based on the provided topic (e.g., method, dataset, or tech_report).
- An AI agent processes the content against top-tier conference criteria (NeurIPS/ICLR/ACL), generating a critique of at least 3000 characters.
- The system organizes the output into specific sections: Methodology Critique, Experimental Assessment, Strengths/Weaknesses, and Impact Assessment.
- The finalized summary and the raw prompt used for generation are saved to a structured local directory for long-term storage and reproducibility.
Paper Summarize Setup
To integrate this skill into your research environment, ensure your local directory structure is prepared. The Openclaw Skills framework handles the execution via CLI.
# Summarize a methodology-focused paper
paper_summarize --title "Title Name" --topic "method" --abstract "..." --authors "..."
# Summarize a dataset or benchmark paper
paper_summarize --title "Benchmark Name" --topic "dataset" --abstract "..." --authors "..."
Paper Summarize Data Schema & Taxonomy
The skill organizes research data into a predictable hierarchy within the research/ directory to ensure compatibility with other Openclaw Skills.
| Component | Location | Format |
|---|---|---|
| AI Summary | research/{domain}/ai_summaries/{paper_title}.md |
Markdown file with 2000+ word technical critique. |
| Prompt Record | research/{domain}/prompts/{paper_title}_prompt.txt |
Raw text file of the prompt for reproducibility. |
| SOP Templates | src/lib/agents/topic-sops.ts |
TypeScript definitions for dynamic analysis logic. |
Paper Summarize Advanced Features
- Dynamic SOP Selection: Automatically switches analysis logic for 10+ paper types including RL alignment and speech processing.
- Multi-Dimensional Critique: Enforces minimum character counts for technical sections to prevent superficial summaries.
- Dataset Focus: Specialized logic to extract and evaluate training and testing dataset protocols.
- Local Knowledge Management: Automatic creation of nested directory structures based on research domains.
- Reproducibility Tracking: Maintains a dedicated log of actual prompts used to generate every summary.
SKILL.md
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