Research Summarizer

What Is Research Summarizer?

Research Summarizer is a structured research summarization agent skill designed for non-developer users who need to process and synthesize information from academic papers, web articles, reports, and documentation. Rather than providing generic summaries, Research Summarizer extracts key findings, generates comparative analyses, and produces properly formatted citations. This tool is especially valuable for product managers, analysts, founders, and professionals who are required to digest large volumes of information and transform it into actionable insights.

As an extension for platforms such as Claude Code, Codex, Gemini CLI, and OpenClaw, Research Summarizer streamlines the process of understanding complex source material, enabling users to focus on decision-making rather than data wrangling. The skill is open-source, licensed under MIT, and maintained by Alireza Rezvani.

Why Use Research Summarizer?

In many professional environments, individuals are inundated with dense documents—ranging from scientific papers to technical documentation—that are time-consuming to parse. Traditional summarization tools often yield superficial results, omitting the nuanced insights required for informed decision-making. Research Summarizer addresses these challenges by:

• Delivering Structured Output: Instead of a one-size-fits-all summary, it provides a repeatable framework that highlights relevant findings, methodologies, and implications.
• Facilitating Comparative Analysis: Users can easily compare multiple sources, identifying consensus, divergence, and gaps in the literature.
• Ensuring Citation Accuracy: Automatic extraction and formatting of citations reduces manual errors and saves time, supporting academic and professional reporting standards.
• Supporting Non-Developers: Intuitive slash commands and integration with popular AI platforms eliminate the need for coding expertise.

This skill accelerates research workflows, reduces the cognitive load associated with manual review, and improves the quality of research briefs and reports.

How to Get Started

To begin using Research Summarizer, ensure that your environment supports Claude Code or one of the compatible platforms (Codex, Gemini CLI, OpenClaw). The skill is accessible via simple slash commands, requiring no programming knowledge. Below is a basic workflow:

1. Installation

Clone the repository and follow the setup instructions relevant to your platform:

git clone https://github.com/alirezarezvani/claude-skills.git
cd claude-skills/product-team/research-summarizer
## Follow the README for integration steps

2. Using Slash

Commands

Once integrated, you can interact with Research Summarizer using natural language and predefined commands. For example:

Summarize a single document:

/research:summarize [link-to-paper-or-article]

Compare multiple sources:

/research:compare [link1] [link2] [link3]

Extract citations from a document:

/research:cite [link-to-document]

The tool will process the input and return structured outputs directly in your interface.

Key Features

Research Summarizer offers a suite of capabilities tailored for effective research synthesis:

• Structured Summaries: Produces standardized briefs, including sections for key findings, methodologies, limitations, and implications.
• Comparative Synthesis: Aligns findings from up to five sources side-by-side, highlighting similarities, differences, and emerging trends.
• Automated Citation Extraction: Scans documents for references and outputs them in widely-used formats (APA, MLA, etc.).
• Broad Document Support: Accepts URLs or uploads for academic papers, news articles, technical documentation, and formal reports.
• Platform-Integrated: Operates as a plugin within supported AI code assistants, providing frictionless access for non-technical users.
• Repeatability: Employs a consistent approach to summarization, which is especially important for teams standardizing knowledge management processes.

Example Output

Summarizing a Paper:

/research:summarize https://arxiv.org/abs/XXXX.XXXXX

Output:

Title: Understanding Deep Neural Networks
Key Findings:
- Model X outperforms previous benchmarks by 15%
- Data augmentation was critical to performance gains

Methodology:
- 100k image dataset, stratified sampling
- Comparative evaluation against baseline models

Implications:
- Potential for improved real-world image classification
- Recommends further study on model robustness

Citations:
[1] Doe, J., et al. (2024). Understanding Deep Neural Networks. arXiv preprint arXiv:XXXX.XXXXX.

Comparing Sources:

/research:compare https://source1.com https://source2.com

Output:

| Source       | Key Findings                  | Methodology     | Limitations         |
|--------------|------------------------------|-----------------|---------------------|
| Source 1     | Increase in accuracy by 10%  | Randomized trial| Small sample size   |
| Source 2     | Comparable accuracy, faster  | Field study     | Short study period  |
Synthesis: Both sources report accuracy improvements, but differ in methodology and scale.

Best Practices

• Specify Your Objective: When requesting a summary or comparison, indicate the aspect of the document most relevant to your goals (e.g., focus on methodology, results, or implications).
• Curate Reliable Sources: Ensure that the links or documents you provide are accessible and from reputable publishers to enhance output quality.
• Limit Scope for Comparisons: For optimal clarity, compare between 2-5 sources; larger sets may reduce the granularity of insights.
• Review and Edit: While Research Summarizer automates much of the process, always review outputs for critical decisions or public dissemination.

Important Notes

• Research Summarizer relies on the accessibility of online documents. Subscription-based or paywalled content may not be fully processed.
• While citation extraction is robust, always verify citations for high-stakes academic or legal use.
• The skill is optimized for English-language documents; results may vary with non-English sources.
• Regular updates and enhancements are available on the project’s GitHub repository. Contributions and feedback are encouraged under the MIT license.
• As with any AI-powered tool, use critical judgment—outputs are intended to accelerate, not replace, thorough human review.