Can I use this for any arXiv paper?

Yes, while it's optimized for LLM agent surveys, you can add any valid arXiv IDs to the input text file to download and extract text from other domains.

Does it require an internet connection?

Yes, a network connection is required to reach the arXiv repository and download the PDF documents.

Where are the downloaded files stored?

Files are stored within your local workspace under the `ref/agent-surveys/` directory, including dedicated folders for PDFs and text extracts.

How does it help with writing style?

It extracts headers and transitions to generate a style report, allowing you to identify how experts organize claims and transition between complex chapters.

What happens if a PDF is scanned or image-based?

If the text extraction returns empty, the PDF may be an image-only scan. You can try increasing the max-page limit or selecting a different source survey.

Agent Survey Corpus Builder

Name: Agent Survey Corpus Builder
Author: WILLOSCAR

byWILLOSCAR

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Learning & Documentation

Automates the collection and textual analysis of arXiv LLM agent surveys to establish high-quality writing and structural benchmarks.

About

The Agent Survey Corpus skill enables researchers and developers to build a localized reference library of peer-reviewed LLM agent surveys. By automatically downloading arXiv PDFs and extracting text, it facilitates the study of professional academic structures, rhetorical patterns, and data presentation styles. This tool is ideal for users looking to emulate the depth and rigor of academic literature in their own technical documentation or research reports, providing a data-driven foundation for outlining, sectioning, and evidence-backed writing.

Key Features

Automated arXiv PDF downloading via specific paper IDs
Configurable extraction depth with page-limit controls
Pre-configured directory structures for local knowledge management
Automatic generation of STYLE_REPORT.md for structural benchmarking
Intelligent text extraction for analyzing academic writing styles
89 GitHub stars

Use Cases

Creating a localized reference library for offline research and style learning
Analyzing sectioning and subsection granularity of top-tier AI agent surveys
Gathering evidence-backed rhetorical patterns for technical report writing

About

Key Features

Automated arXiv PDF downloading via specific paper IDs
Configurable extraction depth with page-limit controls
Pre-configured directory structures for local knowledge management
Automatic generation of STYLE_REPORT.md for structural benchmarking
Intelligent text extraction for analyzing academic writing styles
89 GitHub stars

Use Cases

Creating a localized reference library for offline research and style learning
Analyzing sectioning and subsection granularity of top-tier AI agent surveys
Gathering evidence-backed rhetorical patterns for technical report writing