benchmark-research-skill

Extract benchmarks, datasets, and metrics from research papers.

36|2|Updated Apr 24, 2026

Search Tags:#data extraction #research survey #paper analysis #benchmark analysis #benchmark identification

Ready to run

npx skills add https://github.com/EternalWavee/benchmark-research-skill --skill benchmark-research-skill

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: benchmark-research-skill
Source: https://github.com/EternalWavee/benchmark-research-skill/tree/main
Command: npx skills add https://github.com/EternalWavee/benchmark-research-skill --skill benchmark-research-skill

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires PyYAML, requests, PyMuPDF, fitz, xml.etree.ElementTree, argparse, json, re, yaml, urllib, os, gzip, tarfile, tempfile, html, html.parser, collections, datetime, pathlib, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill streamlines the process of analyzing research papers, extracting benchmark-related information, and generating comprehensive reports, saving researchers valuable time and effort.

Core Features & Use Cases

Single Paper Analysis: Analyze individual papers to identify benchmarks, datasets, metrics, baselines, and experiment evidence.
Direction Survey: Survey research directions to identify practical benchmarks for evaluating work in that area.
Integration with Obsidian: Export analysis results directly to Obsidian for further research and documentation.

Quick Start

Use the benchmark-research-skill to analyze a paper with the arXiv ID '2503.10522'.

Frequently Asked Questions about benchmark-research-skill

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I extract benchmarks and datasets from research papers?▼

You extract benchmarks and datasets from research papers by automating semantic extraction to identify baselines, metrics, and experiment evidence. This process requires Claude Code for semantic analysis and PyMuPDF to parse PDF files.

Can I survey a research direction to find practical benchmarks for evaluating work?▼

Yes, you can survey a research direction to identify practical benchmarks for evaluating work. The direction survey feature analyzes multiple related papers to extract relevant datasets and metrics for your specific research area.

Does benchmark analysis work with Obsidian for exporting research results?▼

Benchmark analysis works with Obsidian by exporting extracted results directly into the application. This integration allows you to document and manage the identified datasets, metrics, and related work within your research notes.

What do I need to analyze arXiv papers for benchmark identification?▼

To analyze arXiv papers for benchmark identification you need Claude Code for semantic extraction and dependencies including PyYAML, requests, and PyMuPDF. The process handles arXiv IDs to extract datasets and metrics automatically.

Is there a way to analyze single papers for experiment evidence and baselines?▼

Yes, single paper analysis identifies experiment evidence and baselines by processing individual research documents. It extracts specific benchmarks, datasets, metrics, and related work from the targeted paper using semantic analysis.

What are the limitations when extracting related work from PDF files?▼

Extracting related work from PDF files depends on Claude Code for semantic extraction and requires specific dependencies like PyMuPDF and PyYAML. The analysis is limited by PDF structure quality and requires proper environment setup.