benchmark-research-skill

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.

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.

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.

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.

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.

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.