{"repo":"EternalWavee/benchmark-research-skill","free":true,"listed":false,"github":"https://github.com/EternalWavee/benchmark-research-skill","clone":"git clone https://github.com/EternalWavee/benchmark-research-skill.git","description":"Claude Code skill for benchmark research. Survey papers to find datasets, metrics, and evaluation protocols used in a research direction.","language":"Python","stars":37,"topics":["claude-code","skills","claude-code-skill"],"license":"MIT","category":"analytics","readme_excerpt":"benchmark-research-skill 中文版 benchmark-research-skill is a Claude Code style skill for benchmark research. It is built for two jobs: 1. Analyze one paper and answer: what benchmarks, datasets, metrics, baselines, and experiment evidence does it use? 2. Survey a direction and answer: what benchmarks are practical for evaluating work in this area? The design is deliberately simple: - scripts fetch and organize evidence - Claude Code does the semantic extraction - reports are written into your configured Obsidian workspace What It Does Mode 1: Single Paper Given an arXiv ID or PDF, the skill will: - fetch arXiv source / tex first - read experiment, evaluation, and result sections - extract tables and benchmark-facing snippets - optionally recover source figures or render PDF pages - let Claude Code write benchmarks.json - optionally collect missing links - generate a Markdown note Mode 2: Direction Survey Given a topic, the skill will: - search related method/system papers - prefer papers with real experiments, not just titles containing \"benchmark\" - read their benchmark sections - expand through compared methods and baseline relations - aggregate datasets, metrics, representative works, and discovery paths - generate a survey report Repository Layout Only four scripts are part of the public workflow: - fetch context.py - search papers.py - collect links.py - generate report.py config utils.py is only an internal helper for config-based path resolution. Install Minimal Config C","default_branch":null,"files":null,"tree":[],"storefront":"/r/EternalWavee","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/EternalWavee/benchmark-research-skill/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}