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EternalWavee / benchmark-research-skill

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Claude Code skill for benchmark research. Survey papers to find datasets, metrics, and evaluation protocols used in a research direction.

16
0
100% credibility
Found Apr 25, 2026 at 16 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A helper tool for analyzing benchmarks, datasets, metrics, and experiments in arXiv papers, generating Markdown reports saved to an Obsidian note vault.

How It Works

1
📰 Discover the research helper

You stumble upon a simple tool that makes understanding experiments in research papers a breeze.

2
📁 Point to your notebook

You share the folder where you keep your digital notes, so reports land right there.

3
Pick your adventure
📄
One paper

Focus on a single study to uncover its tests and results.

🔍
Topic survey

Scan many papers to map out common tests in a field.

4
💬 Chat with your AI buddy

Just tell your AI assistant like Claude, 'Check this paper' or 'Survey this topic' – it feels like magic.

5
📥 Gathers the good stuff

It pulls key sections, tables, figures, and links without you lifting a finger.

📊 Reports ready to read

Open your notebook to find neat summaries, tables of datasets and scores, and easy links – perfect for quick insights.

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Star Growth

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AI-Generated Review

What is benchmark-research-skill?

This Python tool is a Claude code skill for benchmark research, pulling datasets, metrics, and eval protocols from arXiv papers or entire research directions. Feed it an arXiv ID or topic like "text-to-audio generation," and it fetches sources, extracts key experiment sections, then generates Markdown reports with tables of benchmarks, baselines, and links—dumping everything into your Obsidian vault. It's free to claude code download and claude code install via pip, solving the slog of manually surveying evals.

Why is it gaining traction?

It stands out by prioritizing arXiv source code over PDFs for accurate extraction, with optional GitHub/Hugging Face link hunting via claude github integration, all triggered by natural Claude code CLI prompts like "Use benchmark-research-skill to survey benchmarks for X." Obsidian-friendly outputs with embedded assets make audits easy, unlike generic scrapers—developers dig the Claude code skills workflow that keeps evidence traceable without leaving your vault.

Who should use this?

ML researchers benchmarking new models, who need quick surveys of practical datasets/metrics in a direction. Paper reviewers extracting baselines from experiments. Anyone using Claude code docs for claude github repo analysis or claude code pricing-free evals in cs.AI/LG.

Verdict

Worth a spin for niche benchmark hunts—solid claude code installieren, bilingual docs, and 16 stars show promise despite 1.0% credibility score and early maturity. No tests yet, but simple config makes it low-risk to try; pair with Claude for real value.

(178 words)

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