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

benchmark-research-skill/
  SKILL.md
  config.example.yaml
  requirements.txt
  scripts/
    config_utils.py
    fetch_context.py
    search_papers.py
    collect_links.py
    generate_report.py

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

pip install -r requirements.txt

Minimal Config

Copy config.example.yaml to config.yaml, then set a real vault_path.

language: "zh"
vault_path: "E:/Your/Obsidian/Vault"

benchmark_survey:
  workspace_dir: "Benchmark_Surveys"
  papers_dir_name: "papers"
  reports_dir_name: "reports"
  survey_runs_dir_name: "surveys"
  enable_collect_links: false

These are the fields that matter most:

Optional Search Tuning

These are only used by search_papers.py, mostly for direction survey mode.

research_domains:
  "your-topic":
    keywords:
      - "main phrase"
      - "synonym"
      - "related task phrase"
    arxiv_categories:
      - "cs.AI"
      - "cs.LG"

excluded_keywords:
  - "workshop"
  - "survey"
  - "review"

If you mostly use single-paper analysis, this section is not important.

Single-paper save layout

For paper 2503.10522, the config above resolves to:

Path/to/Your/Obsidian/Vault/
  Benchmark_Surveys/
    papers/
      2503.10522/
        context.json
        benchmarks.json
        links.json
        assets/
    reports/
      2503.10522.md

Survey save layout

For topic text-to-audio, the config above resolves to:

Path/to/Your/Obsidian/Vault/
  Benchmark_Surveys/
    surveys/
      YYYY-MM-DD-text-to-audio/
        papers.json
        links.json
        reports/
          benchmark-survey.md

Typical Usage

You normally use this as a Claude Code skill in natural language.

Single Paper

Ask Claude Code:

Use benchmark-research-skill to analyze what benchmarks this paper uses: 2503.10522

Or:

Use benchmark-research-skill to read this PDF and tell me the datasets, metrics, baselines, and experiment figures.

Direction Survey

Ask Claude Code:

Use benchmark-research-skill to survey benchmarks for text-to-audio generation.

Or:

Use benchmark-research-skill to find what benchmarks this direction uses, follow related work up to two layers, and produce a Markdown report.

Output Shape

Single-paper report

# Benchmark Analysis: [Paper Title]

## Conclusion

## Benchmark Table
| Dataset | Link | Evaluated Ability | Metrics | Compared Methods | Evidence |
|---|---|---|---|---|---|

## Datasets And Metrics

## Related Work / Baselines

Survey report

# Benchmark Survey: [Direction]

## TL;DR

## Basic Metrics

## Benchmark Overview
| Dataset | Link | Suitable Task | Common Metrics | Representative Work |
|---|---|---|---|---|

## Representative Works

## Grouped By Benchmark

## Experiment Figures/Tables

## Related Work Discovery Path

Notes

• Single-paper mode should stay fast by default.
• Link collection is optional because it can be slow and rate-limited.
• Tables are primary evidence.
• The skill is meant to be auditable: evidence stays next to the note.