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Benchmark Extractor

Community
chtc66
benchmark-extractor

extract structured benchmark information from academic papers when the input includes multiple pdfs, abstracts, or links and the user needs chinese notes or table-ready fields for tasks, datasets, metrics, baselines, sota claims, code release, or evaluation settings.

Overview

Publisherchtc66
Repositoryacademic-skills
Skill namebenchmark-extractor
Stars
364
Forks
27
Bundled files
3
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by chtc66 on GitHub. Read the source before you install it.

Installation

Install the Benchmark Extractor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/chtc66/academic-skills/tree/main/benchmark-extractor
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/chtc66/academic-skills.git /tmp/academic-skills
mkdir -p .claude/skills
cp -r /tmp/academic-skills/benchmark-extractor .claude/skills/benchmark-extractor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Benchmark Extractor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Benchmark Extractor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Benchmark Extractor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Benchmark Extractor

用这个 skill 从论文中抽取 benchmark、dataset、metric、baseline、SOTA 等结构化信息,方便后续整理成表格、CSV 或 JSON。

工作流

  1. 先按论文逐篇抽取稳定字段。
  2. 参考 references/extraction_schema.md 统一字段名和缺失值写法。
  3. 参考 references/comparison_table_template.md 输出中文说明版或结构化表格版。

输入处理规则

  • 接收多篇 PDF、多篇摘要或一组论文链接。
  • 如果不同论文信息完整度不一致,仍按统一 schema 填写。
  • 未明确写出的字段统一写为 未知,不要臆造。

输出模式

  • 中文说明版:适合先看整体 benchmark 版图。
  • 结构化表格版:适合后续导出 CSV、JSON 或继续人工补全。
  • 如果用户没有指定,默认同时给出简短说明和结构化表格。

输出规则

  • 字段尽量包括:
    • 论文标题
    • 任务
    • 数据集
    • 指标
    • baseline
    • 是否报告 SOTA
    • 是否开源代码
    • 是否开源数据
    • 评测设置说明
    • 备注
  • 对“是否报告 SOTA”这类字段,如果论文只是声称优于 baseline,但未明确写“state of the art”,优先写“未明确”。

证据与表述约束

  • 不要根据常识自动补数据集、指标或开源状态。
  • 如果只有摘要,明确说明当前抽取只是初步结果。
  • 如果字段存在歧义,在备注里写出歧义来源。

何时读引用文件

  • 始终读取 references/extraction_schema.md。
  • 在组织表格输出时读取 references/comparison_table_template.md。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Benchmark Extractor AI skill do?

extract structured benchmark information from academic papers when the input includes multiple pdfs, abstracts, or links and the user needs chinese notes or table-ready fields for tasks, datasets, metrics, baselines, sota claims, code release, or evaluation settings.

Why use Benchmark Extractor on TypingMind?

Because you install it once and use it with any model. Benchmark Extractor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Benchmark Extractor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chtc66/academic-skills/tree/main/benchmark-extractor. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Benchmark Extractor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Benchmark Extractor?

As many as you like. As long as a model supports skills, you can use Benchmark Extractor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Benchmark Extractor AI skill free?

Yes. It is published on GitHub by chtc66 under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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