Scientific Taxonomy logo

Scientific Taxonomy

Organization
AMinerOrg
scientific-taxonomy

Build a hierarchical Markdown taxonomy from scientific-paper titles and complete abstracts. Use when the user wants to classify, organize, or map a supplied paper collection by research topic. If titles or complete abstracts are missing, first obtain them through an available local PDF-extraction capability or an AMiner search skill/API, then generate the taxonomy. Do not use this skill to implement PDF parsing, search for unrelated papers, create folders, or create symbolic links.

Overview

PublisherAMinerOrg
Repositoryaminer-open-skill
Skill namescientific-taxonomy
Stars
66
Forks
19
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Scientific Taxonomy 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/AMinerOrg/aminer-open-skill/tree/main/skills/scientific-taxonomy
  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/AMinerOrg/aminer-open-skill.git /tmp/aminer-open-skill
mkdir -p .claude/skills
cp -r /tmp/aminer-open-skill/skills/scientific-taxonomy .claude/skills/scientific-taxonomy
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scientific Taxonomy 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 Scientific Taxonomy 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 Scientific Taxonomy 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.

Scientific Taxonomy

Organize a supplied collection of scientific papers into one coherent topic hierarchy and write the result as Markdown.

Input readiness

Build the taxonomy from these fields only:

  • paper_id: optional stable identifier used internally for deduplication and AMiner URL construction. Do not display it in the Markdown paper list.
  • title: required for every paper.
  • abstract: the complete abstract, required when it can be retrieved. An abstract_slice, snippet, or truncated preview is not a complete abstract.
  • venue: conference or journal name, required when it can be retrieved.
  • year: publication year, required when it can be retrieved.
  • aminer_url: preferred paper link. If absent and a valid AMiner paper_id exists, construct https://www.aminer.cn/pub/{paper_id}.

Before classifying, check whether the supplied information is sufficient:

  1. When the user supplies local PDFs instead of titles and abstracts, invoke an available local PDF-extraction tool or Skill first. Consume its structured title and abstract output; do not implement PDF parsing in this Skill.
  2. Treat abstract_slice, search snippets, and visibly truncated abstracts as insufficient taxonomy input. For AMiner search results, invoke aminer-academic-search and use paper_detail with each selected AMiner paper ID to retrieve the complete abstract before classifying. Follow that Skill's authorization, pricing, and high-cost confirmation rules.
  3. When a paper is supplied only as a DOI, arXiv ID, URL, or title, use an available AMiner Skill or AMiner API to resolve the paper and retrieve its complete abstract. Prefer a free lookup route for identity resolution, then use full paper details when the free result contains only an abstract slice.
  4. Enrich only papers already supplied by the user. Do not add related papers unless the user explicitly asks.
  5. Never invent or expand an abstract. If full-abstract retrieval is unavailable or returns no abstract, classify from the verified title and available slice, then record the affected paper titles in a short metadata note without visibly displaying paper IDs.

Taxonomy method

  1. Infer a concise overall topic for the complete collection.
  2. Create broad, reusable top-level research areas, then subdivide only where a meaningful distinction exists.
  3. Use one semantic dimension within each sibling group, such as research problem, method family, application domain, or data type. Do not mix dimensions arbitrarily at the same level.
  4. Consider the full collection when revising any level. For large inputs, read papers in batches but maintain one global taxonomy; do not build independent batch trees and merge them afterward.
  5. Assign every paper to exactly one leaf. Do not omit, duplicate, or invent papers.
  6. Prefer concise category names in the user's language. Avoid copied paper titles, numbered clusters, and vague labels such as “Other” or “Miscellaneous”.
  7. Stop splitting when a category is already coherent or further division would create weak or artificial groups. Five heading levels below the document title is a safety limit, not a target.

Output

Write one file named taxonomy.md in the user's requested location or the current workspace. If file writing is unavailable, return the same Markdown directly. Do not create JSON, directories, copied papers, or symbolic links.

For every paper entry, display metadata in this order:

  1. Conference or journal abbreviation.
  2. Publication year.
  3. Exact paper title as a Markdown hyperlink to its AMiner page.

Use widely recognized venue abbreviations whenever available, such as ACL, ACL SRW, EMNLP, NAACL, COLING, KDD, CIKM, ICML, ICLR, NeurIPS, TACL, IEEE BigData, and ICDMW. Remove redundant words such as Proceedings of and avoid repeating a year already embedded in the venue name. For journals, use a recognized short title where one exists. Do not invent an uncertain abbreviation; use a concise official venue name instead. If the venue is unavailable after retrieval, write Venue 未收录. If the year is unavailable, write 年份未收录.

Do not render paper_id as visible text. The identifier may appear only inside the AMiner hyperlink URL. If no verified AMiner URL can be formed, show the exact title as plain text rather than fabricating a link.

Use this structure:

markdown
# <Collection topic>

## <Broad research area>

### <Specific topic>

- ACL 2024 — [Exact paper title](https://www.aminer.cn/pub/<paper_id>)
- TACL 2025 — [Another exact paper title](https://www.aminer.cn/pub/<paper_id>)

List papers only under leaf headings. Preserve titles exactly and use paper IDs internally to verify identity, uniqueness, and links. Order sibling categories coherently and keep the original paper order within each leaf unless the user asks for another order.

Use complete abstracts only as classification evidence. By default, do not reproduce abstract text in taxonomy.md; show only taxonomy headings and paper entries formatted as venue abbreviation year — linked exact title.

If metadata remains missing after attempted enrichment, append one blockquote after the taxonomy:

markdown
> Metadata note: Complete abstracts were unavailable for “Paper title A” and “Paper title B” after retrieval was attempted; these papers were classified from verified titles and available abstract slices.

Before finishing, verify that every input paper appears once and only once, every listed paper came from the supplied collection, every title links to the correct AMiner page when a verified link is available, no paper ID is visibly rendered, venue names are shortened where reliable, and every heading accurately summarizes its descendant papers.

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 Scientific Taxonomy AI skill do?

Build a hierarchical Markdown taxonomy from scientific-paper titles and complete abstracts. Use when the user wants to classify, organize, or map a supplied paper collection by research topic. If titles or complete abstracts are missing, first obtain them through an available local PDF-extraction capability or an AMiner search skill/API, then generate the taxonomy. Do not use this skill to implement PDF parsing, search for unrelated papers, create folders, or create symbolic links.

Why use Scientific Taxonomy on TypingMind?

Because you install it once and use it with any model. Scientific Taxonomy 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 Scientific Taxonomy in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AMinerOrg/aminer-open-skill/tree/main/skills/scientific-taxonomy. 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 Scientific Taxonomy?

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 Scientific Taxonomy?

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

Is the Scientific Taxonomy AI skill free?

Yes. It is published on GitHub by AMinerOrg 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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