Nature Academic Search

CommunityPopular

Yuan1z0825

nature-academic-search

Search literature across sources, verify or manage citations, and build MeSH strategies or citation-impact audits. Use for 文献检索、引文核对、参考文献管理、严格他引 and evidence-backed citer profiles; not for translating a paper or drafting manuscript prose.

Overview

Publisher	Yuan1z0825
Repository	`nature-skills`
Skill name	`nature-academic-search`
Stars	42.8K
Forks	2.3K
Bundled files	45
License	Apache-2.0
Links	Skill source Repository

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.
45 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by Yuan1z0825 on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
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.

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/Yuan1z0825/nature-skills.git /tmp/nature-skills
mkdir -p .claude/skills
cp -r /tmp/nature-skills/skills/nature-academic-search .claude/skills/nature-academic-search

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nature Academic Search 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 Nature Academic Search 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 Nature Academic Search 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.

Academic Search — Router

Routing protocol

For local citation-file conversion with complete supplied records, load references/workflows/wf4-citation-file-mgmt.md and its format reference directly. Source lookup, API setup, and network preflight apply only when retrieval or verification is needed; do not require them for a requested offline conversion.

For a new task, load the core and matching resources below. Reuse already loaded guidance on follow-ups; load more only when the task needs it.

1. Load the manifest and the core layer

Read manifest.yaml. It declares the workflow axis, the allowed values, and the file paths each value maps to.

Also read every file listed under always_load:

static/core/tools.md — the MCP tool inventory (core search, extended search, PubMed utilities) and the shared-module map.
static/core/routing-and-ops.md — the T1→T2→T3 source routing quick guide, environment setup, error handling, and limitations.

2. Detect the workflow

Map the user's need to one or more workflow values:

multi-source-search — find literature across sources.
citation-verification — verify citations extracted from a document.
mesh-strategy — build a MeSH/PubMed search strategy.
citation-file-mgmt — convert/manage .nbib/.ris/.bib files.
reference-mgmt — BibTeX, related-article discovery, ID conversion.
strict-other-citation-impact-audit — determine strict independent other-citations, build article-level citation metric tables, identify high-profile citers (academy members, presidents/deans, talent-award holders, fellows, field leaders), and extract how they cited the target paper.

A combined request (for example search then export) may need more than one. State the detected workflow(s) in one short line before proceeding.

3. Load the matching workflow fragment(s)

Read the file mapped for each detected workflow (under references/workflows/). Do not read every workflow. Each workflow file links to the shared modules it needs.

4. Run the workflow using the loaded material

Apply the loaded material in this order:

Core tools and routing (core/tools.md, core/routing-and-ops.md) — which MCP tool for which need, and the T1→T2→T3 fallback chain for source retrieval.
The workflow fragment — its specific steps.
Shared modules and scripts on demand (dedup, citation parser, search strategy, RIS/BibTeX format, format converter).

Report specific tool failures and continue with remaining tools; broaden terms when there are no results; fall back to manual generation from MCP-fetched metadata if a script fails twice.

5. Reach for references only when needed

The files under references/ (and scripts/) are deep references, not defaults. Open them on demand per the references.on_demand table in the manifest — for example references/source-tiers.md for the full reliability classification, references/dedup-engine.md / references/citation-parser.md / references/search-strategy.md / references/ris-bibtex-format.md for the shared modules, and scripts/academic_search.py (no-MCP fallback discovery search) / scripts/format-converter.py / scripts/preflight.py for the tooling.

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 Nature Academic Search AI skill do?

Why use Nature Academic Search on TypingMind?

Because you install it once and use it with any model. Nature Academic Search 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 Nature Academic Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Yuan1z0825/nature-skills/tree/main/skills/nature-academic-search. 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 Nature Academic Search?

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 Nature Academic Search?

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

Is the Nature Academic Search AI skill free?

Yes. It is published on GitHub by Yuan1z0825 under the Apache-2.0 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Academic Search — Router

Routing protocol

1. Load the manifest and the core layer

2. Detect the workflow

3. Load the matching workflow fragment(s)

4. Run the workflow using the loaded material

5. Reach for references only when needed

Bundled files

Frequently asked questions

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