Crossref Search logo

Crossref Search

Community
beita6969
crossref-search

Academic metadata search via CrossRef API. Use when: user needs DOI resolution, citation counts, journal metadata, or publisher info. NOT for: full-text access or downloading papers.

Overview

Publisherbeita6969
RepositoryScienceClaw
Skill namecrossref-search
Stars
950
Forks
106
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by beita6969 on GitHub. Read the source before you install it.

Installation

Install the Crossref Search 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/beita6969/ScienceClaw/tree/main/skills/crossref-search
  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/beita6969/ScienceClaw.git /tmp/ScienceClaw
mkdir -p .claude/skills
cp -r /tmp/ScienceClaw/skills/crossref-search .claude/skills/crossref-search
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Crossref 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 Crossref 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 Crossref 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.

CrossRef Search

Academic metadata search and DOI resolution via the public CrossRef REST API.

When to Use

  • Resolving a DOI to get full citation metadata
  • Searching for papers by title, author, or keywords
  • Looking up journal ISSN metadata or publisher info
  • Finding citation counts and reference lists
  • Retrieving funder information for grants/awards

When NOT to Use

  • Full-text access or downloading papers (use publisher sites)
  • Preprint search (use arxiv-search)
  • Biomedical literature (use pubmed-search)
  • Author profile pages or h-index (use openalex-search)

DOI Resolution

bash
curl -s "https://api.crossref.org/works/10.1038/nature12373" | python3 -c "
import sys, json
data = json.load(sys.stdin)['message']
title = data.get('title', [''])[0]
authors = ', '.join(f\"{a.get('given','')} {a.get('family','')}\" for a in data.get('author', []))
journal = data.get('container-title', [''])[0]
cited = data.get('is-referenced-by-count', 0)
print(f'Title: {title}')
print(f'Authors: {authors}')
print(f'Journal: {journal} | Citations: {cited}')
"

Works Search

bash
# Search by query terms
curl -s "https://api.crossref.org/works?query=machine+learning+protein+folding&rows=5&mailto=user@example.com" | python3 -c "
import sys, json
data = json.load(sys.stdin)['message']
for item in data['items']:
    title = item.get('title', [''])[0]
    doi = item.get('DOI', '')
    cited = item.get('is-referenced-by-count', 0)
    print(f'{title}')
    print(f'  DOI: {doi} | Citations: {cited}')
"

# Filter by date, type, and sort by citations
curl -s "https://api.crossref.org/works?query=CRISPR&filter=from-pub-date:2023-01-01,type:journal-article&rows=10&sort=is-referenced-by-count&order=desc&mailto=user@example.com"

# Search by author
curl -s "https://api.crossref.org/works?query.author=Jennifer+Doudna&rows=10&sort=published&order=desc&mailto=user@example.com"

Journal Lookup

bash
# Search journals by title
curl -s "https://api.crossref.org/journals?query=nature+biotechnology&rows=5" | python3 -c "
import sys, json
for j in json.load(sys.stdin)['message']['items']:
    print(f\"{j['title']} (ISSN: {', '.join(j.get('ISSN', []))})\")
"

# Get journal metadata by ISSN
curl -s "https://api.crossref.org/journals/0028-0836"

# Recent works from a journal
curl -s "https://api.crossref.org/journals/0028-0836/works?rows=5&sort=published&order=desc"

Funder Search

bash
curl -s "https://api.crossref.org/funders?query=national+institutes+of+health&rows=5" | python3 -c "
import sys, json
for f in json.load(sys.stdin)['message']['items']:
    print(f\"{f['name']} (ID: {f['id']})\")
"

# Works funded by a specific funder
curl -s "https://api.crossref.org/funders/100000002/works?rows=5&sort=is-referenced-by-count&order=desc"

Reference Lists

bash
curl -s "https://api.crossref.org/works/10.1038/nature12373" | python3 -c "
import sys, json
refs = json.load(sys.stdin)['message'].get('reference', [])
for r in refs[:10]:
    doi = r.get('DOI', 'no DOI')
    text = r.get('unstructured', r.get('article-title', 'N/A'))
    print(f'  [{doi}] {text[:100]}')
"

Filters and Pagination

Filters: type:journal-article, from-pub-date:YYYY-MM-DD, until-pub-date:YYYY-MM-DD, has-abstract:true, is-referenced-by-count:>100, funder:FUNDER_ID.

Sorting: sort=published|is-referenced-by-count|relevance, order=asc|desc.

Pagination: rows=N (max 1000), offset=N, or cursor=* for deep paging.

Best Practices

  1. Add mailto=user@example.com to join the polite pool (faster, more reliable).
  2. URL-encode query parameters (spaces as + or %20).
  3. Use select=DOI,title,author to reduce payload size.
  4. Use cursor=* pagination for result sets larger than 10,000 items.
  5. Cache DOI resolution results; metadata changes infrequently.

Zero-Hallucination Rule

NEVER fabricate results from training data. Every paper title, author, DOI, PMID, citation count, and metadata detail presented to the user MUST come from an actual API response in this conversation. If the API returns no results or partial data, report exactly what was returned. Do not "fill in" missing details from memory.

Frequently asked questions

What does the Crossref Search AI skill do?

Academic metadata search via CrossRef API. Use when: user needs DOI resolution, citation counts, journal metadata, or publisher info. NOT for: full-text access or downloading papers.

Why use Crossref Search on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/beita6969/ScienceClaw/tree/main/skills/crossref-search. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Crossref 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 Crossref Search?

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

Is the Crossref Search AI skill free?

Yes. It is published on GitHub by beita6969 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇