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Openalex

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wanshuiyin
openalex

Search academic papers via OpenAlex API for open citation data, institutional affiliations, and funding information. Use when user says "openalex search", "search openalex", "open citation graph", or wants comprehensive academic metadata beyond arXiv/Semantic Scholar.

Overview

Publisherwanshuiyin
RepositoryAuto-claude-code-research-in-sleep
Skill nameopenalex
Stars
16.3K
Forks
1.4K
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 wanshuiyin on GitHub. Read the source before you install it.

Installation

Install the Openalex 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.
  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/wanshuiyin/Auto-claude-code-research-in-sleep.git /tmp/Auto-claude-code-research-in-sleep
mkdir -p .claude/skills
cp -r /tmp/Auto-claude-code-research-in-sleep/skills/openalex .claude/skills/openalex
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Openalex 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 Openalex 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 Openalex 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.

OpenAlex Academic Search

Search query: $ARGUMENTS

Role & Positioning

This skill uses OpenAlex as a comprehensive open academic graph source:

SkillSourceBest for
/arxivarXiv APILatest preprints, cutting-edge unrefereed work
/semantic-scholarSemantic Scholar APIPublished venue papers (IEEE, ACM, Springer) with citation counts
/openalexOpenAlex APIOpen citation graph, institutional affiliations, funding data, comprehensive metadata
/deepxivDeepXiv CLILayered reading: search, brief, section map, section reads
/exa-searchExa APIBroad web search: blogs, docs, news, companies, research papers
/gemini-searchGemini MCP / CLIAI-powered broad literature discovery

Use OpenAlex when you want:

  • Open citation data — fully open citation graph (no API key required for basic use)
  • Institutional affiliations — author institutions and collaborations
  • Funding information — NSF, NIH, and other funding sources
  • Comprehensive metadata — topics, keywords, abstract, open access status
  • Cross-database coverage — indexes 250M+ works from multiple sources

Constants

  • MAX_RESULTS = 10 — Default number of results. Override with — max: 20.
  • DEFAULT_SORT = relevance — Sort by relevance. Override with — sort: citations or — sort: date.
  • OPENALEX_FETCHER — canonical name openalex_fetch.py, resolved per shared-references/integration-contract.md §2 (Policy D1 — standalone /openalex has no documented inline fallback, so unresolved helper terminates with an explicit error).

Overrides (append to arguments):

  • /openalex "topic" — max: 20 — return up to 20 results
  • /openalex "topic" — year: 2023- — papers from 2023 onward
  • /openalex "topic" — year: 2020-2023 — papers from 2020 to 2023
  • /openalex "topic" — type: article — only journal articles
  • /openalex "topic" — type: preprint — only preprints
  • /openalex "topic" — open-access — only open access papers
  • /openalex "topic" — min-citations: 50 — minimum 50 citations
  • /openalex "topic" — sort: citations — sort by citation count (descending)
  • /openalex "topic" — sort: date — sort by publication date (newest first)

Setup

Prerequisites

  1. Python 3.7+ with requests library:

    bash
    pip install requests
  2. Optional: API keys — Create .claude/.env in project root:

    bash
    # Copy from template
    cp .claude/.env.example .claude/.env
    
    # Edit and add your keys
    # .claude/.env
    OPENALEX_API_KEY=your-key-here
    OPENALEX_EMAIL=your-email@example.com

    Claude Code automatically loads .claude/.env as environment variables.

  3. Get API keys (optional but recommended):

    • OpenAlex API key: Free tier $1/day (10,000 list calls, 1,000 search calls) from openalex.org
    • Email for polite pool: Faster response times (no registration needed)

Verify Setup

bash
python3 "$OPENALEX_FETCHER" search "machine learning" --max 3

(Resolve $OPENALEX_FETCHER via the canonical chain first — see Step 2 below.)

Workflow

Step 1: Parse Arguments

Parse $ARGUMENTS for:

  • query: The research topic (required)
  • max: Override MAX_RESULTS
  • year: Publication year filter (e.g., 2023-, 2020-2023)
  • type: Work type filter (article, preprint, book, book-chapter, dataset, dissertation)
  • open-access: Only include open access papers
  • min-citations: Minimum citation count threshold
  • sort: Sort order (relevance, citations, date)

Step 2: Locate Script

Resolve $OPENALEX_FETCHER via the canonical strict-safe chain (see shared-references/integration-contract.md §2). Policy D1: there is no native inline fallback for OpenAlex (retrieval requires the requests SDK + optional API key — the fetcher script encapsulates pagination, throttling, and per-source parameters), so unresolved helper terminates with explicit remediation.

bash
cd "$(git rev-parse --show-toplevel 2>/dev/null || pwd)" || exit 1
if [ -z "${ARIS_REPO:-}" ] && [ -f .aris/installed-skills.txt ]; then
    ARIS_REPO=$(awk -F'\t' '$1=="repo_root"{print $2; exit}' .aris/installed-skills.txt 2>/dev/null) || true
fi
if [ -z "${ARIS_REPO:-}" ] && [ -f "$HOME/.aris/repo" ]; then
    ARIS_REPO=$(cat "$HOME/.aris/repo" 2>/dev/null) || true
fi
OPENALEX_FETCHER=".aris/tools/openalex_fetch.py"
[ -f "$OPENALEX_FETCHER" ] || OPENALEX_FETCHER="tools/openalex_fetch.py"
[ -f "$OPENALEX_FETCHER" ] || { [ -n "${ARIS_REPO:-}" ] && OPENALEX_FETCHER="$ARIS_REPO/tools/openalex_fetch.py"; }
[ -f "$OPENALEX_FETCHER" ] || {
  echo "ERROR: openalex_fetch.py not resolved at .aris/tools/, tools/, \$ARIS_REPO/tools/, or via ~/.aris/repo." >&2
  echo "       Fix: rerun bash tools/install_aris.sh or smart_update.sh (refreshes ~/.aris/repo), export ARIS_REPO, or copy the helper to tools/." >&2
  echo "       Also ensure 'requests' is installed: pip install requests" >&2
  exit 1
}

Step 3: Execute Search

Basic search:

bash
python3 "$OPENALEX_FETCHER" search "QUERY" --max 10

With filters:

bash
python3 "$OPENALEX_FETCHER" search "QUERY" --max 10 \
  --year 2023- \
  --type article \
  --open-access \
  --min-citations 20 \
  --sort citations

Get specific work by DOI:

bash
python3 "$OPENALEX_FETCHER" work "10.1109/TWC.2024.1234567"

Get specific work by OpenAlex ID:

bash
python3 "$OPENALEX_FETCHER" work "W2741809807"

Step 4: Parse Results

The script returns structured JSON with:

  • title: Paper title
  • authors: List of author names
  • publication_year: Year published
  • venue: Journal/conference name
  • venue_type: Type of venue (journal, repository, conference, etc.)
  • cited_by_count: Number of citations
  • is_oa: Boolean for open access status
  • oa_status: Open access type (gold, green, bronze, hybrid, closed)
  • oa_url: Direct PDF link if available
  • doi: DOI identifier
  • openalex_id: OpenAlex work ID
  • abstract: Full abstract text
  • topics: Top 3 research topics
  • keywords: Top 5 keywords
  • type: Work type (article, preprint, etc.)

Step 5: Present Results

Format results as a structured table:

| # | Title | Venue | Year | Citations | OA | Summary |
|---|-------|-------|------|-----------|----|---------| 
| 1 | ... | IEEE TWC | 2024 | 156 | ✓ | ... |
| 2 | ... | NeurIPS | 2023 | 89 | ✓ | ... |

For each paper, also show:

  • DOI: Canonical identifier
  • OpenAlex ID: For cross-reference
  • Open Access: Status (gold/green/bronze/hybrid/closed) and PDF link
  • Topics: Top research topics
  • Abstract: First 200 characters or full text

Step 6: Offer Follow-up

After presenting results, suggest:

text
/semantic-scholar "DOI:..."     — get S2 citation context and related papers
/arxiv "arXiv:XXXX.XXXXX"      — fetch arXiv preprint if available
/research-lit "topic" — sources: openalex, semantic-scholar  — combined multi-source review
/novelty-check "idea"          — verify novelty against literature

Key Rules

  • OpenAlex is fully open — no API key required for basic use, but recommended for higher rate limits
  • Comprehensive metadata — OpenAlex provides richer metadata than most sources (institutions, funding, topics)
  • Citation data is open — unlike Semantic Scholar, all citation data is freely accessible
  • Rate limits: Without API key, very limited (~$0.01/day). With free API key: 10,000 list calls/day, 1,000 search calls/day.
  • Polite pool: Set OPENALEX_EMAIL environment variable for faster response times
  • Cross-reference with other sources: OpenAlex indexes papers from arXiv, PubMed, Crossref, etc. — use DOI/arXiv ID to cross-reference
  • If OpenAlex API is unreachable or rate-limited, suggest using /semantic-scholar, /arxiv, or /research-lit "topic" — sources: web as alternatives.

OpenAlex vs Other Sources

FeatureOpenAlexSemantic ScholararXiv
Coverage250M+ works200M+ papers2.4M+ preprints
Citation dataFully openPartially openNone
Institutions✓ Full affiliations✓ Limited
Funding✓ NSF, NIH, etc.
Open access✓ Full OA status✓ PDF links✓ All papers
API keyOptional (free)Optional (free)Not required
Rate limits1,000 searches/day (free key)Unknown1 req/3s
Abstract✓ Full text✓ TLDR✓ Full text
Best forComprehensive metadata, institutions, fundingCitation counts, venue infoLatest preprints

When to use OpenAlex over S2:

  • Need institutional affiliation data
  • Need funding information
  • Want fully open citation graph
  • Need comprehensive topic/keyword metadata
  • Working with non-CS fields (OpenAlex covers all disciplines)

When to use S2 over OpenAlex:

  • Need real-time citation counts (S2 updates faster)
  • Need "highly influential citations" metric
  • Need paper recommendations
  • CS/AI-focused research (S2 has better CS coverage)

Frequently asked questions

What does the Openalex AI skill do?

Search academic papers via OpenAlex API for open citation data, institutional affiliations, and funding information. Use when user says "openalex search", "search openalex", "open citation graph", or wants comprehensive academic metadata beyond arXiv/Semantic Scholar.

Why use Openalex on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep/tree/main/skills/openalex. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Openalex?

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 Openalex?

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

Is the Openalex AI skill free?

Yes. It is published on GitHub by wanshuiyin 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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