Openalex

OpenAlex Academic Search

Search query: $ARGUMENTS

Role & Positioning

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

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.
• FETCH_SCRIPT — tools/openalex_fetch.py relative to the project root.

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:

   pip install requests
   

2. Optional: API keys — Create .claude/.env in project root:

   # 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

python3 tools/openalex_fetch.py search "machine learning" --max 3

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

SCRIPT=$(find tools/ -name "openalex_fetch.py" 2>/dev/null | head -1)
[ -z "$SCRIPT" ] && SCRIPT=$(find ~/.claude/skills/openalex/ -name "openalex_fetch.py" 2>/dev/null | head -1)

If not found, tell the user:

openalex_fetch.py not found. Make sure tools/openalex_fetch.py exists and requests is installed:
  pip install requests

Step 3: Execute Search

Basic search:

python3 "$SCRIPT" search "QUERY" --max 10

With filters:

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

Get specific work by DOI:

python3 "$SCRIPT" work "10.1109/TWC.2024.1234567"

Get specific work by OpenAlex ID:

python3 "$SCRIPT" 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:

/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

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)