| name | semantic-scholar-search |
| description | Search Semantic Scholar for papers with rich metadata — citation counts, AI-generated TLDRs, influence scores, citation graphs. Use for finding established research, tracing citation chains, understanding paper impact and field structure. |
| keywords | ["semantic-scholar","citations","papers","research","tldr","impact","academic","literature"] |
Semantic Scholar Search
Search 200M+ papers with rich metadata. No API key required (100 req/sec).
Two modes:
- Search — find papers by query
- Detail — get deep info on a specific paper (references, citations)
Usage
SCRIPT=$(find ~/.claude/plugins -path "*/ai-frontier/skills/semantic-scholar-search/scripts/search.mjs" 2>/dev/null | head -1)
node "$SCRIPT" "your query" [maxResults] [--year=2024] [--open-access]
node "$SCRIPT" --detail=<s2PaperId|DOI|ArXiv:id>
Arguments
| Arg | Default | Description |
|---|
| query | (required) | Natural language search query |
| maxResults | 10 | Number of results (1-100) |
| --year=YYYY | none | Only papers from YYYY onward |
| --open-access | false | Only papers with free PDF |
| --detail=ID | n/a | Get full details for a specific paper |
When to Use
- Finding papers with citation context (how influential, who cites it)
- Getting AI-generated TLDRs for quick understanding
- Tracing citation chains (what a paper builds on, what builds on it)
- Filtering by year to find recent work
- Finding open access PDFs for deeper reading
Key Fields
results[].tldr — AI-generated one-sentence summary (from S2)results[].citations — total citation countresults[].source_specific.influential_citations — citations from influential papersresults[].source_specific.fields_of_study — e.g., ["Computer Science", "Mathematics"]- Detail mode:
paper.top_references and paper.recent_citations for citation graph
Examples
node "$SCRIPT" "reinforcement learning from human feedback" 10
node "$SCRIPT" "mixture of experts scaling" 15 --year=2025
node "$SCRIPT" --detail=ArXiv:2210.11416
