Semantic Scholar Skill

Why use it

Key features

Search: relevance, bulk, snippets, title-match, author
Resolve by DOI / arXiv / PMID / PMCID / MAG / ACL / SHA / URL / CorpusId
Citation graph traversal (forward + backward, paginated)
Recommendations: single-seed similarity or multi-seed positive/negative
Batch: up to 500 papers / 1000 authors per request
Exports: BibTeX, Markdown, JSON

Live Demo

What it looks like in practice

semanticscholar-skill.replay ▶ ready

0/0

Install

Author repository · Claude Skills documentation

Use Cases

Real-world ways to use Semantic Scholar Skill

Scaffold a literature review around a seed paper

👤 Grad students, researchers starting a review ⏱ ~45 min intermediate

When to use: You have one strong anchor paper and want to build the surrounding literature.

Flow

Resolve the seed

Resolve 10.1038/s41586-020-2649-2 and get its full metadata.✓ Copied

→ Full paper object with abstract + fields of study
Pull references + citations

Get the seed's references and top 50 citing papers sorted by influence.✓ Copied

→ Two ranked lists with venues + years
Export Markdown with BibTeX keys

Export the combined set as Markdown with BibTeX entries I can drop into Zotero.✓ Copied

→ Markdown with inline citekeys + bibtex block

Outcome: A starter lit-review skeleton grounded in real citation data.

Pitfalls

Assuming the most-cited paper is the most-relevant paper — Skim abstracts; use recommendations with positive/negative seeds to refine

Combine with: paper-fetch-skill · zotero

Profile an author for an interview, talk, or tenure review

👤 Program chairs, journalists, hiring committees ⏱ ~15 min beginner

When to use: You need a fast, accurate snapshot of someone's publication record.

Flow

Find the author

Search authors 'Yann LeCun' and pick the NYU/Meta one.✓ Copied

→ Author ID + affiliations + h-index
Get publications

List their top 50 publications by citation count; group by fields of study.✓ Copied

→ Grouped list with counts; venue + year per paper

Outcome: A defensible author profile with real data.

Pitfalls

Disambiguation errors — common names match multiple people — Always inspect affiliation and co-author signal before committing

Get recommendations from what you've liked and disliked

👤 Researchers building a reading list ⏱ ~20 min intermediate

When to use: You have 5 papers you liked and 2 you didn't; find more in that space.

Flow

Provide seeds

Positive seeds: [DOIs]. Negative seeds: [DOIs]. Recommend 20 papers.✓ Copied

→ Ranked recs biased toward positives, away from negatives
Filter

Only venues with year ≥ 2022 and open access = true.✓ Copied

→ Filtered recs include only OA post-2022 work

Outcome: A tailored list you'd actually want to read.

Batch-fetch metadata for a long reading list

👤 Anyone importing a bibliography ⏱ ~25 min intermediate

When to use: You have 100+ DOIs and need metadata in one shot.

Flow

Submit the batch

Batch-fetch metadata for these 200 DOIs; export BibTeX.✓ Copied

→ Single response covering all; BibTeX stream follows
Handle misses

For IDs that didn't resolve, try match_title as fallback.✓ Copied

→ Fallback search closes most gaps

Outcome: Clean, consistent bibliography ready for Zotero.

Pitfalls

Exceeding 500 papers per batch call — Chunk at 500; skill handles rate limits between chunks

Combine with: zotero

Combinations

Pair with other MCPs for X10 leverage

semanticscholar-skill + paper-fetch-skill

Use Semantic Scholar to discover, then fetch the PDFs

Search Semantic Scholar; fetch open-access PDFs for the top 10 hits.✓ Copied

semanticscholar-skill + zotero

Import via Zotero for local library management

Export BibTeX; import into Zotero and tag the collection.✓ Copied

semanticscholar-skill + arxiv

Cross-reference with arXiv metadata

For each arXiv ID in my list, enrich with Semantic Scholar citation counts.✓ Copied

Tools

What this MCP exposes

Tool	Inputs	When to call	Cost
search_relevance	query	Default paper search	1 request
search_bulk	query + filters	Bulk results for downstream filtering	1 request
search_snippets	query	Find papers by phrases in the text	1 request
match_title	title	Resolve a paper by an imprecise title	1 request
get_paper	ID (DOI/arXiv/...)	When you have an identifier	1 request
get_citations / get_references	paper id, limit	Traverse the citation graph	1 request
find_similar / recommend	seed(s) (pos + neg)	Lookalike and refined recommendations	1 request
batch_papers / batch_authors	array of IDs	Large reading lists	1 request
search_authors	name	Profile authors	1 request
export_bibtex / export_markdown / export_json	papers[]	Final export step	0

Cost & Limits

What this costs to run

API quota: Semantic Scholar public API — rate limited; skill enforces 1.1s gap and exponential backoff
Tokens per call: Metadata is small; abstracts and snippets larger
Monetary: Free — Semantic Scholar is public
Tip: Batch whenever possible — single batch call beats hundreds of get_paper calls.

Security

Permissions, secrets, blast radius

Credential storage: No credentials required for public endpoints. API key optional for higher limits — keep it in env, not config.

Data egress: Queries and results go to api.semanticscholar.org.

Citation counts are snapshots — don't compare counts taken months apart as evidence of trends without noting the dates.

Troubleshooting

Common errors and fixes

429 Too Many Requests

Skill already handles this; if persistent, slow your batch pace or request an API key.

Verify: Inspect response headers for x-rate-limit-*

Paper not found by DOI

Fall back to match_title with the paper's title; ~10% of papers lack DOI coverage.

Verify: match_title + grep output

Author disambiguation wrong

Filter by affiliation or co-author before committing; Semantic Scholar occasionally merges distinct people.

Verify: Inspect the author's publication list for coherence

Alternatives