semanticscholar-skill

Semantic Scholar Search Workflow

Search academic papers via the Semantic Scholar API using a structured 4-phase workflow.

Critical rule: NEVER make multiple sequential Bash calls for API requests. Always write ONE Python script that runs all searches, then execute it once. All rate limiting is handled inside s2.py automatically.

Phase 1: Understand & Plan

Parse the user's intent and choose a search strategy:

Decision Tree

Default to search_bulk(). Per Semantic Scholar's own docs, bulk search is preferred over relevance search for most cases because relevance search is more resource-intensive. Use search_relevance() only when you need TLDR fields or author/citation details inline.

User wants...	Strategy	Function
Broad topic exploration	Bulk search (preferred)	search_bulk() with build_bool_query()
Need TLDR / inline author details	Relevance search	search_relevance()
Precise technical terms, exact phrases	Bulk search with boolean operators	search_bulk() with build_bool_query()
Specific passages or methods	Snippet search	search_snippets()
Known paper by title	Title match	match_title()
Known paper by DOI/PMID/ArXiv	Direct lookup	get_paper()
Papers citing a known work	Citation traversal	get_citations()
Related to one paper	Single-seed recommendations	find_similar()
Related to multiple papers	Multi-seed recommendations	recommend()
Find a researcher	Author search	search_authors()
Researcher's profile	Author details	get_author()
Researcher's publications	Author papers	get_author_papers()

Query Construction Rules

• Ambiguous terms (e.g., "stem cells" could mean mesenchymal or stem-like T cells): Use build_bool_query() with exact phrases and exclusions
  • Example: build_bool_query(phrases=["stem-like T cells"], required=["CD4", "TCF7"], excluded=["mesenchymal", "hematopoietic stem cell"])
• Multi-context queries (e.g., "topic X in cancer AND autoimmunity"): Plan separate searches, deduplicate with deduplicate()
• Broad topics: Use search_relevance() with filters (year, venue, fieldsOfStudy, minCitationCount)

Plan Filters

Filter	Use when
year="2020-"	Recent work only
publication_date="2024-01-01:2024-06-30"	Precise date range (YYYY-MM-DD)
fields_of_study="Medicine"	Restrict to domain
min_citations=10	Only established papers
pub_types="Review"	Find reviews/meta-analyses
pub_types="ClinicalTrial"	Clinical trials only
open_access=True	Only open access papers

Checkpoint: Before proceeding, verify: (1) search strategy matches user intent, (2) filters are appropriate, (3) query is specific enough to avoid irrelevant results.

Phase 2: Execute Search

Write ONE Python script that begins with the standard prelude below, then runs all searches:

# --- Standard prelude (use in every script) ---
import sys, os, glob
_candidates = [
    os.path.expanduser("~/.claude/skills/semanticscholar-skill"),
    os.path.expanduser("~/.openclaw/skills/semanticscholar-skill"),
    *glob.glob(os.path.expanduser("~/.claude/plugins/**/semanticscholar-skill"), recursive=True),
    *glob.glob(os.path.expanduser("~/.codex/skills/semanticscholar-skill")),
    ".",
]
SKILL_DIR = next((p for p in _candidates if os.path.isfile(os.path.join(p, "s2.py"))), None)
if SKILL_DIR is None:
    raise RuntimeError("Cannot locate semanticscholar-skill (s2.py not found)")
sys.path.insert(0, SKILL_DIR)
from s2 import *
# --- end prelude ---

# Build precise query
q = build_bool_query(
    phrases=["stem-like T cells"],
    required=["CD4", "IBD"],
    excluded=["mesenchymal"]
)
papers = search_bulk(q, max_results=30, year="2018-", fields_of_study="Medicine")
papers = deduplicate(papers)

print(format_results(papers, "Stem-like CD4 T cells in IBD"))

Save to /tmp/s2_search.py, then run with python3 /tmp/s2_search.py in a single Bash call. Rate limiting, retries, and backoff are automatic inside s2.py.

No API key: The skill works without S2_API_KEY. When the key is absent or invalid, s2.py automatically switches to unauthenticated mode (no x-api-key header) and widens the request gap to 5 s. Per S2 docs, anonymous calls share a global 1000 req/s pool across all unauthenticated users and can be "further throttled during periods of heavy use" — so a conservative 5 s gap protects against the heavy-use throttling, even though the steady-state pool is generous. If you still see sustained 429s, raise _MIN_GAP to 10 s. Keep max_results ≤ 30 per search and combine fewer searches per script. S2 recommends including an API key on every request — get one at https://www.semanticscholar.org/product/api#api-key-form.

Checkpoint: Verify the script ran successfully (no exceptions) and returned results. If 0 results, broaden the query or relax filters before presenting.

Worked Examples

Each example below assumes the standard prelude from Phase 2 is at the top of the script.

Example 1: Author workflow — "Find papers by Yann LeCun on self-supervised learning"

authors = search_authors("Yann LeCun", max_results=5)
print(format_authors(authors))

# Use the first match's ID to get their papers
author_id = authors[0]["authorId"]
papers = get_author_papers(author_id, max_results=50)
# Filter locally for topic
ssl_papers = [p for p in papers if "self-supervised" in (p.get("title") or "").lower()]
print(format_results(ssl_papers, "Yann LeCun - Self-Supervised Learning"))

Example 2: Citation chain with intent — "Who cited the Transformer paper and how did they use it?"

paper = get_paper("DOI:10.48550/arXiv.1706.03762")
print(f"Title: {paper['title']}, Citations: {paper['citationCount']}")

# Citation envelopes carry contextsWithIntent — keep them, don't flatten.
citing = get_citations(paper["paperId"], max_results=50)
citing.sort(key=lambda c: (c.get("citingPaper") or {}).get("citationCount", 0), reverse=True)
print(format_citations(citing, max_items=10))  # renders intent labels + context snippet

Example 3: Multi-seed recommendations with BibTeX export — "Find papers like these two but not about NLP"

recs = recommend(
    positive_ids=["DOI:10.1038/nature14539", "ARXIV:2010.11929"],
    negative_ids=["ARXIV:1706.03762"],
    limit=20
)
print(format_results(recs, "Vision papers like Deep Learning & ViT, excluding NLP"))

# Export BibTeX for top results
bib_data = batch_papers([r["paperId"] for r in recs[:10]], fields="title,citationStyles")
print(export_bibtex(bib_data))

Phase 3: Summarize & Present

• Use format_results() for consistent output (summary table + top-10 details)
• If user's language is Chinese, present summaries in Chinese
• Always note total results count and search strategy used
• Highlight most relevant papers based on the user's specific question

Phase 4: User Interaction Loop

After presenting results, always offer these options:

1. Translate — titles/summaries to Chinese (or other language)
2. Details — full abstract for specific paper numbers
3. Refine — narrow or expand search with different terms/filters
4. Similar — find papers similar to a specific result (find_similar())
5. Citations — who cited a specific paper and how (get_citations() + format_citations() for intent labels)
6. Export — save results via export_bibtex(), export_markdown(), or export_json()
7. Done — end search session

Loop until user says done. Each follow-up uses the same single-script pattern.

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Additional Resources

• S2folks GitHub — Official Semantic Scholar code examples: https://github.com/allenai/s2-folks
• Postman Collection — No-code API testing: linked from https://www.semanticscholar.org/product/api/tutorial
• API Documentation — Full endpoint reference: https://api.semanticscholar.org/

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API Quick Reference

Helper Module (s2.py)

Use the standard prelude from Phase 2 at the top of every script. Then call any of the functions below — the module's docstring (help(s2) or read s2.py) lists each by phase with one-line summaries.

Paper Search Functions

Function	Purpose	Max Results
search_relevance(query, **filters)	Simple broad search	1,000
search_bulk(query, sort=..., **filters)	Boolean precise search	10,000,000
search_snippets(query, paper_ids=, authors=, inserted_before=, **filters)	Full-text passage search	1,000
match_title(title)	Exact title match	1
paper_autocomplete(query)	Query-completion suggestions	—
get_paper(paper_id)	Single paper details	—
get_citations(paper_id, max_results, publication_date=)	Who cited this	10,000
get_references(paper_id, max_results)	What this cites	10,000
find_similar(paper_id, limit, pool)	Single-seed recommendations	500
recommend(positive_ids, negative_ids, limit)	Multi-seed recommendations	500
batch_papers(ids, fields)	Batch lookup (≤500)	—

Author Functions

Function	Purpose	Max Results
search_authors(query, max_results)	Find researchers by name	1,000
get_author(author_id)	Author profile (affiliations, h-index)	—
get_author_papers(author_id, max_results, publication_date=)	Author's publications	10,000
get_paper_authors(paper_id, max_results)	Paper's author list	1,000
batch_authors(ids, fields)	Batch author lookup (≤1000)	—

Filter Parameters (kwargs)

snake_case kwargs are translated to S2 camelCase params automatically (fields_of_study → fieldsOfStudy, min_citations → minCitationCount, publication_date → publicationDateOrYear, pub_types → publicationTypes, open_access → openAccessPdf). Use snake_case here.

year, publication_date, venue, fields_of_study, min_citations, pub_types, open_access

• year: "2020-", "-2019", "2016-2020"
• publication_date: "2024-01-01:2024-06-30" (YYYY-MM-DD range, open-ended OK)
• pub_types: Review, JournalArticle, Conference, ClinicalTrial, MetaAnalysis, Dataset, Book, CaseReport, Editorial, LettersAndComments, News, Study, BookSection

Boolean Query Syntax (bulk search only)

Syntax	Example	Meaning
"..."	"deep learning"	Exact phrase
+	+transformer	Must include
-	-survey	Exclude
|	CNN | RNN	OR
*	neuro*	Prefix wildcard
()	(CNN | RNN) +attention	Grouping
term~N	bugs~3	Fuzzy: matches words within N edits (e.g. buggy, buns)
"phrase"~N	"blue lake"~3	Proximity: up to N words between terms

Use build_bool_query(phrases, required, excluded, or_terms, fuzzy, proximity) to construct safely.

• fuzzy: list of (term, edit_distance) tuples
• proximity: list of (phrase, word_distance) tuples

Output Functions

Function	Purpose
format_table(papers, max_rows=30)	Markdown summary table
format_details(papers, max_papers=10)	Detailed entries with TLDR/abstract
format_citations(citations, max_items=10)	Citation envelopes with intent labels + context snippet
format_results(papers, query_desc)	Combined: summary + table + details
format_authors(authors, max_rows=20)	Author table (name, affiliations, h-index)
export_bibtex(papers)	BibTeX entries (requires citationStyles field)
export_markdown(papers, query_desc)	Full markdown report saved to file
export_json(papers, path)	JSON export saved to file
deduplicate(papers)	Remove duplicates by paperId

Supported ID Formats

DOI:10.1038/..., ARXIV:2106.15928, PMID:19872477, PMCID:PMC2323569, CorpusId:215416146, ACL:2020.acl-main.447, DBLP:conf/acl/..., MAG:3015453090, URL:https://...

Paper Fields

Default: title,year,citationCount,authors,venue,externalIds,tldr

Additional: corpusId (integer, S2 secondary ID), url (S2 paper page link), abstract, references, citations, openAccessPdf, publicationDate, publicationVenue, fieldsOfStudy, s2FieldsOfStudy, journal, isOpenAccess, referenceCount, influentialCitationCount (influential citations only), citationStyles, embedding, textAvailability

externalIds object contains: ArXiv, MAG, ACL, PubMed, Medline, PubMedCentral, DBLP, DOI

Author fields: name, affiliations, paperCount, citationCount, hIndex, homepage, externalIds, papers

Minimize fields. Per the official S2 tutorial: "Avoid including more fields than you need, because that can slow down the response rate." Only add abstract, references, or citations when the user explicitly needs them.

sort Parameter Values (bulk search only)

The sort kwarg accepts only these three values:

Value	Meaning
citationCount:desc	Most-cited first (default)
publicationDate:desc	Newest first
paperId:asc	Stable deterministic order (useful for pagination)

Recommendations Limits

find_similar() and recommend() return at most 500 papers per call (limit max = 500).

Datasets API Functions

For bulk download of full S2 datasets (papers, authors, abstracts, embeddings, etc.):

Function	Purpose	Requires key?
list_releases()	List all available release date strings	No
list_datasets(release_id="latest")	List datasets in a release	No
get_dataset_links(release_id, dataset_name)	Pre-signed download URLs for a dataset	Yes
get_dataset_diffs(start, end, dataset_name)	Incremental diffs between two releases	Yes

Available dataset names (pass as dataset_name):

Name	Description	Approx size
papers	Core paper attributes (title, authors, date, etc.)	~200M records, 30 × 1.5 GB
abstracts	Paper abstract text where available	~100M records, 30 × 1.8 GB
authors	Author core attributes (name, affiliation, paper count)	—
citations	Citation relationships between papers	—
embeddings-specter_v1	Dense SPECTER vector embeddings of papers	~120M records, 30 × 28 GB
publication-venues	Venue metadata	—
s2orc	Full-body text from open-access PDFs	—
tldrs	Short natural-language summaries	~100M records, 30 × 200 MB

All datasets are delivered as JSON Lines (one record per line). The diffs response contains update_files (insert/replace by primary key) and delete_files (remove from dataset).

Rate Limiting

s2.py adapts automatically based on whether

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