Semantic Scholar

Use when searching academic papers, looking up citations, finding authors, or getting paper recommendations using the Semantic Scholar API. Triggers on queri...

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

User wants...	Strategy	Function
Broad topic exploration	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. Example:

python

import sys, os
SKILL_DIR = next((p for p in [
    os.path.expanduser("~/.claude/skills/semanticscholar-skill"),
    os.path.expanduser("~/.openclaw/skills/semanticscholar-skill"),
] if os.path.isdir(p)), ".")
sys.path.insert(0, SKILL_DIR)
from s2 import *

# 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"))

Execute with: python3 /tmp/s2_search.py

Rules:

• Import everything from s2: from s2 import *
• Write script to /tmp/s2_search.py (or similar temp path)
• One Bash call to execute. Never chain multiple API calls via separate Bash invocations.
• Rate limiting, retries, and backoff are automatic inside s2.py

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

Worked Examples

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

python

import sys, os
SKILL_DIR = next((p for p in [
    os.path.expanduser("~/.claude/skills/semanticscholar-skill"),
    os.path.expanduser("~/.openclaw/skills/semanticscholar-skill"),
] if os.path.isdir(p)), ".")
sys.path.insert(0, SKILL_DIR)
from s2 import *

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 — "Who cited the Transformer paper and what did they build on?"

python

import sys, os
SKILL_DIR = next((p for p in [
    os.path.expanduser("~/.claude/skills/semanticscholar-skill"),
    os.path.expanduser("~/.openclaw/skills/semanticscholar-skill"),
] if os.path.isdir(p)), ".")
sys.path.insert(0, SKILL_DIR)
from s2 import *

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

# Get top-cited papers that cite this one
citing = get_citations(paper["paperId"], max_results=50)
citing_papers = [c["citingPaper"] for c in citing if c.get("citingPaper")]
citing_papers.sort(key=lambda p: p.get("citationCount", 0), reverse=True)
print(format_results(citing_papers, "Most-cited papers citing Attention Is All You Need"))

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

python

import sys, os
SKILL_DIR = next((p for p in [
    os.path.expanduser("~/.claude/skills/semanticscholar-skill"),
    os.path.expanduser("~/.openclaw/skills/semanticscholar-skill"),
] if os.path.isdir(p)), ".")
sys.path.insert(0, SKILL_DIR)
from s2 import *

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 (get_citations())
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.

---

API Quick Reference

Helper Module (s2.py)

python

import sys, os
SKILL_DIR = next((p for p in [
    os.path.expanduser("~/.claude/skills/semanticscholar-skill"),
    os.path.expanduser("~/.openclaw/skills/semanticscholar-skill"),
] if os.path.isdir(p)), ".")
sys.path.insert(0, SKILL_DIR)
from s2 import *

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, **filters)	Full-text passage search	1,000
match_title(title)	Exact title match	1
get_paper(paper_id)	Single paper details	—
get_citations(paper_id, max_results)	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)	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)

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

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

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_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: abstract, references, citations, openAccessPdf, publicationDate, publicationVenue, fieldsOfStudy, s2FieldsOfStudy, journal, isOpenAccess, referenceCount, influentialCitationCount, citationStyles, embedding, textAvailability

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

Rate Limiting

Handled automatically by s2.py: 1.1s gap between requests, exponential backoff (2s→4s→8s→16s→32s, max 60s) on 429/504 errors, up to 5 retries.

Troubleshooting

Error	Cause	Fix
HTTPError 403	Missing or invalid API key	Verify S2_API_KEY is set: echo $S2_API_KEY
HTTPError 429 after 5 retries	Sustained rate limit exceeded	Wait 60s, reduce max_results, or split into smaller batches
ModuleNotFoundError: s2	Skill directory not on path	Verify skill is installed at ~/.claude/skills/ or ~/.openclaw/skills/
ModuleNotFoundError: requests	requests not installed	pip install requests or uv pip install requests
0 results returned	Query too specific or filters too narrow	Broaden query, remove filters, try search_relevance() instead of search_bulk()
KeyError: 'data'	Endpoint returned error object	Check r.get("message") for API error details
tldr field is empty	Not all papers have TLDR	Fall back to abstract field; bulk search never returns tldr