Use when searching academic papers, looking up citations, finding authors, or getting paper recommendations using the Semantic Scholar API. Triggers on queries about research papers, academic search, citation analysis, or literature discovery.
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Use when searching academic papers, looking up citations, finding authors, or getting paper recommendations using the Semantic Scholar API. Triggers on queries about research papers, academic search, citation analysis, or literature discovery.
Requires python3 and the `requests` package. Set S2_API_KEY for higher rate limits (request at https://www.semanticscholar.org/product/api#api-key). Works unauthenticated with strict rate limits.
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:
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"
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?"
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"
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:
Translate — titles/summaries to Chinese (or other language)
Details — full abstract for specific paper numbers
Refine — narrow or expand search with different terms/filters
Similar — find papers similar to a specific result (find_similar())
Citations — who cited a specific paper (get_citations())
Export — save results via export_bibtex(), export_markdown(), or export_json()
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)
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 *
