semanticscholar-skill

name semanticscholar-skill description 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. license MIT homepage https://github.com/Agents365-ai/semanticscholar-skill compatibility 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. platforms ["macos","linux","windows"] metadata {"openclaw":{"requires":{"bins":"[Truncated]"},"emoji":"📚"},"hermes":{"tags":["semantic-scholar","academic","paper-search","citation","literature","research"],"category":"research","requires_tools":["python3"],"related_skills":["asta-skill","zotero-research-assistant","literature-review","paper-reader"]},"author":"Agents365-ai","version":"0.8.1"} 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: 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 and how ( get_citations() + format_citations() for intent labels) 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. 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/ 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