| 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":["python3"]},"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:
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 *
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))
author_id = authors[0]["authorId"]
papers = get_author_papers(author_id, max_results=50)
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']}")
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))
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"))
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
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) tuplesproximity: 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 S2_API_KEY is set:
| Mode | Gap | Official limit | Retries |
|---|
| Authenticated (valid key) | 1.1 s | Introductory 1 req/s per key, dedicated quota, cumulative across all endpoints (raisable on request) | 5× exponential backoff (2s→60s) |
| Unauthenticated (no key or invalid key) | 5.0 s | 1000 req/s shared globally across all anonymous users; "may be further throttled during periods of heavy use" | 5× exponential backoff (2s→60s) |
S2 recommends including an API key on every request, even for endpoints that work anonymously — it gives you a dedicated quota, a smoother experience under load, and better support if you need help. The introductory 1 req/s key can be raised on request. Get one at https://www.semanticscholar.org/product/api#api-key-form
The anonymous 1000 req/s pool is generous in steady state, but the docs explicitly warn it can be throttled hard during heavy use — that is why _MIN_GAP defaults to 5 s without a key, not the 1 ms a 1000 req/s budget would technically allow. If your workload still hits sustained 429s, set _MIN_GAP = 10.0 in s2.py or get a key. The 1 req/s key budget is cumulative across all endpoints, so chained calls (e.g. get_paper → get_citations) count separately.
Bulk Search Response Structure
search_bulk() returns a list of papers already unpaginated. Internally the raw response has:
| Field | Type | Meaning |
|---|
total | integer | Estimated total matching papers (not exact) |
token | string | Present when more pages exist; pass in next request |
data | array | Papers for this page |
s2.py handles token pagination automatically — you only see the final flat list.
Troubleshooting
| Error | Cause | Fix |
|---|
HTTPError 403 | S2_API_KEY is set but invalid/expired | s2.py auto-falls back to unauthenticated; or unset S2_API_KEY, or get a new key at https://www.semanticscholar.org/product/api#api-key-form |
HTTPError 404 | Bad paper/author ID | Check ID format — S2 returns {"error": "Paper/Author/Object not found"} or "...with id ### not found" |
HTTPError 429 after 5 retries | Sustained anonymous rate limit hit | Wait 60 s, raise _MIN_GAP in s2.py from 5.0 → 10.0, keep max_results ≤ 30, or get an API key |
ModuleNotFoundError: s2 | Skill directory not on path | Verify skill is installed at ~/.claude/skills/, ~/.openclaw/skills/, or as a Claude Code plugin under ~/.claude/plugins/ |
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; bulk search never returns it | Fall back to abstract field |
