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

| 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

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:

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

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)

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

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 |

| 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 |