semanticscholar-skill（语义学术技能）

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

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

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:

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

Author Functions

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)

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

Output Functions

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