literature-search

name: literature-search description: "Comprehensive multi-database scientific literature search orchestrating Semantic Scholar, OpenAlex, arXiv, PubMed, and CrossRef. Use when: (1) systematic literature review, (2) finding all relevant papers on a topic, (3) checking state of the art, (4) building comprehensive bibliographies. NOT for: single-database queries (use specific search skills), data analysis (use code-execution)." metadata: { "openclaw": { "emoji": "🔎" } }

Literature Search (Meta Skill)

Orchestrate comprehensive literature searches across multiple databases. Always execute real API calls — never fabricate results or rely on training data.

Priority Order of Databases

1. Semantic Scholar (PRIMARY) — best relevance ranking, AI TLDR summaries, citation graph
2. OpenAlex (PRIMARY) — 250M+ works, powerful filtering, open access URLs
3. arXiv — preprints in physics, math, CS, biology, finance, statistics
4. PubMed — biomedical and life sciences (NCBI may be unreachable from some networks)
5. CrossRef — DOI resolution and metadata only (NOT for search — poor relevance ranking)

IMPORTANT: CrossRef search results are poorly ranked by relevance. Never use CrossRef as the primary search engine. Use it only for DOI-based lookups and metadata enrichment.

Mandatory Search Protocol

Every literature search MUST follow this protocol:

Step 1: Semantic Scholar Search (always do this first)

# Primary search — returns papers ranked by relevance with AI summaries
curl -s "https://api.semanticscholar.org/graph/v1/paper/search?\
query=YOUR+SEARCH+TERMS&limit=10&\
fields=title,authors,year,abstract,citationCount,influentialCitationCount,\
isOpenAccess,openAccessPdf,url,externalIds,tldr,venue,publicationDate"

Parse results with:

| python3 -c "
import sys, json
data = json.load(sys.stdin)
print(f'Total: {data[\"total\"]} papers')
for i, p in enumerate(data['data']):
    authors = ', '.join(a['name'] for a in (p.get('authors') or [])[:3])
    if len(p.get('authors') or []) > 3: authors += ' et al.'
    tldr = p.get('tldr', {})
    tldr_text = tldr['text'][:150] if tldr else 'N/A'
    oa = '🔓' if p.get('isOpenAccess') else '🔒'
    doi = (p.get('externalIds') or {}).get('DOI', '')
    print(f'[{i+1}] {p[\"title\"]}')
    print(f'    {authors} ({p.get(\"year\",\"?\")}) — {p.get(\"venue\",\"?\")}')
    print(f'    Cited: {p.get(\"citationCount\",0)} (influential: {p.get(\"influentialCitationCount\",0)}) {oa}')
    print(f'    TLDR: {tldr_text}')
    print(f'    DOI: {doi}')
    print()
"

Useful filters:

• year=2022-2025 — restrict by year range
• fieldsOfStudy=Computer Science — filter by discipline
• minCitationCount=10 — only cited papers

Step 2: OpenAlex Search (for broader coverage + OA links)

# Complementary search with powerful filtering
curl -s "https://api.openalex.org/works?\
search=YOUR+SEARCH+TERMS&per_page=10&\
sort=relevance_score:desc&\
select=title,publication_year,cited_by_count,doi,authorships,open_access,\
primary_location,abstract_inverted_index&\
mailto=scienceclaw@openclaw.ai"

Useful filters (append to URL as &filter=):

• publication_year:2023-2025 — year range
• cited_by_count:>50 — minimum citations
• open_access.is_oa:true — only open access
• authorships.author.id:A5023888391 — by author OpenAlex ID
• concepts.id:C41008148 — by concept (e.g., Computer Science)

Step 3: Discipline-Specific Database (if relevant)

Step 4: Deduplication and Ranking

Match across databases by DOI (most reliable), then normalized title. Rank by: Semantic Scholar relevance > citation count > influential citations > recency.

Step 5: Citation Chaining (for thorough searches)

For top 3-5 seed papers, retrieve their references and citations:

# Forward citations (who cites this paper)
curl -s "https://api.semanticscholar.org/graph/v1/paper/{paperId}/citations?\
fields=title,year,citationCount,venue&limit=20"

# Backward references (what this paper cites)
curl -s "https://api.semanticscholar.org/graph/v1/paper/{paperId}/references?\
fields=title,year,citationCount,venue&limit=20"

Step 6: Paper Recommendations (for discovery)

# Find similar papers
curl -s "https://api.semanticscholar.org/recommendations/v1/papers/\
forpaper/{paperId}?fields=title,year,citationCount,tldr&limit=10"

Search Quality Checklist

Before presenting results, verify:

• At least Semantic Scholar was searched with a real API call
• Results contain real DOIs/paper IDs (not fabricated)
• Citation counts are from the API (not estimated)
• Each paper has a verifiable identifier (DOI, arXiv ID, PMID, or S2 URL)
• TLDR summaries are from Semantic Scholar (not self-generated)

Output Format

[1] Title
    Authors (Year) — Venue
    Cited: N (influential: M) 🔓/🔒
    TLDR: AI-generated summary from Semantic Scholar
    DOI: 10.xxxx/xxxxx | arXiv: xxxx.xxxxx | PMID: xxxxxxxx
    URL: https://...

Zero-Hallucination Rule (ABSOLUTE)

Every citation detail must come from a tool result in this conversation.

• NEVER fabricate or "fill in" paper titles, authors, DOIs, PMIDs, citation counts, or journal names from training data
• NEVER say "a well-known study by X et al." without having searched for it first
• If a search returns 0 results, report that honestly — do not substitute training knowledge
• If a tool returns partial metadata (title but no DOI), report only what the tool returned
• Before presenting any paper, verify: Did a tool in THIS conversation return this information?

Common Pitfalls to Avoid

1. DO NOT use CrossRef /works?query= for discovery — its relevance ranking is poor
2. DO NOT fabricate paper titles, authors, or DOIs from training knowledge
3. DO NOT skip API calls and rely on what you "know" about the literature
4. DO NOT present Semantic Scholar TLDRs as your own analysis
5. ALWAYS run the actual curl commands and parse real responses
6. ALWAYS include at least one verifiable identifier per paper
7. ALWAYS self-check: every detail in your response must trace back to a tool result

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