{
"data": [
{
"citingPaper": {
"paperId": "def456ghi",
"title": "Newer Paper Citing This",
"year": 2024,
"abstract": "We extended the work of [original paper]...",
"externalIds": {
"DOI": "10.9012/citing.2024"
}
}
}
]
}
Filter for relevance:
For each citing paper:
Title match: Keywords present in title?
Abstract match: User's query terms in abstract?
Recency: Newer papers often build on findings (prioritize < 2 years)
Citation count: If Semantic Scholar provides, highly cited papers more likely relevant
Scoring:
Title keywords match: +3 points
Abstract keywords match: +2 points
Recent (< 2 years): +2 points
Moderate recency (2-5 years): +1 point
Only add to queue if score ≥ 5
4. Deduplication
Before adding to queue:
Check papers-reviewed.json:
doi = paper["externalIds"].get("DOI")
if doi in papers_reviewed:
skip # Already processed
else:
add to queue
CRITICAL: After evaluating any paper from citation traversal, add it to papers-reviewed.json regardless of score. This prevents re-processing the same paper from multiple sources.
Track citation relationship in citations/citation-graph.json:
CRITICAL: Use ONLY citation-graph.json for citation tracking. Do NOT create custom files like forward_citation_pmids.txt or citation_analysis.md. All findings go in SUMMARY.md.
If relevant, extract data and potentially traverse its citations too
Smart Traversal Limits
To avoid explosion:
Only traverse papers scoring ≥ 7 in initial evaluation
Only follow citations scoring ≥ 5 in relevance filtering
Limit traversal depth to 2 levels (original → references → references of references)
Check with user after every 50 papers total
Breadth-first strategy:
Get all references + citations for current paper
Filter and score them
Add high-scoring ones to queue
Process next paper in queue
Repeat until queue empty or hit limit
Progress Reporting
Report as you traverse:
🔗 Analyzing citations for: "Original Paper Title"
→ Found 45 references, 12 look relevant
→ Found 23 citing papers, 8 look relevant
→ Adding 20 papers to queue
📄 [51/127] Following reference: "Method for measuring IC50"
Source: Referenced by original paper in Methods section
Abstract score: 7 → Fetching full text...
API Rate Limiting
Semantic Scholar limits:
Free tier: 100 requests per 5 minutes
With API key: 1000 requests per 5 minutes
Be efficient:
Request multiple fields in one call (?fields=title,abstract,externalIds,year)
Use limit=100 to get more results per request
Cache responses - don't re-fetch same paper
If rate limited:
Wait 5 minutes
Report to user: "⏸️ Rate limited by Semantic Scholar API. Waiting 5 minutes..."
Consider getting API key for higher limits
Integration with Other Skills
After traversing citations:
Queue now has N new papers to evaluate
For each, use evaluating-paper-relevance skill
If relevant, extract to SUMMARY.md
If highly relevant (≥9), traverse its citations too
Update citation-graph.json to track relationships
Quick Reference
Task
API Endpoint
Get paper by DOI
GET /graph/v1/paper/DOI:{doi}?fields=paperId,title
Get references
GET /graph/v1/paper/{paperId}/references?fields=contexts,title,abstract,externalIds
Get citations
GET /graph/v1/paper/{paperId}/citations?fields=title,abstract,externalIds
Check if processed
Look up DOI in papers-reviewed.json
Filter relevance
Score based on context/title/intent/recency
Relevance Filtering Checklist
Before adding citation to queue:
Check if already in papers-reviewed.json (skip if yes)
Score based on context/title keywords (need ≥ 5)
Verify external ID (DOI or PMID) exists
Add source tracking ("backward_from:DOI" or "forward_from:DOI")
Add to queue with metadata
Common Mistakes
Not tracking all evaluated papers: Only adding relevant papers to papers-reviewed.json → Add EVERY paper after evaluation to prevent re-review
Creating custom analysis files: Making forward_citation_pmids.txt, CITATION_ANALYSIS.md, etc. → Use ONLY citation-graph.json and SUMMARY.md
Following all citations: Exponential explosion → Filter before adding to queue
Ignoring context: Citation might be tangential → Read context strings
Not deduplicating: Re-process same papers → Always check papers-reviewed.json before and after evaluation
Too deep: Following 5+ levels → Limit to 2 levels, check with user
Missing forward citations: Only checking references → Use both backward and forward
No rate limiting awareness: API blocks you → Add delays, handle 429 errors
Example Workflow
1. User asks: "Find selectivity data for BTK inhibitors"
2. Search finds Paper A (score: 9, has great IC50 data)
3. Traverse citations for Paper A:
- References: 45 total, 12 relevant (mention "selectivity", "IC50")
- Citations: 23 total, 8 relevant (newer papers on BTK)
4. Add 20 papers to queue
5. Evaluate first queued paper (score: 8)
6. Extract data, traverse its citations (add 5 more)
7. Continue until queue empty or user says stop
Next Steps
After traversing citations:
Process queued papers with evaluating-paper-relevance
Update SUMMARY.md with new findings
Check if reached checkpoint (50 papers or 5 minutes)
