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
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. Usesearch_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() |
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