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. Use `search_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()` | | 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 recommendation...