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 *