semantic-scholar-deep

# Semantic Scholar — Deep Research Purpose: fill the gaps that `semantic-scholar-lookup` (allenai) leaves — `references`, `recommendations`, `batch`, and multi-hop citation-graph traversal. ## Contents - [Dispatch Rule](#dispatch-rule-read-first) — inline vs delegate; model selection - [When to Use](#when-to-use) — trigger scenarios - [Scripts](#scripts) — `ss_client.py` + `citation_graph.py` - [Authentication & Rate Limits](#authentication--rate-limits) - [Progressive Disclosure](#progressive-disclosure) — deeper references - [Output Hygiene](#output-hygiene) - [Integration](#integration) — typical pipeline with the subagent ## Dispatch Rule (read first) Two execution modes: ### Inline (run the Bash scripts yourself) Use when the user asks for **one specific endpoint**: - "get references of paper X" → `ss_client.py references <id>` - "recommendations for paper Y" → `ss_client.py recommendations <id>` - "batch-resolve these 30 DOIs" → `ss_client.py batch ...` - "find the snippet where X is said" → `ss_client.py snippets "..."` Fast, cheap, no orchestration overhead. ### Delegate to `deep-paper-researcher` subagent Use when the task is **multi-step** or would otherwise flood the context: - Literature review on a topic - Citation graph / network analysis around a seed paper - Novelty check for an idea - State-of-the-art survey - Anything that requires merging Exa discovery + S2 graph + ranking **Mandatory prompt contents.** The subagent runs in isolated context with ...