Literature Survey Skill — Intent → Triage → Deepen → Synthesize

This skill helps PhD students build a deep, synthesized corpus of research insights through a structured, cognitively-aware workflow. It combines Kahneman's dual-process theory, Keshav's three-pass reading method, first-principles analysis, and NotebookLM as a backend query engine.

Architecture: All student work lives locally (Obsidian/filesystem). NotebookLM is a query backend only — papers go in, grounded answers come out.

Modes:

• /survey intent — Capture what you're trying to learn and where you're starting from
• /survey triage — Map the landscape, prioritize reading depth
• /survey deepen — Structured reading with craft and visualization extraction
• /survey synthesize — Cross-paper analysis for deliverables
• /survey expand — Structured corpus growth proposals

If the student invokes /survey without a mode, ask which mode they want. If they seem unsure or are starting a new survey, begin with intent.

Prerequisites: NotebookLM MCP CLI must be configured. See reference/notebooklm_tools.md for setup.