PhD 학생들은 문학 조사에 주를 보냅니다. 그 시간의 대부분은 배관이다 - 종이를 발견, 주장을 추출, 십자가 철근 terminology, 건물 비교 테이블, chasing 인용 체인. 문제의 지적 작업 - 공유 가정을 스트리팅, 그들이 휴식을 발견, 틈을 인식하지 않은 것은 악화 - 노력의 분수를 가지고 있지만 학생이 이미 물류에서 배출 한 후 발생합니다.

이 기술 offloads 기계에 배관. NotebookLM은 종이를 섭취하고 정확한 견적으로 지상 추출을 반환합니다. Claude Code는 워크플로우를 관리합니다. 구조화된 쿼리, 템플릿 인구, 읽기 심도 추적. 당신은 hypotheses 형성에 초점을, 어떤 사정을 결정하고, narrative 건물. 기술은 단지 시간을 절약하지 않습니다; 그것은 당신에게 예리한 만듭니다. 모든 체크포인트 - 캘리브레이션 프로토콜, WYSIATI 체크, 첫 번째 포인스 디컴포지트 - 짧은 곳에서 이해와 confront을 분명히합니다.

**이를 위해: ** PhD 학생과 10-150 + 종이의 문학 조사를 실행하는 연구원. 시스템 및 네트워킹을 위해 조정 (SIGCOMM, NSDI, CoNEXT, IMC) 하지만 모든 필드에 적합 — 귀하의 필드에 사용자 지정 참조.

literature-survey-skill

PhD students spend weeks on literature surveys. Most of that time is plumbing — finding papers, extracting claims, cross-referencing terminology, building comparison tables, chasing citation chains. The intellectual work that matters — spotting shared assumptions, finding where they break, recognizing the gap nobody has exploited — takes a fraction of the effort but only happens after the student is already exhausted from logistics.

This skill offloads the plumbing to the machine. NotebookLM ingests your papers and returns grounded extractions with exact quotes. Claude Code manages the workflow — structured queries, template population, reading-depth tracking. You focus on forming hypotheses, deciding what matters, and building the narrative. The skill doesn’t just save time; it makes you sharper. Every checkpoint — calibration protocols, WYSIATI checks, first-principles decomposition — forces you to articulate your understanding and confront where it falls short.

Who this is for: PhD students and researchers running literature surveys of 10-150+ papers. Tuned for systems and networking (SIGCOMM, NSDI, CoNEXT, IMC) but adaptable to any field — see Customize for Your Field.

Built on Claude Code.

Quick Start

git clone https://github.com/SNL-UCSB/literature-survey-skill.git
cd literature-survey-skill
./setup.sh

This installs the skill to ~/.claude/skills/survey/. Four commands become available:

/survey intent     — Capture your goals, expertise, and survey archetype
/survey triage     — Map the landscape, prioritize reading depth
/survey deepen     — Structured deep reading with craft and visualization extraction
/survey synthesize — Cross-paper analysis for your deliverable
/survey expand     — Propose and evaluate corpus growth

Start with /survey intent. The skill guides you from there.

Prerequisites

The NotebookLM MCP CLI must be configured as an MCP server in your Claude Code setup. Follow the setup guide — it covers installation, Google authentication, and MCP registration in ~5 minutes.

Manual Install

Copy SKILL.md to ~/.claude/skills/survey/SKILL.md. Copy reference/ and templates/ alongside it.

How Each Mode Works

Intent — “What are you trying to learn?”

The skill captures your goals, expertise level, and success criteria before any paper is read. It assigns one of four survey archetypes with calibrated defaults: