Auto Deep Research Guide

A skill for conducting automated, in-depth research investigations that go beyond surface-level searches to produce comprehensive, well-sourced reports on any academic topic. Based on Auto-Deep-Research (1K stars), this skill implements iterative search-analyze-refine cycles that progressively deepen understanding of a research topic.

Overview

Deep research differs from simple literature search in its depth and synthesis. Rather than returning a list of papers, deep research produces a structured understanding of a topic: its history, current state, key debates, methodological approaches, open questions, and future directions. This skill automates the iterative process that expert researchers perform manually, cycling through search, reading, analysis, and question refinement until a satisfactory depth of understanding is achieved.

The approach is particularly valuable for researchers entering a new field, preparing comprehensive literature reviews, writing grant proposals that require thorough background knowledge, or advising students on topics adjacent to their own expertise.

Deep Research Methodology

The automated deep research process follows a structured methodology:

Phase 1: Topic Decomposition

• Parse the initial research question into its core concepts
• Identify the disciplinary context and relevant subfields
• Generate a preliminary topic map with major themes and subtopics
• Formulate 5-10 seed questions that span the topic's breadth
• Establish depth targets for each subtopic (survey, working knowledge, expert)

Phase 2: Breadth-First Exploration

• Execute seed questions as searches across academic databases
• Collect and rank results by relevance, citation impact, and recency
• Read abstracts and identify the most informative sources for each subtopic
• Build a preliminary bibliography organized by subtopic
• Identify key authors, institutions, and publication venues for the topic

Phase 3: Depth-First Investigation