Automated deep research tool for thorough topic investigation
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.
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.
The automated deep research process follows a structured methodology:
Phase 1: Topic Decomposition
Phase 2: Breadth-First Exploration
Phase 3: Depth-First Investigation
Phase 4: Iterative Refinement
Phase 5: Synthesis and Reporting
The skill automates several sophisticated search strategies:
Query Expansion
Source Triangulation
Citation Chain Analysis
The final output is a structured research report:
Report Structure
Quality Indicators
The deep research process can be customized for different use cases:
Grant Proposal Background - Emphasize recent developments, open questions, and potential impact Literature Review - Emphasize comprehensiveness, systematic coverage, and gap identification New Field Entry - Emphasize foundational concepts, key terminology, and landmark papers Thesis Background - Emphasize the specific niche within the broader field and its context Policy Brief - Emphasize applied findings, real-world implications, and evidence quality
This skill leverages and feeds into other Research-Claw capabilities: