Academic Deep Research

A structured multi-cycle investigation framework designed for exhaustive academic research. Unlike single-pass literature searches, this skill implements iterative deepening: each cycle expands the search scope, refines the query based on discovered themes, and synthesizes findings into an increasingly comprehensive knowledge map.

Overview

Traditional literature searches follow a linear process: define keywords, search databases, screen results, extract data, synthesize. This approach works well for scoping reviews but often misses important connections across subfields, fails to surface grey literature, and stops too early when the obvious sources have been found. Academic Deep Research addresses these limitations through a multi-cycle approach where each cycle builds on the findings of the previous one, progressively expanding the search frontier.

The framework is inspired by systematic review methodology but optimized for speed and breadth rather than exhaustive recall within a single database. It is particularly useful for interdisciplinary research questions, emerging fields where terminology is not yet standardized, and complex topics where important insights may be scattered across diverse literatures.

The Multi-Cycle Framework

Cycle Architecture

Cycle 1: BREADTH (Survey Phase)
  Purpose: Map the landscape, identify major themes and key authors
  Sources: Google Scholar, Semantic Scholar, review articles
  Output: Theme taxonomy, key author list, terminology inventory