What problem does it solve? When reviewing multiple research papers, manually spotting contradictions, complementary findings, and unexplored gaps across studies is slow and error-prone. This Skill automates cross-paper association discovery over structured knowledge items, producing a knowledge graph and prioritized research gaps. ## Core Features & Use Cases - Seven-Type Relation Detection: Checks every knowledge-item pair for contradiction, complement, evolution, gap, causation, parallel, and dependency relations, each with a 0-1 confidence score and evidence reference. - Knowledge Graph Construction: Outputs a graph of papers as nodes and typed associations as edges, with summary statistics by relation type. - Research Gap Identification: Detects methodology, population, and domain gaps with explicit evidence and H/M/L priority, formatted as direct input for hypothesis generation. - Use Case: Given extracted knowledge from five papers on ADHD eye-tracking, produce associations.json and gaps.json revealing a contradiction between two screening studies and a high-priority methodology gap for downstream hypothesis generation. ## Quick Start Analyze these extracted knowledge items from my literature set and identify all contradictions, complementary findings, and research gaps with confidence scores.
