What problem does it solve? When analyzing texts, conceptual maps, or rhizomatic architectures, it is easy to mistake thematic proximity or co-occurrence for meaningful connection. This Skill provides a rigorous procedure to determine which relations actually connect elements, how those connections function, and what analytical significance they carry—without inventing unsupported links. ## Core Features & Use Cases - Relational Identification: Distinguishes genuine connections (dependency, mediation, feedback, transformation) from mere thematic association, proximity, or coexistence. - Evidence Grading: Classifies each connection as explicit, strongly inferential, weakly inferential, or unsupported, with directionality, asymmetry, scope, and temporal analysis. - Structured Output Protocol: Produces per-connection reports covering relation, function, transformation, propagation, inter-stem relevance, classification (A–E), and confidence level. - Use Case: Given a cross-domain research map linking AI, Science, and Society, use this Skill to test whether claimed connections are demonstrated, whether they imply causality or transformation, and whether they support node or inter-stem status. ## Quick Start Analyze the connections in this conceptual map and report each significant relation with its evidence, function, directionality, and classification.
