What problem does it solve? Entering an unfamiliar research field usually produces a flattened narrative review that hides disagreements, conflates influence with evidence quality, and cannot be traced back to sources. This Skill builds a structured, source-traceable map of a field that keeps schools, methods, disputes, gaps, and evidence strength as separate dimensions. ## Core Features & Use Cases - Disclosed search protocol: Records databases, queries, date ranges, languages, inclusion/exclusion rules, and stopping conditions before drawing conclusions, so coverage limits stay visible. - Work inventory and clustering: Builds a stable-ID inventory of works with access state and method, then clusters them by school, question, method, or evidence base with interpretive boundaries labeled. - Relationship ledger: Classifies intellectual links as builds-on, critiques, replicates, contradicts, applies, or independent-parallel, each with a source locator and confidence level. - Use Case: A graduate student starting a thesis on a cross-disciplinary topic uses it to produce a field map with competing schools preserved, a dispute and gap map, and a justified reading sequence before writing any synthesis. ## Quick Start Ask the AI to map the literature on your research question with a defined scope, date range, and disciplines, and to deliver the cluster map, relationship ledger, and reading sequence.
