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attack-tree-construction

A visual framework for structuring continuous product discovery. Connects a desired outcome to customer opportunities, possible solutions, and experiments to validate them.

Structure vague product requests into validated opportunities and testable solutions before building. \n \n Guides teams through a five-question discovery process: extract desired outcome, identify customer problems (opportunities), generate solution ideas, evaluate feasibility and impact, and select a proof-of-concept to test first \n Prevents \"feature factory\" syndrome by forcing divergence across multiple opportunities and solutions before converging on what to build \n Outputs a structured

Systematic visualization and analysis of attack paths with difficulty, cost, and detection metrics. \n \n Provides Python data models for building attack trees with OR/AND logic, leaf attacks, and attributes (difficulty, cost, detection risk, time required) \n Includes fluent builder API for constructing trees programmatically and methods to find easiest, cheapest, and stealthiest attack paths \n Exports to Mermaid and PlantUML diagram formats for stakeholder communication and threat visualizati