What problem does it solve? Starting a new topological data analysis experiment requires knowing the correct domain package layout, coding conventions, and test structure, which is error-prone when done manually. ## Core Features & Use Cases - Domain-aware scaffolding: Places new experiment files in the correct package for financial_tda, trajectory_tda, or poverty_tda based on the research domain. - Convention enforcement: Generates modules with typed configuration, Google-style docstrings, pathlib paths, logging, and a standard load/compute/analyse/main pipeline structure. - Test stub generation: Creates a matching pytest file with smoke tests and appropriate slow or integration markers. - Use Case: A researcher wants to test a hypothesis about sector crises using Rips filtration on financial data; the Skill asks for the domain, method, and research question, then produces a ready-to-fill experiment module and test file. ## Quick Start Ask the assistant to scaffold a new TDA experiment named sector_crisis_2024 in the financial_tda domain using the Rips method.
