What problem does it solve? Adding a new analysis to a research codebase requires matching existing conventions for structure, typing, testing, and output handling, which is error-prone when done manually. ## Core Features & Use Cases - Guided Scaffolding: Collects the domain, analysis type, module name, inputs, and outputs before writing any code. - Convention-Aware Generation: Reads existing analysis modules in the target domain to mirror established patterns such as pure functions, type hints, and Google docstrings. - Test Creation: Generates a matching test module with unit tests on synthetic data and marked integration tests. - Use Case: A researcher wants to add a robustness check to the financial_tda domain; the Skill creates financial_tda/analysis/<module>.py plus tests/financial_tda/test_<module>.py following project conventions. ## Quick Start Ask the assistant to create a new regime analysis module named regime_shift in the financial_tda domain that consumes persistence diagrams and outputs statistics and plots.