Senior Prompt Engineer

Replace existing static dependency call sites with a wrapper or built-in abstraction that already exists or is registered in DI, across a bounded scope (file, project, namespace). USE FOR: replace DateTime.UtcNow/DateTime.Now with TimeProvider and add the constructor parameter, migrate static call sites to a wrapper already in DI, bulk replace File.* with IFileSystem, scoped migration of statics in only certain files, update unit tests to a fake time source, make an existing static or utility class testable by adding an ambient TimeProvider/IFileSystem seam while every current call site keeps compiling, behavior-preserving time refactors that must keep the same DateTimeKind. DO NOT USE FOR: detecting statics (use detect-static-dependencies), designing a brand-new wrapper interface that does not exist yet (use generate-testability-wrappers), migrating between test frameworks.

Use when adding metadata to commits without changing history, tracking review status, test results, code quality annotations, or supplementing commit messages post-hoc - provides git notes commands and patterns for attaching non-invasive metadata to Git objects.

UX research and design toolkit for Senior UX Designer/Researcher including data-driven persona generation, journey mapping, usability testing frameworks, and research synthesis. Use when conducting user research, creating personas, mapping user journeys, planning usability tests, or validating designs.

Generate database seed scripts with realistic sample data. Reads Drizzle schemas or SQL migrations, respects foreign key ordering, produces idempotent TypeScript or SQL seed files. Handles D1 batch limits, unique constraints, and domain-appropriate data. Use when populating dev/demo/test databases. Triggers: 'seed database', 'seed data', 'sample data', 'populate database', 'db seed', 'test data', 'demo data', 'generate fixtures'.

Use for \"interrogate\", \"adversarial review\", \"multi-model review\", \"challenge this\", \"stress test this code\", \"find blind spots\", or \"tear this apart\". Multiple LLM reviewers challenge changes from independent angles.

World-class senior data scientist skill specialising in statistical modeling, experiment design, causal inference, and predictive analytics. Covers A/B testing (sample sizing, two-proportion z-tests, Bonferroni correction), difference-in-differences, feature engineering pipelines (Scikit-learn, XGBoost), cross-validated model evaluation (AUC-ROC, AUC-PR, SHAP), and MLflow experiment tracking — using Python (NumPy, Pandas, Scikit-learn), R, and SQL. Use when designing or analysing controlled experiments, building and evaluating classification or regression models, performing causal analysis on observational data, engineering features for structured tabular datasets, or translating statistical findings into data-driven business decisions.