Skill UI

An expert skill designed for writing, reviewing, and refactoring SwiftUI code for iOS and macOS. It provides deep insights into modern state management (@Observable), optimizing view composition, handling data flow, and improving performance. The skill also assists with interpreting Instruments traces and adhering to Apple's latest Human Interface Guidelines.

Test Data Builder Guidance auto-activates when you ask about test data strategies, best practices, or configuration for automation frameworks, delivering step-by-step patterns, production-ready samples, and validation against common standards.

A comprehensive guide covering best practices for unit and integration testing, including Test-Driven Development (TDD) and Behavior-Driven Development (BDD). This guide details factory functions for creating reusable mock data and props, advanced mocking strategies for modules and hooks, and custom rendering utilities, ensuring tests are maintainable and reliable in React/TypeScript environments.

This comprehensive toolkit guides senior UX designers and researchers through the entire product development lifecycle. It covers generating data-backed personas, mapping user journeys, planning structured usability tests, and synthesizing raw research data into actionable design recommendations. Use it when conducting deep user research or validating product designs.

This skill validates API responses against predefined schemas, including OpenAPI and JSON Schema, to ensure strict contract compliance and data integrity. Use it for implementing robust contract tests in middleware or CI/CD pipelines, detecting schema drift, and guaranteeing backward compatibility across API versions.

This skill automates the creation of comprehensive test suites for any codebase. Utilizing advanced AI capabilities, it analyzes function signatures, automatically identifies potential edge cases, generates meaningful assertions, and produces necessary mock data and fixture files. It is essential for developers needing robust unit tests, increasing code coverage, and adopting Test-Driven Development (TDD) workflows across various frameworks like Jest or pytest.

Comprehensive guidance on using Shodan’s web UI, CLI, and API for reconnaissance, exposed-service discovery, screenshot capture, and exporting JSON/CSV data during penetration tests.

Guides using SQLMap to automate SQL injection discovery, enumerate databases/tables/columns, dump data, and collect hashes across MySQL/PostgreSQL/MSSQL/Oracle during authorized penetration tests.

Hypogenic automates LLM-driven hypothesis generation and testing over tabular datasets, blending literature insights with empirical analysis to accelerate discovery in domains like deception detection or AI content evaluation.

PyDESeq2 brings the DESeq2 differential expression workflow to Python, letting users load bulk RNA-seq counts, specify design formulas, fit DESeq2 models, run Wald tests with FDR correction, optionally apply apeGLM shrinkage, and export gene-level statistics within pandas/AnnData pipelines.

This comprehensive profiler provides systematic performance analysis for modern applications built with Node.js, Python, and Go. It detects critical bottlenecks across CPU, memory, and I/O dimensions. Key features include generating flamegraphs, analyzing bundle sizes, optimizing database queries (N+1 detection), and running rigorous load tests using k6 and Artillery. It mandates before-and-after measurements to ensure verifiable performance improvements.

Guides product and growth teams through evaluating whether to scale, test, or kill acquisition channels using unit economics (CAC, LTV, payback), customer quality (retention, NRR), and scalability indicators for data-driven go-to-market budgeting.