Skill UI

This skill helps generate comprehensive and structured TestNG test cases for Java. It supports advanced testing features crucial for enterprise automation, including method grouping, data-driven testing using @DataProvider, parallel execution setup, XML suite configuration, and custom listeners. Use it when building robust, scalable, and maintainable automated test suites.

This template generates structured, multi-frame video scripts compatible with Remotion and Hyperframes. It guides the creation of cinematic, high-impact videos, ensuring each frame conveys a single, strong concept. The output includes necessary metadata for automated playback, transitions, and timing, making the script ready for professional video rendering into MP4.

WGM is a robust, agentic methodology designed to transform vague or rough requirements into working, validated software. It enforces a disciplined workflow across six stages: deep alignment (Grill), structured planning, deterministic iterative building (Loop), mandatory holdout-scenario judging, and final documentation audits. It ensures that the resulting code converges against rigorously tested acceptance criteria rather than relying on single-pass generation.

This guide details integrating FoundationModels for running Large Language Models (LLMs) directly on the device (iOS 26). It covers advanced patterns including text generation, extracting structured data using @Generable, implementing custom tool calling for domain logic, and streaming responses for real-time UI updates. Key features emphasize strong privacy and offline capability, ensuring no data leaves the device.

A comprehensive security auditing tool designed to analyze codebases for vulnerabilities. It provides a structured workflow to scan entire repositories, review specific changes (like PR diffs or single commits), and suggest verified patch files. Findings are challenged by independent agents, ensuring that suggested fixes are targeted and require manual review before application.

This skill acts as the central orchestrator for writing and submitting manuscripts to the Journal of the American Academy of Religion (JAAR). It guides authors through the entire writing lifecycle, emphasizing the critical 'reframing test' to ensure the work addresses issues of broad, fundamental interest to the study of religion, rather than remaining confined to a narrow subfield. It provides a structured path from initial idea generation to final submission and revision.

This skill guides the generation of a professional and highly structured revision response package for top-tier academic journals. It specifically addresses multi-round, double-blind review processes, ensuring every reviewer comment is addressed point-by-point. The output includes a summary statement, a detailed response table, and the marked revision manuscript, all while adhering to strict rules against identity leakage, making it ideal for streamlining the academic publishing workflow.

This skill serves as the central orchestrator for submitting manuscripts to the Journal of Communication (JoC). It does not write content but guides the user through the entire academic lifecycle, from initial idea generation and literature positioning to final submission and rebuttal. It determines the appropriate content format (e.g., original article, Forum post) and routes the user to the specific, necessary sub-skills required at that stage, ensuring a structured and comprehensive academic workflow.

This guide details the methodology for packaging scientific artifacts (code, solvers, data, logs) for machine learning submissions. It focuses on creating double-blind, self-contained ZIP archives that allow reviewers to independently and quickly verify complex probabilistic claims, even under strict time constraints. It covers best practices for anonymity, structured README generation, and handling data/licensing.

An AI SDK specifically designed for Dart and Flutter applications. Genkit provides a unified interface for building sophisticated AI agents, defining complex data flows, and integrating various Large Language Models (LLMs) like Gemini, OpenAI, and Anthropic. Use it to manage code generation, structured outputs, tools, and complex operational workflows in Dart.

This core skill is designed for academic publishing, combining deep AI research with structured, professional writing. It guides the creation of manuscripts using IMRAD structure and handles complex citation styles (APA, AMA, etc.). Crucially, it mandates the generation of graphical abstracts and rich technical schematics to ensure the highest level of visual rigor for research papers.