Skill UI

This skill creates complete specifications (requirements, design, tasks) for all features in a roadmap using parallel sub-agent dispatch organized by dependency waves. It ensures dependency ordering, cross-spec consistency, and parallel processing for efficient development planning.

A comprehensive checklist and implementation guide for utilizing RunwayML's API for advanced AI video generation and image editing. This skill provides the Python SDK pattern, covering tasks like image-to-video creation, model selection, and waiting for task completion. It also includes critical error handling for common issues (e.g., unauthorized access or insufficient credits), making it essential for developers building production-level creative AI applications.

A comprehensive workflow guide for fine-tuning open-source Large Language Models (LLMs) using the Together AI API. This process covers preparing structured training data (JSONL format), uploading the file, initiating the fine-tuning job, monitoring the training status, and finally deploying the custom model for inference. Ideal for developers needing to customize models for specific enterprise tasks, such as customer support or specialized content generation.

This guide details how to extend the Webiny CLI by adding custom commands using the `CliCommandFactory` pattern. Developers can define command structures, handle typed parameters, and implement custom logic for various tasks, including data migration, code generation, deployment scripting, or system health checks. It covers command definition, parameter handling, and formatted terminal output using the `Ui` service.

The RunAPI CLI serves as the dedicated execution layer for interacting with RunAPI's suite of advanced AI models. It allows developers to programmatically generate multimodal content, including images, videos, and music/audio, directly from the terminal or CI/CD pipeline. Users can submit one-off jobs, manage asynchronous tasks, and pass complex JSON request bodies to various services like Suno, Claude, and Gemini, making it essential for robust AI automation workflows.

This skill provides a generic entry point for delegating self-contained, complex tasks to various LLM-powered sub-agents (Codex, Claude Code, Pi). It is designed to be used in meta-skill DAG steps for tasks like feature creation, PR reviews, code refactoring, or document generation, providing full tool surface access. It favors non-interactive CLI modes for reliable background execution.

This skill enforces a high standard for all generated code, prioritizing correctness, clarity, and idiomatic patterns across all languages. It helps minimize code bloat by removing unnecessary abstractions and verbose patterns, while also optimizing the agent's output by using targeted diffs and eliminating excessive conversational overhead. Use this for all code generation, refactoring, or review tasks.

Use this comprehensive skill for interacting with the Gemini API. It supports a wide range of advanced AI tasks, including text generation, multi-turn chat, multimodal understanding, image/video creation, function calling, and structured output. Provides modern SDKs for robust application development in Python and JavaScript/TypeScript.

This skill enables deterministic programmatic PDF operations including text and table extraction, merging and splitting pages, filling form fields, and generating new PDFs from JSON data. It utilizes libraries like pypdf, pdfplumber, and reportlab to handle structured PDF work efficiently. Ideal for automating document processing tasks such as report analysis, form completion, and document consolidation within a Python environment.

This skill allows you to delegate bounded coding tasks to the Google Antigravity CLI (agy) while retaining review and landing responsibility. You write the brief, dispatch the task, wait for completion, review the generated diff against gates, and commit the changes yourself. It requires the agy CLI installed and authenticated, suitable for orchestrators managing code generation workflows.

The RunAPI CLI serves as the dedicated execution layer for interacting with RunAPI's suite of advanced AI models. It allows developers to programmatically generate multimodal content, including images, videos, and music/audio, directly from the terminal or CI/CD pipeline. Users can submit one-off jobs, manage asynchronous tasks, and pass complex JSON request bodies to various services like Suno, Claude, and Gemini, making it essential for robust AI automation workflows.

This skill enforces a high standard for all generated code, prioritizing correctness, clarity, and idiomatic patterns across all languages. It helps minimize code bloat by removing unnecessary abstractions and verbose patterns, while also optimizing the agent's output by using targeted diffs and eliminating excessive conversational overhead. Use this for all code generation, refactoring, or review tasks.