Skill UI

A comprehensive skill for generating multimodal media content using fal.ai models. It supports text-to-image (Nano Banana), text/image-to-video (Veo, Kling, Seedance), and text/video-to-audio (CSM-1B, ThinkSound). Use this tool for any creative project requiring high-quality generated images, videos, or sound effects.

This skill guides the operation, consolidation, and maintenance of complex working systems within Google Workspace (Docs, Sheets, Slides). It emphasizes treating files as integrated systems—such as trackers or planning hubs—requiring careful inspection, precise edits, and structural cleanup rather than simple content rewriting. Use it for consolidating scattered data, cleaning shared spreadsheets, or ensuring the correct canonical version of a document is utilized.

This pattern is designed to solve context limitations in complex multi-agent workflows. Instead of relying on a single, massive context dump, it implements a structured, multi-cycle loop (Dispatch -> Evaluate -> Refine -> Loop). This process iteratively refines the search query by scoring content relevance and identifying missing context gaps, ensuring sub-agents gather the most targeted and highly relevant information needed for tasks like debugging or feature implementation.

Manim is a powerful Python library used for creating high-quality mathematical and scientific animations. This skill facilitates the generation of educational videos and complex visualizations for presentations. It is ideal for researchers, educators, or content creators who need to visualize complex concepts in an engaging animated format. Note: This specific file is related to localization/translation.

A comprehensive framework for planning and executing multi-channel product launch campaigns. This skill guides users through audience research, positioning definition, content strategy (landing pages, emails, social, video), and rigorous review gates to ensure every asset drives conversion and maintains brand consistency across all platforms. Ideal for orchestrating major product releases.

This file serves as the dedicated translation resource for the MCP Server Patterns library. It is used to localize key strings and texts to ensure accurate user experience across different languages. Developers should refer to the original skill file for the core English content.

This skill is intended for managing the Japanese translation and localization of the core nanoclaw-repl module. The current content serves as a placeholder, indicating that the Japanese translation is actively under development. Users are advised to refer to the original English source file for the full and functional version of the skill.

This skill is intended for Perl testing functionalities. Currently, the Japanese translation and content are under development, serving as a placeholder. Users should refer to the original English skill file for the core functionality and detailed implementation.

This skill file acts as a Japanese translation placeholder for the 'product-capability' module. It indicates that the localized content is currently under development and not functional. Users should refer to the original English skill file for the core functionality and detailed implementation.

This skill is designed for conducting comprehensive production audits. It helps users systematically check processes, identify quality gaps, and ensure compliance standards are met in a production environment. (Note: The provided content is a translation placeholder.)

This skill provides a structured framework for designing and implementing composable recommendation, ranking, and feed pipelines. It models the industry-standard six-stage pattern: Source, Hydrator, Filter, Scorer, Selector, and SideEffect. Use it for any system that selects "the top K items for a (user, context)", such as social feeds, ad ranking, RAG rerankers, or content recommendation engines, ensuring a robust and scalable architecture.

This tool provides comprehensive safety scanning for untrusted inputs, such as user submissions, API payloads, and webhook data. It actively detects various security threats, including prompt injection, jailbreak attempts, instruction override attacks, and unsafe content. Beyond basic checks, it offers deep analysis, full multi-layer scanning, and even capabilities to train defensive models using detected threats. Essential for secure AI application development.