Skill UI

This skill provides a structured framework for managing complex, multi-agent development tasks. It treats agents as a collaborative team, utilizing structured work items, defined ownership, Agent Kanban boards, and explicit merge gates. This ensures that large-scale, multi-tool, or cross-branch development efforts are predictable, highly visible, and result in reliably mergeable, production-ready artifacts.

An interactive tool designed to discover, select, and assemble specialized teams of AI agents. It supports structured agent collections across different domains (e.g., Engineering, Marketing). Users select multiple roles for a specific task, and the skill launches them in parallel, synthesizing their independent findings into a unified report, including consensus and conflict analysis.

This skill guides users through evidence-based terminal operations. It is designed for scenarios requiring direct command execution, Git status checks, debugging CI/build failures, and pushing narrow, verified fixes. It emphasizes following a structured, minimal-scope workflow and accurately reporting the repository's current state (e.g., local changes vs. pushed code).

This guide details the architectural and implementation patterns for building modular applications or microservices using the tinystruct Java framework. It covers core concepts like using the @Action annotation for dual routing (CLI/HTTP), managing request state with Context, and using native Builder for JSON serialization, ensuring a lightweight and structured development environment.

This skill provides a comprehensive workflow for training advanced neural network models, including LSTM, Transformer, and N-BEATS, using historical market data. It facilitates model training, prediction generation with confidence intervals, performance comparison across different model types, and the structured storage of results for further analysis and SONA training.

Tree Ring Memory is a local-first, framework-agnostic layer for managing durable knowledge in AI agents. It provides structured methods for recalling past decisions, recording evidence, auditing project state, and deliberately forgetting information, ensuring the core project memory remains clean and actionable without relying on raw conversation transcripts.

This tool automatically converts design screenshots or visual exports into structured, functional Vue 3 Composition API components. It supports popular UI libraries like Vant, Element Plus, and Ant Design Vue. By batch converting screenshots, it streamlines the development process from raw design assets to reusable, production-ready code, significantly boosting frontend development efficiency.

Provides a structured workflow for systematically accessing and analyzing official intellectual property data from the USPTO. This skill covers patent search (PatentSearch API), trademark status checks (TSDR), ownership transfer tracking, and building reproducible IP landscape logs. It is designed for data collection and record verification, emphasizing the use of official APIs and rigorous logging practices. (Warning: Not for legal advice.)

This skill clusters a collection of vectors (embeddings, e.g., code snippets) within a designated namespace using graph community detection algorithms (Spectral/Louvain). It is essential for discovering underlying themes, identifying natural groupings, and analyzing the relationships within large, unstructured vector datasets. Use it when you need to organize embeddings based on semantic relatedness rather than simple keyword matching.

A comprehensive tool for authoring and managing complex workflows. It supports two primary methods: persistent MCP templates for long-lived, human-gated, resumable pipelines, and native JavaScript scripts for advanced, code-driven orchestration, allowing for complex fan-out, parallel processing, and structured data aggregation.

This guide assists developers in creating a systematic QA plan for RevenueCat integration. It provides a structured approach to verifying the entire subscription lifecycle, including initial purchases, renewal, upgrades, and cancellations. The core principle is linking tests to specific, verifiable dashboard signals, ensuring robust and reliable in-app monetization setups.

This skill is designed to review Chinese skill documentation to ensure it meets publishing standards. It systematically checks the clarity of descriptions and the executability of process steps, providing structured feedback in Chinese while maintaining necessary machine enumeration values for technical integrity.