Skill UI

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 powerful tool utilizes Playwright to record high-quality, professional web application demo videos. It simulates natural user interactions, injecting a visible cursor overlay and adhering to specific pacing guidelines. It is ideal for creating comprehensive tutorials, feature walk-throughs, or documentation for stakeholders.

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.

A comprehensive reference for managing Uncloud, a decentralized self-hosting platform. This guide details using the `uc` CLI to deploy services, configure Caddy ingress, manage ports (HTTP/HTTPS/TCP/UDP), scale workloads, and inspect logs across a WireGuard mesh cluster. Ideal for setting up resilient, self-owned microservices infrastructure.

Design and implement a unified operational workflow for managing notifications sourced from disparate tools like GitHub, Linear, local hooks, and chat platforms. This skill addresses notification fragmentation, alert noise, and redundancy by establishing clear severity levels, ownership, routing channels, and next operational actions into a single, cohesive pipeline.

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.

This skill generates high-dimensional vector embeddings (384-dim) from text, code, or documents using the ruvector package. It utilizes the ONNX all-MiniLM-L6-v2 model, making it ideal for implementing advanced semantic search, similarity comparison, and data clustering. The resulting vectors are normalized and can be efficiently stored and indexed using HNSW, significantly enhancing retrieval accuracy in RAG and knowledge systems.

This skill enables the embedding of hierarchical datasets, such as dependency trees, ontologies, and module structures, into the Poincare ball model. Hyperbolic space is uniquely suited for capturing deep, non-linear relationships, allowing the preserved geodesic distance to accurately reflect structural depth and similarity within a hierarchy. Ideal for analyzing code architecture, complex knowledge graphs, and taxonomy.

A comprehensive vector search plugin offering two distinct paths: large-scale corpus search using HNSW (supporting RaBitQ quantization for memory efficiency) and a fast WASM-backed router for high-priority hot patterns. Ideal for implementing Retrieval-Augmented Generation (RAG), semantic search across massive datasets, or routing queries to specific, limited patterns.

This skill automates the comprehensive setup and bootstrapping of the ruvector framework. It ensures all necessary optional add-ons—including ONNX, Brain, and LLM components—are installed. Furthermore, it registers the required MCP server and runs a full battery of diagnostics to verify the entire toolchain is functional and ready for immediate use.

A comprehensive, multi-stage skill designed to simulate a professional Continuous Integration (CI) pipeline. It guides users through critical quality gates, including build verification, type checking, linting, running test suites (with coverage checks), security scanning for secrets, and final diff review. Use this skill before creating a Pull Request or finalizing any significant feature to ensure code robustness and adherence to best practices.