Skill UI

This tool ingests raw market data (OHLCV) for a specified symbol. It normalizes the data into relative OHLCV vectors, converts these vectors into high-dimensional embeddings, and then stores and indexes them using HNSW. This process prepares the data for fast, similarity-based pattern detection and advanced financial time series analysis.

This tool manages RVF (Ruflo Vector Format) files, providing comprehensive functions for handling, transferring, and backing up agent memories. It supports listing, exporting, importing, migrating, and generating statistics for complex vector embeddings and associated metadata, ensuring knowledge portability across different projects and platforms.

This skill performs mean-variance portfolio optimization by solving the linear system Σ · x = μ using the Conjugate Gradient (CG) method. It is significantly faster (40-60x) and numerically robust compared to legacy methods like the Neumann series. It takes a covariance matrix (Σ) and expected return vector (μ) to determine optimal asset weights, providing full provenance tracking for auditability.

This skill helps discover natural groupings within a large collection of semantic embeddings using the `ruvector` tool. It employs graph-based algorithms (Spectral/Louvain) to cluster code or data, revealing underlying themes, identifying outliers, and organizing vast vector sets. Ideal for analyzing codebases or large datasets to understand relationships between entries.

A utility designed to generate high-dimensional vector embeddings (384-dim) from various sources including text, code, or documents. This process converts raw data into numerical representations optimized for semantic search, similarity comparison, and clustering, utilizing ONNX and HNSW indexing for efficient retrieval.

This skill utilizes hyperbolic space embedding (Poincare ball model) to represent complex data structures with inherent hierarchy, such as dependency trees, ontologies, or module taxonomies. Unlike standard Euclidean embeddings, hyperbolic space preserves hierarchical distances efficiently, allowing for accurate distance calculations (geodesic distance) that grow logarithmically with tree depth. It is ideal for advanced knowledge graph analysis and code architecture mapping.

This plugin provides advanced vector search capabilities using two distinct paths. The large-scale path supports millions of vectors via HNSW, ideal for general corpus search. The hot-path router handles small, critical pattern routing (≤11 patterns) with sub-ms latency. Key features include RaBitQ 1-bit quantization for memory efficiency (32× reduction), hyperbolic space search for hierarchical data, and unified cross-namespace search.

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 guide detailing the entire model lifecycle for RuView. It supports multiple advanced tracking methods including camera-free pose estimation (WiFlow), camera-supervised methods using MediaPipe, robust contrastive embeddings (RuVector), and domain generalization (MERIDIAN). Covers training, evaluation, and deployment across local GPU and cloud platforms.

This skill facilitates the persistent storage of critical brand knowledge, including campaign learnings, brand guidelines, and competitive intelligence, into a specialized memory layer (such as a vector database). It automatically indexes content, deduplicates entries via hashing, and applies rich metadata tagging, ensuring that valuable institutional knowledge is preserved and retrievable through natural language search across different sessions.

Performs advanced semantic search across all stored brand knowledge, including guidelines, campaign learnings, and competitor insights. By combining vector databases, knowledge graphs, and local indices, it provides comprehensive, context-aware answers to complex natural language questions, ensuring users make decisions based on the brand's entire historical knowledge base.

Provides comprehensive guidelines for designing self-contained, comparative, and publication-ready tables and figures for Comparative Political Studies (CPS) manuscripts. It emphasizes transforming raw statistical results into visually compelling exhibits that clearly communicate the cross-case or over-time comparative argument, while adhering to professional journal standards (e.g., grayscale legibility, vector output).