Skill UI

A comprehensive guide for training or fine-tuning advanced sentence-transformer models. This skill assists in selecting the correct architecture—including Bi-encoders (SentenceTransformer), CrossEncoders (for reranking), and SparseEncoders—for various Natural Language Processing (NLP) tasks. Use cases include semantic retrieval, similarity matching, document clustering, and pair-wise classification.

A technical specialization focused on Generative Engine Optimization (GEO). This skill ensures that specific web pages are structured, documented, and optimized for modern AI answer engines (Google AI Overviews, ChatGPT, Bing Copilot). It focuses on technical elements like structured data (JSON-LD), semantic HTML, and robust metadata to maximize discoverability, trustworthiness, and quotability for AI consumption, rather than traditional SEO ranking.

The /goal command converts standard agent prompts into persistent, self-correcting loops (plan → act → test → review → iterate). It is designed for long-running, complex engineering tasks like migrations, coverage lifts, or refactoring, ensuring progress is tracked against a verifiable stop condition and mandated documentation.

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.