Browse skills

This skill generates vector embeddings from text, enabling semantic similarity searches and powering AI applications needing contextual understanding.

This skill generates high-quality embeddings for text using Facebook's (FT) transformer model, boosting semantic understanding and similarity search.

Quickly retrieve relevant documents from large datasets using semantic similarity search powered by vector embeddings.

Generates clean, semantic HTML & CSS code from natural language descriptions, ideal for rapid prototyping and web design.

Generates clean, semantic CSS code snippets from natural language descriptions, streamlining frontend development workflows.

Quickly locate code blocks related to rust integration within a codebase using intelligent semantic search.

Swiftdata generates clean, semantic HTML from JSON data, streamlining frontend development and improving data presentation efficiency.

Automatically adds semantic HTML and ARIA attributes to Svelte components for improved accessibility.

This skill generates vector embeddings of text, enabling semantic similarity searches and powering advanced AI applications like recommendation systems.

Automates Git tag creation and release branching based on semantic versioning rules and predefined workflows.

Generates clean, semantic Laravel Blade UI components from descriptions, accelerating development and ensuring consistency.

Refactors markdown documents into cleaner, more structured formats using advanced parsing and semantic understanding.

Find relevant documents or data points based on semantic similarity using vector embeddings powered by winsorllc.

This skill leverages vector databases to efficiently store and retrieve semantic similarities between data, enabling powerful search & retrieval tasks.

This Embedding Service converts text into numerical representations (embeddings) for semantic search and machine learning tasks, boosting AI performance.

Together Embeddings generates high-quality vector representations of text data for semantic search and AI applications, boosting performance & understanding.

This skill generates CLIP-aware embeddings for images and text, enabling powerful semantic search and similarity matching across diverse content.

Ontopo extracts structured knowledge (triples) from unstructured text, enabling semantic understanding and reasoning for AI applications.

Semantic memory search using Qdrant + Ollama embeddings (QRAS). Use as primary method for recalling workspace files, notes, decisions, and prior work before falling back to built-in search.

Cocoindex enables semantic search across your documents by indexing content for faster, more relevant retrieval – boosting productivity and knowledge discovery.

Stores and retrieves vector embeddings in a Qdrant database, enabling semantic search and similarity analysis.

21 production-ready scripts for iOS app testing, building, and automation. Provides semantic UI navigation, build automation, accessibility testing, and simulator lifecycle management. Optimized for A

Generates vector representations (embeddings) of text using Gemini, enabling semantic search and similarity analysis for powerful AI applications.

This skill automatically converts documents into vector datasets for efficient semantic search and AI model training.