> browse/registry

> together-ai

Cloud platform for running open-source AI models. Provides inference APIs for LLMs, image models, and embedding models. Supports fine-tuning on custom data, OpenAI-compatible API format, and competitive pricing for open-source model hosting.

> trieve

Expert guidance for Trieve, the all-in-one search infrastructure that combines full-text, semantic, and hybrid search with built-in RAG capabilities. Helps developers implement production search with chunking, re-ranking, recommendations, and analytics without managing vector databases or embedding models.

> unusual-whales-api

Query the Unusual Whales API for unusual options flow, dark pool prints, market tide sentiment, gamma exposure, congressional trading, and stock greeks. Use when someone asks about "unusual options activity", "whale trades", "dark pool prints", "market sentiment", "gamma exposure", "GEX", "congressional trading", "insider trading", or needs real-time and historical market data for trading analysis and AI agent integration.

> v0-dev

Generate UI components with v0.dev, the AI-powered UI generator by Vercel. Use when a user asks to prototype React components, generate shadcn/ui layouts, create landing pages, or scaffold UI from natural language prompts.

> lancedb

Embedded vector database with LanceDB — serverless, zero-config vector search for AI applications. Use when someone asks to "vector search without a server", "embedded vector database", "LanceDB", "local vector search", "serverless vector DB", "vector search in a file", or "lightweight RAG storage". Covers table creation, vector search, full-text search, hybrid search, and multimodal embeddings.

> arize

You are an expert in Arize and its open-source Phoenix library for AI observability. You help developers monitor LLM applications with tracing, evaluation, embedding analysis, drift detection, and retrieval quality metrics — using Phoenix for local development (open-source, self-hosted) and Arize platform for production monitoring at scale.

> reddit-insights

Semantic search across Reddit to find pain points, validate ideas, discover niches, and analyze sentiment. Use when a user asks to research Reddit, find Reddit discussions about a topic, analyze Reddit sentiment, discover what people complain about on Reddit, validate a business idea on Reddit, find pain points in a subreddit, or mine Reddit for product insights.

> gtars

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

> sentiment-analysis

Analyze user feedback data to identify segments with sentiment scores, JTBD, and product satisfaction insights. Use when analyzing user feedback at scale, running sentiment analysis on reviews or surveys, or identifying satisfaction patterns.

> dspy

Build complex AI systems with declarative programming, optimize prompts automatically, create modular RAG systems and agents with DSPy - Stanford NLP's framework for systematic LM programming

> alphaear-sentiment

Analyze finance text sentiment using FinBERT or LLM. Use when the user needs to determine the sentiment (positive/negative/neutral) and score of financial text markets.

> long-context

Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.

> customer-support

Elite AI-powered customer support specialist mastering conversational AI, automated ticketing, sentiment analysis, and omnichannel support experiences. Integrates modern support tools, chatbot platforms, and CX optimization with 2024/2025 best practices. Use PROACTIVELY for comprehensive customer experience management.

> embedding-strategies

Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.

> azure-ai-openai-dotnet

Azure OpenAI SDK for .NET. Client library for Azure OpenAI and OpenAI services. Use for chat completions, embeddings, image generation, audio transcription, and assistants. Triggers: "Azure OpenAI", "AzureOpenAIClient", "ChatClient", "chat completions .NET", "GPT-4", "embeddings", "DALL-E", "Whisper", "OpenAI .NET".

> chroma

Open-source embedding database for AI applications. Store embeddings and metadata, perform vector and full-text search, filter by metadata. Simple 4-function API. Scales from notebooks to production clusters. Use for semantic search, RAG applications, or document retrieval. Best for local development and open-source projects.

> analyzing-text-sentiment

This skill enables Claude to analyze the sentiment of text data. It identifies the emotional tone expressed in text, classifying it as positive, negative, or neutral. Use this skill when a user requests sentiment analysis, opinion mining, or emotion detection on any text, such as customer reviews, social media posts, or survey responses. Trigger words include "sentiment analysis", "analyze sentiment", "opinion mining", "emotion detection", and "polarity".

> sentencepiece

Language-independent tokenizer treating text as raw Unicode. Supports BPE and Unigram algorithms. Fast (50k sentences/sec), lightweight (6MB memory), deterministic vocabulary. Used by T5, ALBERT, XLNet, mBART. Train on raw text without pre-tokenization. Use when you need multilingual support, CJK languages, or reproducible tokenization.

> axiom-uikit-bridging

Use when wrapping UIKit views/controllers in SwiftUI, embedding SwiftUI in UIKit, or debugging UIKit-SwiftUI interop issues. Covers UIViewRepresentable, UIViewControllerRepresentable, UIHostingController, UIHostingConfiguration, coordinators, lifecycle, state binding, memory management.

> excalidraw-studio

Generate Excalidraw diagrams from natural language descriptions. Outputs .excalidraw JSON files openable in Excalidraw. Use when asked to "create a diagram", "make a flowchart", "visualize a process", "draw a system architecture", "create a mind map", "generate an Excalidraw file", "draw an ER diagram", "create a sequence diagram", or "make a class diagram". Supports flowcharts, relationship diagrams, mind maps, architecture, DFD, swimlane, class, sequence, and ER diagrams. Can use icon librarie