Skill UI

Chroma is an open-source, AI-native embedding database designed for building sophisticated LLM applications. It allows users to store high-dimensional embeddings and associated metadata, facilitating powerful vector search and full-text filtering. Ideal for implementing Retrieval-Augmented Generation (RAG) systems, semantic search, and document retrieval in local or self-hosted environments, making it perfect for open-source AI projects and prototyping.

Configure project-specific `.cursorrules` files to guide Cursor IDE’s AI code generation, enforce style, and supply contextual metadata for consistent suggestions.

Provides a minimal Langfuse trace example that initializes the client, records spans and generations, flushes data, and logs a trace URL—ideal when starting integrations, validating setup, or learning trace patterns.

nanoGPT is a simplified, minimalist implementation designed specifically for educational purposes. It reproduces the core architecture of GPT-2 (124M) using clean, hackable code, allowing users to understand the entire transformer pipeline from scratch. The framework supports full workflows, including data preparation on various datasets (e.g., Shakespeare, OpenWebText), model training, and text generation, making it ideal for students and researchers learning NLP and deep learning principles.

Qdrant is a high-performance, Rust-powered vector database designed for production-grade RAG (Retrieval-Augmented Generation) and semantic search applications. It provides fast nearest neighbor search, supports complex hybrid search using metadata filtering, and offers horizontal scalability through sharding and replication. Ideal for building real-time recommendation systems and enterprise-level knowledge retrieval.

Provides Meta AI’s Segment Anything Model for zero-shot image segmentation via interactive prompts or automatic mask generation, enabling rapid dataset creation and integration in vision pipelines.

This skill provides comprehensive patterns and best practices for implementing efficient similarity search mechanisms using vector databases. It is essential for building advanced AI systems such as semantic search engines, implementing Retrieval-Augmented Generation (RAG), and developing recommendation systems. Use this when optimizing retrieval performance or scaling to massive volumes of vectors.

Guides post-training of GLM-class LLMs with Megatron-LM and SGLang, focusing on GRPO rollout generation, custom data buffers, and tight RL scaling workflows for research and production use.

Hypogenic automates LLM-driven hypothesis generation and testing over tabular datasets, blending literature insights with empirical analysis to accelerate discovery in domains like deception detection or AI content evaluation.

Provides AI-ready drug discovery datasets, splits, and benchmarks covering ADME, toxicity, DTI, DDI, and molecular generation so researchers can train, compare, and optimize therapeutic ML models with curated splits and oracles.

A comprehensive skill for leveraging massive pre-trained transformer models across diverse AI domains, including Natural Language Processing (NLP), Computer Vision (CV), and Audio. Use this skill for advanced tasks such as text generation, classification, question answering, summarization, object detection, and model fine-tuning on proprietary datasets. It provides streamlined pipelines and granular control for deployment.