Skill UI

Use UMAP for fast nonlinear manifold reduction to produce embeddings that keep local and global structure, making it ideal for visualization, clustering preprocessing, supervised/parametric workflows, or high-dimensional feature engineering.

Umap provides uniform manifold approximation for dimension reduction and visualization, helping data scientists explore high-dimensional embeddings through interpretable 2D/3D layouts.

Comprehensive guide to LLM Operations (LLMOps). Covers advanced topics like Retrieval-Augmented Generation (RAG), vector database management (Pinecone, Chroma), embeddings, fine-tuning, prompt engineering, and cost optimization. Essential for bridging the gap between AI prototypes and reliable, scalable, production-grade AI systems.

A comprehensive technical module covering foundational deep learning principles and cutting-edge computer vision architectures, drawing from Yann LeCun's research. Topics include CNN fundamentals, backpropagation theory, and advanced self-supervised learning (SSL) models such as SimCLR, MAE, and the modern Joint Embedding Predictive Architecture (JEPA). Provides detailed theoretical explanations and complete PyTorch code for implementation.

This skill guides authorized security professionals through testing web applications for clickjacking vulnerabilities. It involves checking frame embedding controls (like X-Frame-Options and CSP) and crafting complex proof-of-concept overlays to see if users can be tricked into performing unintended sensitive actions, such as deleting accounts or transferring funds.

A powerful tool that converts complex Unified Modeling Language (UML) diagrams—such as Sequence, Class, Activity, and State diagrams—into plain text ASCII art. This is crucial for documentation in environments that do not support image embedding, such as terminal sessions, README files, or Git commit messages. Supports both standard ASCII and enhanced Unicode ASCII formats for better visual fidelity.

This skill automatically generates an interactive SQL Notebook designed to validate changes in dbt models or snapshots. It processes changes identified from a GitHub PR or local repository, generating comparison queries that compare 'before' (production) and 'after' (development) data states. It is essential for embedding data observability and quality checks into CI/CD pipelines.

Provides programmatic and standardized access to massive, versioned datasets of single-cell and spatial transcriptomics from CZ CELLxGENE. This skill allows users to query millions of cells, analyze gene expression, and access pre-calculated embeddings across various tissues, diseases, and species without downloading entire datasets. Essential for large-scale bioinformatics analysis, machine learning model training, and comprehensive cross-dataset comparison.

ESM provides advanced protein language models (ESM3, ESMC) for deep biological understanding, generation, and structural prediction. This skill enables users to generate novel protein sequences with specific functions, perform structure prediction from sequence, execute inverse folding (sequence design from structure), and extract high-quality protein embeddings for downstream machine learning tasks. Ideal for drug discovery and advanced bioinformatics research.

UMAP (Uniform Manifold Approximation and Projection) is a powerful technique for non-linear dimensionality reduction. This skill enables generating scalable and structure-preserving embeddings for visualization (2D/3D), clustering preprocessing, and feature engineering. It supports supervised and semi-supervised workflows, allowing users to guide the embedding process with labeled data while maintaining local and global data structure integrity.

A comprehensive guide providing minimal working examples for core Cohere API v2 endpoints, including Chat completion, text embedding, and search reranking. This resource details basic setup, usage patterns, and includes runnable code examples in both TypeScript and Python, making it perfect for new AI integrations or learning advanced LLM functionality.

This guide demonstrates how to build advanced AI agents using Anthropic's Tool Use (Function Calling) feature. It allows Claude to dynamically determine when external actions are needed, such as querying databases, calling APIs, or reading files. The pattern involves sending the user query, receiving a `tool_use` request, executing the corresponding function in your code, and submitting the result back to Claude for a final, coherent response. Essential for building multi-step, agentic workflows.