Skill UI

Guides network operators through creating, tuning, and validating RFC 8805-compliant CSV geofeeds with opinionated best practices that improve accuracy, completeness, and usability for publicly routable IP space.

A comprehensive guide to optimizing website content for modern AI search systems, including ChatGPT, Perplexity, and Google AI Overviews. It covers structural improvements, maximizing citability scores, incorporating multi-modal elements, building brand authority, and implementing technical standards like llms.txt and proper server-side rendering (SSR). Essential for maximizing organic reach in the LLM era.

A structured methodology for precisely identifying specific geographic locations or historical landmarks. This technique systematically narrows the search scope, moving from broad regions (countries/former states) down through administrative divisions, city centers, and finally to specific monuments or structures. It requires utilizing diverse clues like historical events, census data, and distance constraints, while critically verifying assumptions at each step to ensure accuracy.

This technique teaches the user how to pinpoint an exact location by cross-referencing multiple unique surrounding features, such as specific types of businesses (e.g., law firms, restaurants) or natural landmarks (e.g., rivers, mountains). Instead of relying on a single clue, the power lies in the combination of these details, which often creates a unique geographic signature to narrow down the general area to the precise spot.

This skill guides users on how to search for physical monuments, memorials, or landmarks by linking them to their originating historical events. The core technique involves treating the historical event (e.g., a battle, a protest, a significant date) as the primary search anchor, and then locating the associated memorial or structure. It provides structured query templates and anti-patterns to ensure accurate, context-aware geographical and historical information retrieval.

This technique guides users in solving trivia or puzzle questions where the target band's country is not explicitly named. Instead, the country is hinted at through indirect geographic clues such as land area ranges, EU membership dates, or national flag descriptions. It emphasizes systematic deduction, requiring the user to treat the clue as a multi-candidate puzzle and test all viable countries before refining the search to the specific band name.

This guide teaches advanced methodology for identifying target species using multiple biological features. Users learn to combine distinct clues such as habitat, morphology, behavior, and geographic range. A key technique involves first identifying the country through demographic clues (e.g., population growth, endemic status) to significantly narrow the search scope before searching for the specific species.

A comprehensive toolkit for advanced geospatial analysis. It covers remote sensing (Sentinel, Landsat), GIS operations (vector/raster/point cloud), spatial statistics, and Machine Learning for Earth Observation. Supports cloud-native workflows, multiple data types, and 8 programming languages, making it ideal for complex scientific research and commercial spatial projects.

A powerful Python library that extends pandas to enable complex spatial operations on vector geometric data. It supports reading and writing diverse formats, including Shapefile, GeoJSON, and GeoPackage. Users can perform advanced GIS tasks such as spatial joins, buffering, overlay analysis, calculating areas, managing Coordinate Reference Systems (CRS), and generating interactive choropleth maps, making it essential for geographic data science projects.

An expert skill for transforming CPU-bound Python code into highly performant GPU-accelerated code using NVIDIA technologies. It specializes in optimizing scientific workflows, including machine learning (ML), physics simulations, geospatial analysis, and large-scale array computations. Utilize tools like CuPy for array operations, Numba CUDA for custom kernels, and Warp for spatial computing and differentiable simulations, achieving dramatic speedups (10x to 1000x). Use this when dealing with NumPy, pandas, SciPy, or complex computational loops that benefit from parallel processing on NVI...

PyTorch Geometric (PyG) is the definitive standard library for building Graph Neural Networks (GNNs) atop PyTorch. It provides specialized data structures for handling diverse graph types, including heterogeneous graphs. The library offers over 60 GNN layer implementations (such as GCN, GAT, GraphSAGE), full support for dataset loading (Planetoid, TUDataset, OGB), and necessary data transformations. It is essential for deep learning tasks involving node classification, graph classification, and link prediction.

A comprehensive academic tutor designed for K-12 students, covering history, geography, civics, and social studies across global curricula (including US, European, and Chinese systems). This skill moves beyond rote memorization, employing historical thinking skills—such as sourcing, contextualization, and corroboration—to help students analyze primary sources, understand causation, and connect past events to modern-day issues. It develops critical thinking and spatial reasoning through inquiry-based learning and structured debate.