Skills DirectoryData & Analytics
Data analysis, BI, visualization, datasets, statistics, and ML workflows
Browse data & analytics skills
Showing 145–168 of 12,865 skills
How to test and debug Feast — running targeted tests, writing unit tests for new components, debugging registry and online store issues, and inspecting live feature store state. Use when writing tests for a new feature, debugging a failing test, investigating a runtime error, or verifying that a change works correctly end-to-end.
Development guide for contributing to the Feast codebase. Covers environment setup, testing, linting, project structure, and PR workflow for feast-dev/feast.
Internals of the Feast codebase — how each component works, where the key abstractions live, and the data flow through the system. Use when asked how feast apply works, how the registry stores data, how materialization moves data, how get_online_features retrieves features, how the feature server works, how the Kubernetes operator manages deployments, or when navigating the codebase to understand where to make a change.
Guide for working with Feast (Feature Store) — defining features, configuring feature_store.yaml, retrieving features online/offline, using the CLI, and building RAG retrieval pipelines. Use when the user asks about creating entities, feature views, on-demand feature views, stream feature views, feature services, data sources, feature_store.yaml configuration, feast apply/materialize commands, online or historical feature retrieval, or vector-based document retrieval with Feast.
Guide for working with Feast (Feature Store) — defining features, configuring feature_store.yaml, retrieving features online/offline, using the CLI, and building RAG retrieval pipelines. Use when the user asks about creating entities, feature views, on-demand feature views, stream feature views, feature services, data sources, feature_store.yaml configuration, feast apply/materialize commands, online or historical feature retrieval, or vector-based document retrieval with Feast.
How to test and debug Feast — running targeted tests, writing unit tests for new components, debugging registry and online store issues, and inspecting live feature store state. Use when writing tests for a new feature, debugging a failing test, investigating a runtime error, or verifying that a change works correctly end-to-end.
Development guide for contributing to the Feast codebase. Covers environment setup, testing, linting, project structure, and PR workflow for feast-dev/feast.
Internals of the Feast codebase — how each component works, where the key abstractions live, and the data flow through the system. Use when asked how feast apply works, how the registry stores data, how materialization moves data, how get_online_features retrieves features, how the feature server works, how the Kubernetes operator manages deployments, or when navigating the codebase to understand where to make a change.
Data-driven severity classification for smart contract audit findings with statistical breakdowns and 30 representative examples per level from top audit firms. Use when assigning severity to findings, justifying classifications with historical data, or calibrating severity judgment against Code4rena, Sherlock, and Cyfrin benchmarks.
Pure-Python knowledge base statistics dashboard. Reads shelf-index and log.md; emits Inventory, Layer distribution, Domain distribution, Recent Activity, and Staleness sections. No agent dispatch. Read-only.
WildWorld large-scale action-conditioned world modeling dataset with 108M+ frames from a photorealistic ARPG game, featuring per-frame annotations, 450+ actions, and explicit state information for generative world modeling research.
AI-powered analysis of Trump's social media posts to predict stock market movements using 31.5M brute-force tested rules
Use TRIBE v2, Meta's multimodal foundation model for predicting fMRI brain responses to video, audio, and text stimuli
> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.
Expertise in See-through, a framework for single-image layer decomposition of anime characters into manipulatable 2.5D PSD files using diffusion models.
Minimalist batteries-included repository for training, evaluating, and deploying diffusion-forcing video world models for robot manipulation, gaming, and MPC planning.
Generate high-quality 3D human and humanoid robot motions using Kimodo, a kinematic motion diffusion model controlled via text prompts and kinematic constraints.
> Skill by [ara.so](https://ara.so) — Daily 2026 Skills collection.
Search arXiv papers by keyword, author, category, or ID.
Import datasets from HuggingFace and convert them to Coval test sets. Use when the user wants to create test cases from HuggingFace dataset or repository.
Build or improve a Coval dashboard with metric visualizations backed by real data. Creates new dashboards from scratch or rebuilds existing ones by analyzing usage patterns, metric frequency, and data density. Use when user says "create a dashboard", "build a dashboard", "improve my dashboard", "add widgets", "visualize my metrics", "make a performance dashboard", or "dashboard for my runs".
Trago cada negócio aberto do seu CRM, classifico de acordo com os critérios de saída de estágio do seu playbook em Commit / Best / Pipeline / Omit, somo a receita anual por categoria, e comparo com o forecast da semana passada para marcar qualquer atraso. A confiança de cada negócio é o mínimo entre o avanço de estágio, o quanto a qualificação está completa, e o quanto o plano de fechamento está completo, sem achismo.
Configure o monitoramento operacional que você precisa para não voar às cegas. Escolha o que você precisa: uma única métrica que eu capturo diariamente no seu warehouse, ou uma especificação completa de dashboard com seções, visualizações, cadência e SQL somente leitura por trás de cada gráfico. Eu redijo a especificação, você ou sua ferramenta de BI a renderiza.
Marco seu gasto qualificado de P&D para dar suporte à Seção 174 e ao crédito federal de P&D. Agrupo o gasto nas quatro categorias do IRS (salários qualificados por função do funcionário e proporção de tempo, suprimentos, locação de nuvem / computadores, pesquisa contratada a 65%), aloco entre seus projetos (ou um único grupo 'P&D não alocado' se não houver lista de projetos), e sinalizo as exclusões típicas (correções pós-lançamento, análises de rotina, pesquisa financiada por terceiros). Ape...