Loading...
Loading...
Found 19 Skills
Debug distributed training failures (NeMo, Megatron, PyTorch) from worker stderr logs and optional AIStore daemon logs. Finds root cause across NCCL timeouts, data loading errors, and storage failures.
TanStack Router best practices for type-safe routing, file-based routing, data loading, search params, and navigation in React. Use when building React SPAs with complex routing, implementing type-safe search params, setting up route loaders, integrating with TanStack Query, or configuring code splitting and preloading.
Load automatically when planning, researching, or implementing Medusa Admin dashboard UI (widgets, custom pages, forms, tables, data loading, navigation). REQUIRED for all admin UI work in ALL modes (planning, implementation, exploration). Contains design patterns, component usage, and data loading patterns that MCP servers don't provide.
Reviews React Router code for proper data loading, mutations, error handling, and navigation patterns. Use when reviewing React Router v6.4+ code, loaders, actions, or navigation logic.
Apply React Router 7 framework mode best practices including server-first data fetching, type-safe loaders/actions, proper hydration strategies, middleware authentication, handle metadata, useMatches/useRouteLoaderData hooks, and maximum type safety. Use when working with React Router 7 framework mode, implementing loaders, actions, route protection, breadcrumbs, streaming with Suspense/Await, URL search params, form validation, optimistic UI, resource routes (API endpoints), route configuration, or building SSR applications.
Implement HTTP data fetching in Angular v20+ using resource(), httpResource(), and HttpClient. Use for API calls, data loading with signals, request/response handling, and interceptors. Triggers on data fetching, API integration, loading states, error handling, or converting Observable-based HTTP to signal-based patterns.
TanStack Router best practices for type-safe routing, data loading, search params, and navigation. Activate when building React applications with complex routing needs.
Type-safe, file-based router for React with first-class search params, data loading, and code splitting. Use when user asks to "create routes with TanStack Router", "set up file-based routing", "add search params", "use loaders", "protect routes with auth", "add code splitting", or asks about @tanstack/react-router, createFileRoute, createRouter, routeTree.gen.ts, useSearch, useParams, useNavigate, useBlocker, useMatch, useRouterState, beforeLoad, or route configuration. Do NOT use for TanStack Start server functions, Next.js App Router, React Router (without migration context), or Remix routing. Covers routing setup, navigation, search/path params, data loading, authentication, code splitting, SSR, error handling, testing, deployment, and bundler configuration (Vite, Webpack, Rspack, esbuild).
Type-safe routing for React and Solid applications with first-class search params, data loading, and seamless integration with the React ecosystem.
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
This skill should be used when the user asks to "query BigQuery with Python", "use the google-cloud-bigquery SDK", "load data into BigQuery", "define a BigQuery schema", or needs guidance on best practices for the Python BigQuery client library.
Explore and query any dataset annotated with a Frictionless Data Package descriptor (datapackage.json). Use this skill whenever a user wants to discover what tables or resources a dataset contains, look up column names and descriptions, surface usage warnings embedded in metadata, or understand how to load data from Parquet files, DuckDB or SQLite databases, or CSV files described by a datapackage.json. Also use when the user has a datapackage.json and wants to know what's in it, how to query it efficiently, or how to connect its metadata to actual data files. Pairs well with dataset-specific skills (like `pudl`) that layer domain knowledge on top.