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Trace where BigQuery and Cloud Storage data comes from and where it goes. The skill summarizes table or column lineage, meaning data origins and movement, in a readable Markdown report to support data quality investigations.
Author, validate, and test Power Query M expressions in semantic model partitions. Automatically invoke when the user mentions "Power Query", "M code", "M expression", "partition expression", "query folding", or asks to "write Power Query", "fix Power Query", "test a partition", "preview partition data", "debug Power Query step", "optimize Power Query".
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Manages datasets, tables, and jobs in BigQuery, and integrates with BigQuery ML and Gemini for advanced data analytics and AI-driven insights. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources, or leverage BigQuery's built-in ML capabilities. Also use when performing data analysis, ingesting data into BigQuery, or developing AI applications on BigQuery.
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Integrating TanStack Router with TanStack Query: queryClient in router context, ensureQueryData/prefetchQuery in loaders, useSuspenseQuery in components, defaultPreloadStaleTime: 0, setupRouterSsrQueryIntegration for SSR dehydration/hydration and streaming, per-request QueryClient isolation.
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Create a BigQuery materialization to stream Estuary collections into BigQuery tables. Use when setting up BigQuery as a destination for captured data. Use when user says "send to BigQuery", "load into BigQuery", "materialize to BigQuery", or "BigQuery destination".
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Kubb (OpenAPI / Swagger コードジェネレーター) リファレンス。 kubb.config.ts、TypeScript 型・TanStack Query フック・Zod・Faker・MSW モック生成プラグイン、 Fetch / Axios / SWR / React Query クライアント、plugin-client (自前クライアント)。 CLI 8 コマンド、拡張レジストリ (kubb add)、Kubb Studio、Kit (kubb/kit)、 parser-ts / parser-md、adapter-oas、diagnostics、プラグインベース、モノレポ対応。 TanStack Query 本体の API は tanstack-query スキル。
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Create and render Table v9 tables in Svelte 5. Covers basic setup and directs work on reusable components, TanStack Query data integration, and TanStack Virtual rendering, which limits displayed rows or columns for large tables.
Build Table v9 tables in Angular using injectTable and FlexRender. Guidance covers signal-driven data updates, reusable table components, and connections to TanStack Query for data fetching and TanStack Virtual for displaying large tables.
Create and render Table v9 tables in Preact, including headers and cells. Get guidance on reusable components, Query data integration, and virtualization, which renders only part of a table at a time.
Build and render tables with React Table v9, with guidance on stable data and column definitions. Find task-specific references for reusable components, Query data fetching, and virtualization, which limits rendering to visible rows or columns.
Design Google Cloud infrastructure around application needs, scale, and budget. Get service recommendations, cost estimates, infrastructure configuration files, and deployment guidance, with support for automated delivery and security configuration reviews.
Implement and debug data fetching in Expo and React Native apps. Get guidance on API requests, caching, error handling, authentication tokens, offline behavior, and environment configuration, with fetch, React Query, and SWR options.
Write and improve SQL for major data warehouses, accounting for differences in their syntax. Use it to build analytical queries, translate existing SQL, investigate errors, and review slow queries for performance improvements.
Create Vitest tests for Mastra playground features using MSW to simulate network responses. Exercise the real client SDK, React Query cache, and components with typed sample data to check loading, access conditions, redirects, and errors.
Create and render Table v9 tables in Solid while preserving reactive data updates. Get guidance on reusable components, Query data integration, and virtualization, which renders only part of a table at a time.
Build React and TypeScript pages with guidance for data fetching, loading states, styling, and navigation. Organize code by feature and apply lazy loading, which loads components when needed, using patterns for MUI v7 and TanStack Router/Query.
Review SQLx queries changed in Rust code, replace suitable calls with macros that check SQL at compile time, refresh the offline query cache, and check for performance problems and unsafe handling of user input.
Guide cache implementation and code reviews by choosing expiration times, update rules, keys, and storage layers. Address stale data and bursts of requests when cached entries expire, with examples for Redis, HTTP headers, and TanStack Query.
Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics. Triggers: "azure-monitor-query", "LogsQueryClient", "MetricsQueryClient", "Log Analytics", "Kusto queries", "Azure metrics".
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Enables Redshift system-table (SYS_*) log publishing to S3 Tables in Apache Iceberg format for both Provisioned clusters and Serverless namespaces, verifies publishing status, and queries the published logs via any Iceberg-compatible engine including Redshift and Athena. Covers system tables such as sys_query_history, sys_query_text, sys_connection_log, sys_query_detail, and sys_session_history. Applies when turning on S3 Tables log publishing for a cluster or namespace, confirming publishing status and locating the S3 Tables namespace, querying non-realtime data from Redshift system tables off-cluster at scale, or building dashboards for Redshift monitoring and auditing, especially for historical or high-volume system-table data beyond the in-cluster SYS_ view retention window. Trigger phrases: publish redshift system table log to s3 tables, enable-logging s3 tables, describe redshift logging status, query redshift system tables in athena or redshift, redshift log exports to iceberg.
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Execute and manage Athena SQL queries across default and federated catalogs (Glue, S3 Tables, Redshift). Triggers on phrases like: query data, run SQL, athena query, analyze table, SQL query, workgroup status, profile table, query Redshift catalog, query S3 Tables. Do NOT use for finding specific data assets (use finding-data-lake-assets), full catalog audits (use exploring-data-catalog), importing data (use ingesting-into-data-lake).
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Run SQL against Google BigQuery and browse its catalog — submit queries (sync or async), poll job status, page through results, list datasets/tables, and read table schemas. Use this whenever the user wants to query a BigQuery table, ask "what's in this dataset", check a BigQuery job's status, or mentions bigquery.googleapis.com or a `project.dataset.table` path. Always start from this skill when interacting with this service — its bundled scripts and recipes are the fastest path.
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TanStack Query (React Query) v5 best practices for data fetching, caching, mutations, and server state management. Use when building data-driven React applications, setting up query configurations, implementing mutations/optimistic updates, configuring caching strategies, integrating with SSR, or fixing v4→v5 migration errors.
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Generates Python code using BigQuery DataFrames (BigFrames), the pandas/scikit-learn-style API over BigQuery. Use when writing BigFrames code or doing pandas-style dataframe/ML work against BigQuery (e.g. in a notebook). Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.
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