Results for “query”

Work with BigQuery to create datasets and tables, run SQL queries, ingest data, and manage processing jobs. The skill also covers connections to BigQuery ML and Gemini for machine learning and AI analysis.

• Creating datasets and tables
• Analyzing data with SQL queries
• Ingesting data and managing processing jobs

Generate Python code to clean, transform, analyze, visualize, and model BigQuery data with BigQuery DataFrames. The skill keeps processing in the cloud and uses familiar table operations, with guidance for previews, schema checks, and machine learning.

• Clean and transform BigQuery data with Python
• Preview data and check column types
• Visualize aggregated or sampled data

Investigate failing or slow BigQuery jobs and unexpected cost increases. Compare execution details and resource usage with a normal baseline to isolate bottlenecks, identify likely causes, and determine practical remediation steps.

• Investigate failed or slow BigQuery jobs
• Locate bottlenecks in execution stages
• Diagnose sudden increases in query or capacity costs

Register a custom Query type for JetEngine's Query Builder — extend \Jet_Engine\Query_Builder\Queries\Base_Query for the runtime query class, extend \Jet_Engine\Query_Builder\Query_Editor\Base_Query for the editor component, then hook BOTH register actions — jet-engine/query-builder/queries/register (factory) for the runtime, jet-engine/query-builder/query-editor/register for the editor UI. Five abstract methods on Base_Query — _get_items(), get_items_total_count(), get_items_page_count(), get_items_pages_count(), get_current_items_page(). Built-in cache via get_cached_data() / update_query_cache(). Custom queries participate in JE 3.8+ MCP tool exposure and the frontend query inspector automatically. Use when scaffolding a custom query type (HPOS WC orders, third-party API source, custom DB table) for JetEngine listings, dynamic widgets, or REST API endpoints.

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Automate Bitquery tasks through Rube MCP and Composio. The skill helps an agent discover available operations, verify an active connection, and run the selected tools with the input fields and formats they currently require.

• Discovering available Bitquery operations
• Verifying the Bitquery connection
• Executing tasks with current tool input requirements

Deploy osquery on Windows, macOS, and Linux to inspect processes, listening ports, software, and system settings using SQL, a database query language. Configure scheduled checks and centralized management to support threat hunting and compliance monitoring.

• Deploying osquery across Windows, macOS, and Linux
• Investigating suspicious processes and network connections with SQL
• Periodically checking software inventories and system settings

Assess how changes, failures, or paused updates to a BigQuery table or view affect downstream assets. Trace data lineage, the relationships showing where data flows, and summarize dependent tables, dashboards, and processes with direct and indirect impacts.

• Assess downstream effects of a table failure
• Check dependencies before changing or deleting a view
• Evaluate the impact of pausing data updates

Manage BigQuery datasets, tables, views, and jobs, and run SQL queries for basic data analysis. Provides setup examples and references for data ingestion, command-line operations, client libraries, access permissions, and infrastructure configuration.

• Create BigQuery datasets and tables
• Search and aggregate data with SQL
• Load data and manage processing jobs

Automate Bitquery tasks through Rube MCP and Composio. The skill helps an agent discover available operations, verify an active connection, and run the selected tools with the input fields and formats they currently require.

• Discovering available Bitquery operations
• Verifying the Bitquery connection
• Executing tasks with current tool input requirements

TanStack Query (React Query) v5 (@tanstack/react-query) の API リファレンス。 useQuery, useQueries, useInfiniteQuery, useMutation, useSuspenseQuery, QueryClient, QueryClientProvider, queryOptions, HydrationBoundary, invalidateQueries, prefetchQuery, skipToken, keepPreviousData, placeholderData, staleTime, gcTime, optimistic updates, SSR / hydration, persistQueryClient, eslint-plugin-query。 kubb（フック生成側）とは別。

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Use GitNexus to inspect which conditions control a statement and how variable values flow within a function. Understand early exits, investigate empty query results, and guide changes to the tool’s control and data dependency queries.

• Check which conditions control a statement
• Trace a variable within a function
• Investigate empty pdg_query results

Convert Snowflake dbt SQL models to BigQuery through a bulk migration workflow. Preserve Jinja template logic while adapting configuration, explicit type conversions, JSON access, and deduplication patterns, with translated files saved in their directory structure.

• Migrating Snowflake dbt models to BigQuery in bulk
• Preserving dbt references and incremental processing logic
• Adapting JSON, array, and STRUCT access during migration

Analyzes Google Cloud BigQuery slot consumption, query costs, and execution bottlenecks using INFORMATION_SCHEMA. Use when diagnosing slow BigQuery queries, slot starvation, high on-demand query costs, unpartitioned table scans, or join performance issues. Don't use for generic BigQuery administration (use bigquery-basics), BigQuery ML (use bigquery-ai-ml), or DataFrame operations (use bigquery-bigframes).

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Write SQL that uses BigQuery’s built-in machine learning and generative AI for forecasting, anomaly detection, identifying drivers of change, searching by meaning, and classifying or summarizing text stored in your data.

• Forecasting future values from time-series data
• Detecting anomalies and investigating drivers of change
• Searching documents by meaning

Analyzes the downstream impact (blast radius) when a BigQuery table or view is broken, stale, or modified. Identifies all downstream tables, dashboards, and processes that will be affected. Use when: - Performing a blast radius or impact analysis for a BigQuery table or view. - Assessing the consequences of modifying, deleting, or pausing updates to a BigQuery asset. - Identifying downstream dependencies (tables, dashboards, processes) of a BigQuery asset. Don't use for: - General BigQuery querying or data analysis (use BigQuery-related tools instead). - Non-BigQuery assets (e.g., Cloud Storage files) unless they are part of the BigQuery lineage. - Creating or modifying lineage links directly.

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Review Python and BigQuery pipelines for excessive costs, duplicate writes on reruns, and hard-to-diagnose failures. Receive a structured assessment with specific function and line references, prioritized fixes, and rough estimates of the largest cost risks.

• Check BigQuery pipeline code before production use
• Estimate job counts and cost risks in loops
• Review duplicate writes when rerunning failed jobs

Turn a plain-language data request into SQL tailored to your database. Build queries that combine tables, filter records, and calculate summaries, with explanations of each step, performance considerations, and suggestions for adjusting dates or other conditions.

• Create SQL for order counts by status
• Analyze retention by signup month
• Query large tables with date-based partitions

Answers questions about GKE spending using BigQuery billing exports and live cluster metrics. It breaks down costs by project, namespace, or workload and compares requested computing resources with actual usage to investigate cost increases.

• Comparing GKE costs across projects
• Finding the most expensive namespace or workload
• Investigating a sudden increase in cluster costs

Review BigQuery capacity, table layouts, storage retention, and SQL to improve cost efficiency. Compare billing options, plan partitioned tables, and evaluate search indexes, stored aggregation results, and BI Engine for suitable workloads.

• Compare On-Demand pricing with Editions
• Review storage billing models and retention periods
• Design table partitioning and clustering

Retrieve BigQuery execution records and resource metrics using the right tool for each task. The skill guides monitoring tool selection and SQL queries for performance investigations, capacity analysis, and reviews of billable compute and storage usage.

• Retrieve execution details for a slow job
• Check compute capacity contention and queue delays
• Review reserved capacity and autoscaling limits

Set up monitoring for deployed agents to inspect execution paths, latency, and model interactions. It explains Cloud Trace, prompt and response logging, BigQuery analytics, and external monitoring integrations, with checks for missing data and unwanted content capture.

• Investigating agent latency and execution errors
• Configuring prompt and response logging
• Analyzing agent events in BigQuery

Azure Monitor Query SDK for Java. Execute Kusto queries against Log Analytics workspaces and query metrics from Azure resources. Triggers: "LogsQueryClient java", "MetricsQueryClient java", "kusto query java", "log analytics java", "azure monitor query java". Note: This package is deprecated. Migrate to azure-monitor-query-logs and azure-monitor-query-metrics.

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Build and render Table v9 tables in Vue with guidance on keeping data updates reactive, reusing shared table components, and connecting TanStack Query for data fetching or TanStack Virtual for displaying large tables.

• Building a Table v9 table in Vue
• Keeping tables in sync with changing data
• Reusing shared table components and settings

Evaluate Google Cloud workloads against built-in best practices or custom organizational rules using Workload Manager. Use public client libraries or the REST API to run checks, review violations and scanned resources, and export results to BigQuery.

• Evaluate workloads against Google Cloud best practices
• Inspect rules for SAP and SQL Server workloads
• Run evaluations using custom organizational rules