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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.
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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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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.
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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.
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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.
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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.
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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.
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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.
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Generate Terraform alerts for Cloud Run services, jobs, and worker pools using PromQL, a monitoring query language. Monitor errors, latency, resource usage, job failures, and queue delays, with configurable thresholds and explanations of when each alert triggers.
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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.
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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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Write, validate, and (optionally) execute an NQL query against a Narrative dataset. Drafts the query from the user's question, runs `narrative_nql_validate` until it compiles, explains the query in plain English, and only runs it on explicit approval (or when invoked with `--run`). Use when: "write an NQL query for X", "query this dataset", "validate this NQL", "run NQL against dataset <id>", "how many rows match Y", "show me the top N records from <dataset>". (narrative-common)
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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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Run a live investigation as an operator-driven query loop: the operator runs each query on the live system and pastes the result back, and you guide the investigation one labelled query at a time. Use alongside support-assist (which covers what to investigate) whenever someone is pasting query or command output back from a deployment, especially during an incident. Covers labelling, pacing, making each query decide the next step, version-grounding, and keeping queries cheap and correctly classified.
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Runs SQL queries on CloudWatch Logs data exported as Apache Iceberg tables in S3 Tables. Covers VPC Flow Logs, WAF logs, CloudFront access logs, Route 53 resolver logs, Network Firewall logs, EKS audit logs, Verified Access logs, SES logs, VPC Lattice logs, Step Functions logs, NLB access logs, and 20+ other AWS vended data sources. Applies when analyzing network traffic, investigating security incidents, querying exported logs with SQL, enabling S3 Tables integration, configuring log export, correlating logs with other data, or running Athena queries on the aws-cloudwatch table bucket. Trigger phrases: query logs with SQL, analyze logs in Athena, SQL on VPC flow logs, investigate network traffic, run SQL on exported logs, enable S3 Tables for CloudWatch, correlate logs, historical log analysis, set up log querying.
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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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Read a Prisma Next structured error envelope and route to the right recovery — code, domain, severity, why, fix, meta. Use for error, exception, my emit failed, my query won't typecheck, my query crashed, my migration won't apply, MIGRATION.HASH_MISMATCH, BUDGET.ROWS_EXCEEDED, BUDGET.TIME_EXCEEDED, RUNTIME.ABORTED, PLAN.HASH_MISMATCH, CONTRACT.MARKER_MISSING, PN-RUN-3001, PN-RUN-3002, PN-RUN-3030, PN-MIG-2001, PN-CLI-4011, PN-SCHEMA-0001, drift, capability missing, planner conflict, prisma studio, EXPLAIN, query log, db.end, db.close, script won't exit, hangs, close connection, pool.end, client is closed.
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Turn plain-language requests into Cloud Logging queries for Google Cloud. Generate service-specific filters to find errors, investigate issues, or review audit logs using LQL, the language used to search these logs.
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Investigate slow SQL, write complex joins and summaries, and design database structures. Uses execution plans, which show how queries run, to guide index choices and query rewrites, with before-and-after performance measurements.
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Call Yao processes to query data models or run scripts. Check which operations are permitted, inspect their required arguments through the documentation workflow, and execute a process using its full name.
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Implement and review browser API requests in OpenBot using shared patterns for reading and changing data. Covers cache keys, refreshing stored results after changes, error handling, and how components display request states.
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Choose and optionally run .NET test commands that match the project's SDK, test platform, and framework. Target specific tests, collect reports or diagnostics, and report execution results using syntax appropriate to the repository.
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Diagnose and eliminate the GraphQL N+1 resolver explosion using Facebook DataLoader's per-request batching, plus the second-order defenses (persisted queries, depth limits, cost analysis). Use when a GraphQL query that returns N items in a list triggers N+1 database round trips, when designing nested resolvers that load associations, when a list query times out under load, or when you need to budget query cost before resolvers run. NOT for REST-style endpoint design (use api-versioning-strategy), generic database query optimization (use database-query-optimizer), or schema design (use graphql-server-architect).
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