google
202 skills with substantive source documentation and an official publisher or observed installation demand. Read each skill’s original documentation through its card.
googleapplication-integration-troubleshooting
Diagnoses failed, stuck, or silent Google Cloud Application Integration runs and Integration Connectors tasks using the REST API, Cloud Logging, and Cloud Monitoring. Use when an integration fails, returns HTTP 200 without doing the expected work, hangs in a suspended or retry state, or hits IAM, run-as service account, connector health, or quota errors. Do not use for initial region provisioning (use application-integration-basics) or building and deploying versions (use application-integration
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googleassuredworkloads-environment-assessor
Discover Google Cloud resource telemetry and effective Org Policies, evaluate brownfield workloads against sovereign data boundary and compliance standards (EU Data Boundary with Support, FedRAMP Moderate/High, DoD IL4/IL5, ITAR, CJIS, US/EU Regional Data Boundaries), and recommend the optimal Assured Workloads Framework. Use when asked to: "assess my environment for compliance", "which Assured Workloads framework should I use?", "check if my folder is ready for EU sovereignty", "evaluate compli
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googleassuredworkloads-frameworks-author
Authors custom cloud controls and customized frameworks for Assured Workloads when built-in resources cannot satisfy core requirements. Use when creating or synthesizing custom data boundary resources. Don't use for browsing or mapping existing built-in frameworks (use assuredworkloads-frameworks-navigator instead).
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googleassuredworkloads-frameworks-navigator
Acts as a Data Boundary Compliance Analyst that maps user compliance queries against Data Boundary Frameworks and cloud controls, validates matched resources, and recommends appropriate Frameworks or cloud controls. Use when mapping direct natural language workload requirements to Assured Workloads / Data Boundary Frameworks and cloud controls, browsing built-in Data Boundary frameworks, or identifying major requirement gaps. Don't use for non-Data-Boundary Compliance Manager posture assessments
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlebigquery-ai-ml
Leverages BigQuery's built-in machine learning and GenAI capabilities for advanced data analytics. Use when you need to write SQL queries that perform time-series forecasting, predict values, detect outliers or anomalies, find key drivers, perform semantic search or vector search, classify text, calculate similarity, summarize content, translate language, evaluate models, filter by semantic conditions, measure the causal effect of an intervention, compute correlations between columns, detect cha
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlebigquery-basics
Manages datasets, tables, and jobs in BigQuery. Use when you need to interact with BigQuery, run SQL queries, manage BigQuery resources (datasets, tables, views), or perform basic data ingestion and analysis.
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlebigquery-bigframes
Generates Python code using BigQuery DataFrames (BigFrames). Use by default for any Python data task involving BigQuery, including data processing, analysis, and machine learning. Don't use for SQL-first workflows or the google-cloud-bigquery client library — use bigquery-basics.
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlebigquery-migration-sql-translator
Translates SQL queries, DDL schemas, and stored procedures from legacy database dialects (Teradata, Oracle, Redshift, Snowflake, SparkSQL, HiveQL) to BigQuery GoogleSQL using the BigQuery Migration Service (BQMS) MCP server. Use when translating legacy SQL queries, migrating schemas or metadata zip archives, or configuring BigQuery Migration Service (BQMS) YAML translation settings. Don't use for executing BigQuery queries or managing datasets and tables (use bigquery-basics), diagnosing runtime
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlebigquery-observability
Provides data-retrieval best practices, tool selection guidance, and performant SQL query syntax for BigQuery telemetry across INFORMATIONSCHEMA, Cloud Monitoring, and the REST API. Use when the telemetry to fetch is already known, selecting telemetry tools, writing performant INFORMATIONSCHEMA queries, retrieving telemetry for diagnosing single-job performance bottlenecks, investigating slot contention, job concurrency and queue latency, analyzing reservation capacity, utilization and autoscali
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlebigquery-optimization
Provides workflows to optimize BigQuery environments (capacity planning, editions), storage assets (partitioning, clustering, storage lifecycles, billing models), and SQL queries. Use when optimizing cost, modeling Edition migrations, rightsizing reservations, evaluating logical vs. physical storage, designing table partitioning/clustering, generating table DDL, migrating unpartitioned tables, managing partition expiration, optimizing individual SQL queries, or evaluating acceleration structures
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlebigquery-slot-cost-optimizer
- Analyzes Google Cloud BigQuery slot consumption, query costs, and execution bottlenecks using INFORMATIONSCHEMA. 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).
- installations
- 2
- GitHub Stars
- 21,1 k
- Mis à jour
- 9 oct.
googlebigquery-troubleshooting
Provides diagnostic workflows and step-by-step root-cause analysis procedures for actively broken, failing, or slow BigQuery jobs, execution graph and query plan stage bottlenecks, system performance issues, or unexpectedly expensive workloads. Use when interpreting symptoms, isolating bottlenecks, diagnosing cost spikes (on-demand query spend, capacity slot autoscaling, storage growth), execution graph stages or substep variables, identifying root causes, and determining remediation steps. Don'
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlebigquery-udfs
Creates, deploys, and troubleshoots BigQuery User-Defined Functions (UDFs) and Remote Functions across SQL, JavaScript, Python, and Cloud Run endpoints. Use when writing custom scalar or aggregate functions in BigQuery, installing PyPI packages or Cloud Storage libraries in a Python or JavaScript UDF, or calling Google Cloud and external APIs from a Python UDF (WITH CONNECTION) or Remote Function. Don't use for standard BigQuery table/dataset CRUD (use bigquery-basics), DataFrames client workflo
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlebigtable-basics
Assists in provisioning instances/tables, designing performant schemas, and querying data in Bigtable. Use when designing Bigtable row keys, configuring column families, writing SQL queries or client library code (Java, Go, Python) for Bigtable, or diagnosing performance/hotspotting issues. Also use when provisioning Bigtable clusters using gcloud or cbt CLIs. Don't use for generic Cloud SQL administration.
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlecloud-databases-onboarding
Guides users through discovering their database requirements, recommends a Google Cloud database based on a recommendation matrix, and assists in database creation. Use when a user asks 'What database service should I use?', 'Help me pick a database', or when a user wants to create a new database on Google Cloud. Don't use for general Google Cloud maintenance, managing existing databases, or database migrations.
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlecloud-logging-configuration-basics
Configure single-project Cloud Logging on Google Cloud: regional log buckets, log sinks, log views, restricting or hiding sensitive logs in the default view (Default) filter, IAM permissions for views (Logs View Accessor, IAM conditions), logs-based metrics, log exclusions, and sampling. Don't use for cross-project logging or multi-project setups.
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlecloud-logging-cross-project-configuration
Configure and troubleshoot Google Cloud cross-project centralized logging and read-time aggregation. Use when: - Setting up log routing from multiple projects/folders/organizations to a central log bucket. - Creating cross-project log sinks and configuring central log buckets. - Troubleshooting cross-project routing. Don't use for single-project basic configurations.
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlecloud-logging-finops
Audits Cloud Logging costs for a Google Cloud project: the charges for log entries written to log buckets, the services, log streams and severities that drive them, and the log sinks that route them. Reconciles Cloud Billing export costs with Cloud Monitoring volume metrics, and generates a FinOps report with charts and copy-paste exclusion filters. Use when auditing Cloud Logging spend, finding logging waste, identifying top logging contributors, generating log cost reduction reports, or creati
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlecloud-monitoring-chart-generation
Generates Cloud Monitoring Server-Driven UI (SDUI) Widget and XyChart Protocol Buffer textprotos on Google Cloud from resolved PromQL or ListTimeSeries queries. Use when: - Generating valid google.monitoring.dashboard.v1.Widget textprotos, containing PrometheusQuery or TimeSeriesFilter datasets, for use with the Cloud Monitoring Dashboards API, gcloud CLI, or declarative dashboard definitions. - Synthesizing Server-Driven UI (SDUI) widget titles, axis labels, and plot types for Prometheus or Lis
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlecloud-monitoring-list-time-series-request
Generates valid Cloud Monitoring ListTimeSeries requests and aggregation specifications from metric descriptors and resource parameters. Use when asked to create, generate, format, or build ListTimeSeries requests, JSON payloads, filter expressions, or aligner/reducer aggregations for Cloud Monitoring metrics and charts. Don't use for metric discovery or metric selection.
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlecloud-monitoring-metric-selection
Retrieve, query, and identify relevant Cloud Monitoring metric descriptors on Google Cloud for a service or resource (such as Compute Engine, Spanner, BigQuery, Cloud Run, Cloud SQL, Pub/Sub, Cloud Storage, etc.). Use when asked to find, list, search, or discover GCP metric types, names, kind/value schemas, or descriptors.
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlecloud-monitoring-notification-channels-configuration
Generates Terraform HCL configurations for Cloud Monitoring notification channels (googlemonitoringnotificationchannel) and adds notification channels to alerting policies (googlemonitoringalertpolicy) in Google Cloud projects. Use when creating or configuring email, SMS, Slack, PagerDuty, Pub/Sub, or webhook notification channels, verifying Pub/Sub topic compatibility (including cross-project topics, message storage policies, and in-transit enforcement) and publishing permissions for the Cloud
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlecloud-monitoring-promql-query
Generates valid PromQL queries from Cloud Monitoring metric descriptors and resource parameters. Use when asked to create, generate, write, or format PromQL queries, PromQL strings, or PromQL aggregations for Cloud Monitoring metrics and resources. Don't use for raw metric discovery or metric selection.
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.
googlecloud-network-insights
Guides and automates setup, onboarding, and synthetic monitoring operations for Cloud Network Insights in Google Cloud and Broadcom AppNeta. Use when enabling Cloud Network Insights, deploying or troubleshooting Monitoring Points across cloud and on-premises environments, configuring Network or Web monitoring policies and AppNeta alarm rules, configuring alert policies, querying path alarms and performance metrics, or verifying synthetic path reachability. Don't use for generic network observabi
- installations
- 2
- GitHub Stars
- 21,6 k
- Mis à jour
- 25 oct.