1.1K skills ・ Works with: Google Cloud · sorted by popularity
Implement and debug Google OAuth 2.0 refresh tokens bound to a client key. Covers key generation, signed DPoP proofs that demonstrate key possession, and nonce challenge retries in WebCrypto and JavaScript environments.
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.
Reserve remote Android devices on Google's Developer Device Platform and connect through ADB, Android's device control tool. Check session status and expiration, extend usage time, or cancel sessions and release resources.
Reduce Google Kubernetes Engine costs by adjusting CPU and memory requests, setting resource quotas, and choosing Spot VMs or committed use discounts. Match spending decisions to steady usage, variable demand, and workloads that tolerate interruptions.
Investigate crashes, out-of-memory errors, and device initialization failures on GKE TPU v6e nodes. The skill checks logs and custom metrics collection for conflicting operations, then guides temporary mitigation by disabling an identified collector.
Create and connect to managed MySQL, PostgreSQL, or SQL Server databases on Google Cloud. This skill guides instance setup, database creation, access permissions, and connection steps, with references for backups, security, and Terraform configuration.
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.
Diagnose interrupted or repeatedly restarting AI training jobs managed by JobSet on GKE. Correlate restart metrics, node events, host health, and worker logs to distinguish Spot VM preemptions, infrastructure failures, and application crashes, then recommend responses.
Create and validate Terraform alerts for GKE using PromQL, a monitoring query language. Cover response times, errors, traffic, memory usage, and cluster health, with checks for alert timing, duplicate signals, and metric ingestion costs.
Configure Backup for GKE to protect Kubernetes configurations and persistent data. Set backup schedules, retention periods, and customer-managed encryption keys, then prepare restore plans with explicit policies for handling existing resources.
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.
Create and manage custom Agent resources on Gemini Enterprise Agent Platform before running conversations. Prepare REST requests to configure instructions, attach files and skills, connect tools, track creation progress, and retrieve, update, or delete agents.
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.
Diagnose why GKE Pods cannot authenticate to Google Cloud APIs. Inspect cluster settings, service account bindings, resource permissions, and metadata server health, then provide evidence and concrete fixes for a human to apply.
Plan and configure GKE cluster networks, including private nodes, IP address ranges, DNS, and outbound connectivity. Use the guidance to set up VPC-native networking and Dataplane V2, which provides built-in network policy enforcement.
Audit and strengthen security for applications running on GKE. Configure pod traffic restrictions, sandbox isolation, namespace security standards, and Secret Manager volume mounts to protect workloads at the application and namespace level.
Build Gemini API integrations on Google Cloud’s Agent Platform using the Google Gen AI SDK. Get guidance on authentication, text and media processing, structured JSON responses, function calls, caching, batch processing, and real-time voice or video interactions.
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.
Configure GKE logging, metrics collection, dashboards, and alerts with Cloud Logging, Cloud Monitoring, and managed Prometheus. Diagnose missing measurements, collection failures, permission errors, and alert rule problems to identify appropriate configuration changes.
Set up a managed Kubernetes environment on Google Cloud, from choosing Autopilot or Standard to creating a GKE cluster, obtaining kubectl connection credentials, and deploying workloads. Includes guidance on private clusters and resource requests.
Prepare and run Google Cloud CLI commands with syntax checks, explicit project and location settings, and focused output. Guides agents through change previews and authorization requirements for destructive operations and sensitive infrastructure changes.
Choose a Google Cloud database by clarifying data needs, workload, and scale. The skill explains its recommendation and prepares validated Terraform configurations or provisioning scripts for review before infrastructure changes are made.
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.
Guides migration from Google AI Studio’s Gemini API to Gemini Enterprise Agent Platform on Google Cloud, covering billing, access permissions, authentication, and connection settings for SDKs and supported agent tools.