174 skills (repositories by google) · sorted by popularity
Adjust application capacity on GKE through manual scaling, HPA for changing the number of running pods, and VPA for adjusting CPU and memory allocations. Includes configuration templates and a workflow for reviewing resource requests against actual usage.
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.
Diagnoses storage failures in GKE, Google's managed Kubernetes service, including stuck Pods, disk attachment errors, storage pressure, and missing volume expansion. It provides diagnostic commands and reviewable configuration patches for deployment through GitOps.
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.
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.
Evaluate whether Google SecOps rules detect threats described in articles or supplied text. Generate simulated attack logs, identify coverage gaps, summarize matching rules, and create rules for uncovered threats, with user approval before adding them.
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.
Generate a client in your chosen language for live, two-way communication with Gemini Enterprise LiveAPI. Includes token renewal, session recovery, tests, and a demo interface for trying text, audio, video, and transcription.
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.
Helps configure persistent storage for GKE applications, including disk selection, storage requests, shared files through Filestore, and volume expansion. It also explains how disk compatibility and zone placement affect workload scheduling.
Plan and configure a shared GKE cluster for multiple teams or environments. Set up namespaces, team permissions, resource limits, and network separation, then use labels and GKE cost allocation to track spending by team.
Diagnose failures in GKE HorizontalPodAutoscaler (HPA), which adjusts Pod counts automatically. Investigate missing metrics, unexpected scaling behavior, and slow reactions, then receive proposed configuration fixes or commands for review before deployment.
Plans, executes, and validates Google Kubernetes Engine upgrades for Standard and Autopilot clusters. Creates environment-specific upgrade plans, checklists, and command-based runbooks, with guidance on maintenance timing, node upgrade strategies, workload readiness, and stalled upgrades.
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.
Review GKE reliability settings for applications running in Kubernetes. Configure availability safeguards during maintenance, health checks, replica counts, graceful shutdown, and distribution across zones and nodes to help workloads handle disruptions.
Assess whether a GKE cluster or application is ready for production. Coordinates specialist skills to review scaling, security, reliability, monitoring, recovery, costs, and maintenance, then reports domain status, a weighted readiness score, and remediation priorities.
Helps build AI features in Go with Genkit, including text generation, structured data output, streaming, tool calls, and conversational agents. Guides HTTP serving and trace inspection to check model responses and tool execution.
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.
Inspect GKE TPU Dynamic Slices, diagnose provisioning failures, and validate workload configuration for single slices or JobSet workloads spanning multiple slices. Get cleanup commands, with confirmation required before destructive changes.
Create or update Kubernetes YAML configuration files for GKE Autopilot and Standard. Translates application requirements into manifests covering container permissions, CPU and memory allocation, health checks, storage, external routing, and GPU resources for AI model serving.
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.
Deploy Model Garden models to Agent Platform endpoints, the addresses used for model requests. Check supported hardware and estimated hourly costs, inspect deployment progress, test predictions, and remove deployed resources after use.
Create and work with Google Bigtable instances and tables, design row keys and column groups, and query data with SQL or client libraries. Investigate slow queries and concentrated access patterns using Key Visualizer and command-line diagnostics.