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Fix false-positive Cloud Run deploys where `gcloud builds submit --tag` silently fails but the follow-up `gcloud run services update --image :latest` succeeds against a stale image, producing a confident "Deploying... Done" message with pre-fix code still live. Use when: (1) gcloud builds submit prints "ERROR: (gcloud.builds.submit) Invalid value for [source]: Dockerfile required when specifying --tag" but the shell scrolls past it, (2) code changes verified locally aren't visible in production after a deploy that printed "revision ... has been deployed and is serving 100 percent of traffic", (3) Chaining `gcloud builds submit` and `gcloud run services update` in a single copy-paste block, (4) Using the mutable `:latest` tag for Cloud Run deploys.

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Train and evaluate RuView models using Wi-Fi measurements for pose estimation, retrieval, and adaptation to different environments. Follow laptop or GCloud GPU workflows, then package and publish trained models to Hugging Face.

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• Train pose estimation without cameras or labels
• Train pose estimation with paired camera and Wi-Fi data
• Adapt models to different rooms and environments

Find and synthesize official Google developer documentation for platforms including Google Cloud, Gemini, Android, and Firebase. Retrieve command syntax, API details, permissions, and product comparisons through Developer Knowledge, then turn the findings into practical technical answers.

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• Checking gcloud commands and flags
• Looking up API syntax and required permissions
• Comparing Google products and architectures

Investigate failed workflows and tasks in Managed Service for Apache Airflow. The skill compares remote logs, task states, and source code to explain failures, show supporting evidence, and recommend specific code or configuration fixes.

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• Investigating a failed DAG run
• Diagnosing task timeouts and queue delays
• Checking service errors against DAG code

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.

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• Check command syntax and flags before execution
• List cloud resources with only the fields needed
• Review infrastructure changes before running commands

Sign in to command-line tools through a browser or device verification code, then save the login to a keychain. Later commands can request the saved credentials by handle, an identifier, without exposing token contents in the conversation.

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• Sign in to AWS CLI through AWS SSO
• Authenticate gh or glab through a browser
• Save a gcloud login for later commands

使用 qingcloud CLI 操作青云 QingCloud。当用户提到青云、QingCloud、青云云、青云QingCloud 等时触发此技能。

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Design and deploy a Google Cloud global external Application Load Balancer with caching and web attack protection. Matches settings to workload types, discovers existing backends, and generates Terraform or gcloud deployment scripts.

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• Design global web delivery across multiple backends
• Choose caching settings for static content and APIs
• Configure Cloud Armor protection for each workload

Plan and operate Google Cloud Platform infrastructure using services such as Cloud Run and GKE, Google's managed Kubernetes service. Get guidance on deployment, access permissions, networking, monitoring, and cost controls for the system's requirements.

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• Planning an API with Cloud Run and Cloud SQL
• Reviewing GKE access permissions
• Designing event processing with Pub/Sub

GCPプロジェクト単位でBigQuery認証を設定するスキル。 gcloud設定プロファイルで複数プロジェクトを安全に分離管理。 「BigQueryに繋ぎたい」「BQ認証」「gcloud認証」「データ分析の認証設定」等のリクエストで発動。

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Provides safety-critical validation, guardrails, and data reduction for gcloud CLI operations across Google Cloud Platform (GCP) services and infrastructure. Use when planning, generating, constructing, proposing, describing, or executing any gcloud CLI commands - including when answering questions about gcloud syntax, or formatting flags. Don't use when writing Google Cloud client library code or raw REST/gRPC API requests.

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Configure Google Cloud IAP to check identity before granting access to internal applications and virtual machines. Set permissions by user group, device, and network, configure reauthentication, enable service-account access, and record access decisions for monitoring.

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• Replacing VPN access to internal Google Cloud applications
• Restricting sensitive tools to managed devices
• Configuring SSH and RDP access through IAP tunnels

Audit Google Cloud access permissions to find overly broad roles, unnecessary service account keys, public access, and impersonation risks. Use gcloud CLI, Policy Analyzer, and IAM Recommender to document findings and plan more limited permissions.

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• Reviewing access permissions across a GCP organization
• Replacing broad Owner and Editor roles with narrower roles
• Auditing unused service accounts and user-managed keys

Design and create new Cloud Storage buckets for uses such as backups, media, and AI data. Check project security and name availability, choose storage and protection settings, and produce creation commands or code in the preferred format.

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• Creating storage for application media
• Designing buckets for sensitive data
• Preparing backup and long-term archive storage

Helps individual developers get started on Google Cloud by checking local tools and authentication, selecting or creating a project, and linking billing. It verifies the setup and directs users to specialized guides for their first deployment.

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• Setting up a first Google Cloud project
• Reusing an existing project for development
• Checking authentication and billing setup

Guides migration of existing AI inference workloads to self-hosted GKE, Google’s managed Kubernetes service. Covers requirements, GPU sizing, deployment configuration, model storage, validation, and traffic cutover using gcloud and kubectl.

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• Migrating an existing Cloud Run AI service to GKE
• Planning a self-hosted replacement for Gemini API inference
• Sizing GPUs and preparing deployment configuration

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.

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• Planning a GKE version upgrade
• Choosing release channels and maintenance schedules
• Preparing node pool upgrades for databases or GPU workloads

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.

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• Choosing a database for a new Google Cloud application
• Clarifying data models, workloads, and scale requirements
• Preparing and validating database provisioning code for review

This skill should be used when user encounters "ADC not found", "gcloud auth error", "GCloud MCP error", "Application Default Credentials", "project not set", or needs help configuring GCloud integration.

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Fix Cloud Run GPU deployment failures caused by quota errors. Use when: (1) `gcloud run deploy` fails with "You do not have quota for using GPUs with zonal redundancy" AND "You do not have quota for using GPUs without zonal redundancy", (2) The service already exists and is running with a GPU, (3) You only need to update the container image, not change GPU config. Uses `gcloud run services update --image` instead of `gcloud run deploy` to bypass quota re-validation on existing GPU services.

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gcloudやbqコマンド実行時に認証エラー（Reauthentication required等）を検出した場合に使用。エージェントが自動で認証コマンドを実行することを防ぎ、ユーザーに認証を依頼します。

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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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• Designing a mobile backend on Cloud Run
• Planning a SaaS architecture on GKE
• Building an event analytics pipeline with BigQuery

Investigate slow TPU training on GKE by comparing diagnostic events with one-minute monitoring metrics. Separate memory or CPU bottlenecks from hardware and network issues, and identify the appropriate next steps.

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• Investigating slower TPU training without pod crashes
• Checking PERFORMANCE_DEGRADATION events
• Separating resource bottlenecks from infrastructure issues

Investigate stalled TPU training across multiple slices on GKE using hang reports, logs, and latency metrics. Separate compilation inconsistencies and data input stalls from hardware or network faults, and identify the relevant nodes for further investigation.

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• Investigating HANG_DETECTED logs during TPU training
• Separating input stalls from hardware faults
• Mapping reported faulty instances to GKE nodes