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Save web content as readable Markdown, a plain-text format for structured notes. Supports X posts, YouTube transcripts, Hacker News discussions, and general pages, with optional local downloads of images and videos.

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• Saving a web article as Markdown
• Keeping a YouTube transcript as a reference
• Archiving a Hacker News discussion

Audit saved project learnings against the current codebase to find outdated explanations, overlapping documents, and contradictions. Maintain the documentation with evidence-based updates, consolidation, replacement, or deletion, and deliver a per-document report with committed changes when applicable.

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• Checking saved learnings against current code
• Consolidating overlapping knowledge documents
• Correcting outdated paths and links

Diagnose GKE TPU slice creation failures caused by shared nodes. Check slice status, partition labels, and monitoring data to identify conflicting resource groups, then provide resolution steps for the user to apply.

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• Investigating node overlap behind SliceCreationFailed
• Finding slices that share nodes across partition shapes
• Checking whether resource conflicts existed at incident time

Diagnose GKE TPU node pool creation or expansion failures despite reserved capacity. Trace which virtual machines and unhealthy node pools occupy reservation slots, check automatic repair settings, and provide remediation steps for the user to apply.

中文简介正在准备中。当前显示英语简介。

• Investigate TPU capacity errors despite a reservation
• Identify node pools occupying reserved TPU capacity
• Check automatic repair settings on unhealthy TPU nodes

Diagnose GKE TPU slice creation failures and pending JobSets caused by unstable managed instance groups, the underlying VM groups. Read-only checks examine logs, instance status, upgrades, and repairs to identify causes and suggest user-applied fixes.

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• Investigating TPU SliceCreationFailed errors
• Checking whether pending JobSets relate to MIG instability
• Tracing stalled node pool upgrades or repairs

Helps address Google Cloud Security Command Center findings by planning targeted fixes for permissions, misconfigurations, vulnerabilities, and combined attack paths. It includes impact checks, verification, rollback steps, and execution after user approval.

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• Fix excessive permissions flagged by Security Command Center
• Plan fixes for exposed buckets or firewall rules
• Address attack paths involving multiple security issues

Manage temporary access in Google Cloud with Privileged Access Manager. Configure who can request which roles and for how long, maintain access configurations, and handle access requests and approval decisions through guided workflows.

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• Request temporary access to Google Cloud resources
• Configure eligible requesters and approvers
• Review and approve or deny pending access requests

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

Design security controls for Google Cloud Agent Gateway, covering incoming client requests and outgoing agent tool calls. Produce architecture diagrams, configuration files, deployment commands, and validation scripts using Model Armor, IAP, and Agent Registry.

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• Design incoming and outgoing agent communication controls
• Prepare Model Armor filters and tool access policies
• Generate scripts to check authentication and allowed tool calls

Design a Google Cloud system for data analysis or machine learning with AI agents. Turn requirements into product choices, an architecture diagram, infrastructure configuration, deployment instructions, and validation steps, with user reviews between phases.

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• Design a data analysis system with multiple AI agents
• Choose Google Cloud products for analytical requirements
• Prepare an architecture diagram and deployment guide

Check Google Cloud IAM allow policy changes against the last 90 days of access logs. The skill identifies access that could be lost and applies changes only after a successful simulation and user approval.

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• Checking access impact before removing a role
• Reviewing allow policy changes across resource scopes
• Applying policy changes after simulation and approval

Plan and build AI agents on Google Cloud, from gathering requirements to choosing a single-agent or multi-agent design. Produce architecture diagrams, deployment code and instructions, and validation plans tailored to the workload.

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• Plan an AI agent architecture on Google Cloud
• Choose between single-agent and multi-agent designs
• Prepare deployment code and step-by-step instructions

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.

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• Assess downstream effects of a table failure
• Check dependencies before changing or deleting a view
• Evaluate the impact of pausing data updates

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.

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• Design a GKE cluster shared by multiple teams
• Separate development, staging, and production workloads
• Set team permissions and resource quotas

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.

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• Build a client for live conversations with Gemini
• Add token renewal and session recovery
• Test audio and video input through a demo interface

Configure GKE ComputeClasses, which define preferred computing resources for workloads. Set fallback options between Spot and on-demand VMs, target GPUs or machine families, control class usage, and investigate pods waiting for suitable nodes.

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• Configuring fallback from Spot VMs to on-demand capacity
• Selecting GPUs, TPUs, or machine families
• Restricting ComputeClass editing and workload usage

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

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.

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• Investigating repeated JobSet restarts
• Checking whether Spot VM preemptions interrupted training
• Tracing recurring failures to a physical host

Guides an application’s first deployment to GKE, Google’s managed Kubernetes service. Covers application assessment, container creation, image storage, deployment configuration, security settings, health checks, and verification that the application is running.

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• Deploying an existing application to GKE for the first time
• Creating a Dockerfile and registering a container image
• Preparing deployment configuration with health checks

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.

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• Investigate Pod authentication failures and 403 errors
• Check Kubernetes service account identity bindings
• Diagnose metadata server connectivity problems

Finds the base image needed to create a custom GKE node image. It translates requirements such as GKE version, operating system, processor architecture, accelerators, and sandbox settings into a matching image name.

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• Finding a base image for custom GKE nodes
• Looking up images for GPU or TPU configurations
• Matching images to COS, Ubuntu, ARM64, or X86_64

Monitor TPU workloads on GKE using system metrics and PromQL, a metrics query language. Check utilization, memory, node availability, and interruptions to investigate infrastructure issues and calculate average recovery times and intervals between interruptions.

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• Checking TPU utilization and memory usage
• Investigating node and node pool availability
• Tracing maintenance and preemption interruptions

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.

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• Reduce excessive CPU and memory requests
• Set resource quotas for teams sharing a cluster
• Move interruption-tolerant batch jobs to Spot VMs

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.

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• Investigate vbar_control_agent crashes and memory exhaustion
• Check tpu-device-plugin metrics checksum errors
• Inspect conflicts with custom TPU metrics collectors