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Google Cloud Waf Sustainability

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  • Cloud and DevOps
  • Apache-2.0
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From the author

Provides recommendations for environmental sustainability, carbon footprint reduction, and energy efficiency based on the Sustainability pillar of the Google Cloud Well-Architected Framework (WAF). Use when the user asks to assess, design, or optimize Google Cloud workloads for sustainability—including the shared responsibility model, selecting low-carbon regions (CFE%), reducing resource and AI/ML energy waste, designing efficient software and storage lifecycles, or measuring and tracking emissions using Google Cloud Carbon Footprint. Don't use for financial cost reduction (use google-cloud-waf-cost-optimization), latency and throughput tuning (use google-cloud-waf-performance-optimization), or high availability and disaster recovery (use google-cloud-waf-reliability).
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill offers a structured approach to minimizing the environmental impact of Google Cloud workloads, based on the Sustainability pillar of the Google Cloud Well-Architected Framework. It guides users through a shared responsibility model, helping them make informed decisions regarding region selection, resource allocation, AI/ML energy efficiency, and data lifecycle management. The skill includes assessment questions and a validation checklist to help architects and developers measure, track, and reduce their carbon footprint while maintaining operational efficiency.

What it does

  • Provides principles for reducing the carbon footprint of cloud-based applications.
  • Offers guidance on selecting Google Cloud regions with high Carbon-Free Energy (CFE) percentages.
  • Suggests strategies for optimizing AI/ML workloads and infrastructure resource usage.
  • Includes a comprehensive checklist for validating workload sustainability.
  • Provides assessment questions to identify sustainability gaps in existing architectures.
  • Recommends tools for measuring and tracking emissions, such as Google Cloud Carbon Footprint.

When to use it

  • When designing new Google Cloud architectures with sustainability requirements.
  • When assessing existing workloads to identify and remediate carbon hotspots.
  • When looking for best practices to improve energy efficiency in AI/ML or data storage.
  • When establishing organizational governance for green cloud operations.

Advantages

  • Aligns technical decisions with environmental goals and global standards.
  • Helps reduce energy waste through rightsizing and lifecycle management.
  • Provides actionable metrics for ESG reporting and carbon footprint tracking.
  • Promotes a culture of sustainability within technical teams.

Usage examples

“How can I reduce the carbon footprint of my Google Cloud data storage?”
The agent will provide strategies such as implementing Object Lifecycle Management to move cold data to Archive storage and using tools like Dataplex to identify and remove dark data.
“What should I consider when choosing a Google Cloud region for sustainability?”
The agent will explain how to prioritize regions with high Carbon-Free Energy (CFE) percentages and suggest using the Google Cloud Region Picker to balance carbon impact with latency and cost.
“How do I optimize my AI/ML workloads for energy efficiency?”
The agent will recommend matching algorithmic needs to specialized hardware like TPUs and applying techniques such as model compression to maximize computations per watt.
“Can you help me assess the sustainability of my current GKE deployment?”
The agent will walk through the validation checklist, asking about your use of Horizontal/Vertical Pod Autoscaling and your strategy for scheduling batch jobs during high CFE periods.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/google/google-cloud-waf-sustainability/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip google-cloud-waf-sustainability.zip -d .agents/skills

Documentation by Google

Google Cloud Well-Architected Framework skill for the Sustainability pillar

Attribution

MUST READ: Before executing any gcloud, REST API, or MCP command, you MUST read and follow the instructions in Usage Attribution.

Overview

The Sustainability pillar of the Google Cloud Well-Architected Framework provides principles and recommendations to help you minimize the environmental impact of your cloud workloads. It focuses on a shared responsibility model—Google optimizes the sustainability of the cloud, while customers optimize sustainability in the cloud. By making informed decisions about architecture, resource allocation, and region selection, you can significantly reduce your carbon footprint and improve overall energy efficiency.

Core principles

The recommendations in the sustainability pillar of the Well-Architected Framework are aligned with the following core principles:

Relevant Google Cloud products

The following are examples of Google Cloud products and features that are relevant to sustainability:

  • Visibility and measurement:

    • Carbon Footprint: Provides dashboard visibility into greenhouse gas emissions associated with Google Cloud usage.

    • BigQuery: Analyzes exported Carbon Footprint data alongside billing data to identify emission hotspots.

  • Infrastructure and operations:

    • Google Cloud Region Picker: Helps weigh carbon footprint, cost, and latency when selecting deployment locations.

    • Active Assist / Recommender: Automatically identifies idle resources and provides VM rightsizing recommendations to reduce waste.

    • Cloud Run / GKE Autopilot: Fully managed compute environments that optimize cluster usage and can scale to zero when idle.

    • Cloud Batch: Optimizes the scheduling of batch jobs, allowing execution during periods of high Carbon-Free Energy.

    • Spot VMs: Utilizes unused data center capacity for fault-tolerant workloads, improving overall hardware efficiency.

  • Data and AI:

    • Cloud Storage Lifecycle Management: Automatically transitions older data to lower-energy storage classes (Nearline, Coldline, Archive).

    • Cloud TPUs: Specialized hardware optimized for the energy efficiency of large-scale AI/ML matrix multiplications.

Workload assessment questions

Ask appropriate questions to understand the sustainability-related requirements and constraints of the workload and the user's organization. Choose questions from the following list:

  • Cloud sustainability:

    • How do you define the boundaries of sustainability responsibility between your organization and your cloud provider?

    • How do you leverage cloud capabilities and AI to drive sustainability outcomes for your broader business operations?

    • How does your cloud strategy account for the sustainability impact of your partner ecosystem and multi-cloud environments?

  • Use regions that consume low-carbon energy:

    • How do you incorporate carbon intensity into your Google Cloud region selection strategy?

  • Optimize AI and ML workloads:

    • How do you optimize the energy efficiency of your AI and machine learning lifecycles?

  • Optimize resource usage:

    • How do you ensure your infrastructure footprint dynamically matches actual workload demand?

    • How do you select and maintain the hardware types used for your cloud workloads?

    • What is your strategy for handling non-urgent or compute-intensive background tasks?

    • How do you balance the need for high availability and disaster recovery with sustainability?

  • Develop energy-efficient software:

    • How do you ensure your backend logic minimizes unnecessary CPU, memory, and network activity?

    • How do you manage the overall efficiency and maintenance of your codebase for sustainability?

    • How do you minimize the data volume and processing load that your application places on end-user devices?

    • How does your user experience (UX) design contribute to energy efficiency for the end user?

  • Optimize data and storage:

    • What process do you have for managing the environmental footprint of your data and storage?

  • Continuously measure and improve:

    • How do you analyze your carbon data to prioritize optimization efforts?

    • How is sustainability measurement embedded into your organization’s governance and culture?

    • What is your current process for gaining visibility into your cloud-related carbon emissions?

    • What proactive steps do you take to remediate identified carbon hotspots?

  • Promote a culture of sustainability:

    • How do you connect individual technical decisions to the organization's mission and hold teams accountable for results?

    • How do you ensure your technical and business staff have the specific skills required to implement sustainability practices?

Validation checklist

Use the following checklist to evaluate the architecture's alignment with sustainability recommendations:

  • Cloud sustainability:

    • [ ] The organization embraces a shared responsibility and shared fate model for sustainability.

    • [ ] AI is used as a catalyst for profitability and resilience to streamline operations, or sustainability is integrated into the design process to create positive feedback loops.

    • [ ] Collaborations with sustainable partners are prioritized and multi-cloud data portability is leveraged, or internal practices align with recognized global standards like the Green Software Foundation.

  • Use regions that consume low-carbon energy:

    • [ ] A data-driven policy prioritizes regions with high Carbon-Free Energy (CFE%) and "Low CO2" indicators, or the Google Cloud Region Picker is actively used to balance carbon footprint with cost and latency.

  • Optimize AI and ML workloads:

    • [ ] Algorithmic needs are matched to specialized hardware (TPUs) to maximize computations per watt, or mathematical techniques like model compression and PEFT are applied to reduce computational complexity.

  • Optimize resource usage:

    • [ ] Fully managed services that scale to zero when idle are utilized, or Horizontal Pod Autoscaling (HPA) and Vertical Pod Autoscaling (VPA) are used in GKE to prevent over-provisioning.

    • [ ] A formal process exists to upgrade to the newest machine types for improved performance-per-watt, or workloads are actively matched to specialized machine families.

    • [ ] Batch jobs are proactively scheduled to run during periods or in regions with the highest proportion of CFE, or Spot VMs are utilized for non-critical batch jobs.

    • [ ] "Cold DR" or serverless failover is prioritized to ensure secondary regions remain at zero energy consumption until an event occurs, or Infrastructure as Code (IaC) is used to rapidly provision a recovery environment only when needed.

  • Develop energy-efficient software:

    • [ ] Resource-intensive busy loops or constant polling are replaced with event-driven logic, or algorithms with optimal time complexity and data structures are prioritized.

    • [ ] The "Don't Repeat Yourself" (DRY) principle is adhered to with regular refactoring, or intelligent caching (e.g., Memorystore) is implemented with smart eviction policies.

    • [ ] The download size of website products is measured and maintained against a strict budget, or CI/CD pipelines automate the minimization and compression of HTML, CSS, and JS files.

    • [ ] Static sites or Progressive Web Apps (PWAs) are preferred for faster loading, or DOM manipulation is minimized to reduce device power consumption.

  • Optimize data and storage:

    • [ ] Object Lifecycle Management is used to automatically move cold data to Archive storage, or discovery techniques (e.g., Dataplex) are used to identify and eliminate "dark data".

  • Continuously measure and improve:

    • [ ] Carbon data is analyzed by project, region, and service to identify gross emitters, or carbon data is joined with Billing data in BigQuery to correlate cost and environmental impact.

    • [ ] A formal GreenOps function defines accountability for carbon reduction targets, or verified Carbon Footprint data from BigQuery supports formal ESG disclosures.

    • [ ] Applications are instrumented to measure the specific carbon intensity of software features, or automated exports of Carbon Footprint data to BigQuery are configured for deep analysis.

    • [ ] The unattended project recommender and Active Assist are regularly used to decommission idle resources, or proactive projects re-architect hotspots by shifting workloads to low-carbon regions.

  • Promote a culture of sustainability:

    • [ ] Abstract carbon metrics are transformed into tangible progress indicators in annual reports, or sustainability is treated as a first-class technical requirement (NFR) tied to KPIs and performance reviews.

    • [ ] Training tailored to specific job roles (e.g., developers on code efficiency, FinOps on carbon unit economics) is provided, or teams are formally trained to access and interpret carbon footprint data.

Files

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Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

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Attribution and licence

Published by
Google
Publisher
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Source
google/skills · skills/cloud/google-cloud-waf-sustainability · commit 2194d3b
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Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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