
Google Cloud Load Balancer Skill
FreeStreamline your Google Cloud load balancer configurations.
Free · Opens the source repo
What Google Cloud Load Balancer Skill does
The Google Cloud Load Balancer Configuration Skill provides a structured approach to designing and deploying global external Application Load Balancers on Google Cloud. This skill guides users through a six-step discovery process, ensuring that configurations align with best practices while accommodating specific workload requirements. It integrates seamlessly with Google Cloud services like Cloud CDN, Cloud Armor, and Service Extensions, making it an essential tool for developers and system architects working with cloud infrastructure.
This skill is particularly useful for those looking to implement load balancing solutions that optimize performance and security. It assists in discovering existing resources such as Cloud Storage, Managed Instance Groups (MIGs), Google Kubernetes Engine (GKE), and Cloud Run services, which can be utilized as backends for the load balancer. Additionally, the skill can generate production-grade Terraform HCL scripts or gcloud CLI commands, simplifying the deployment process and ensuring that configurations are reproducible.
The guided process is designed to minimize complexity by progressively revealing options based on user inputs. This allows users to focus on essential decisions without being overwhelmed by technical details. The skill also includes features for detecting and reconciling configuration drift, ensuring that the deployed load balancer remains aligned with the intended configuration over time. This is particularly beneficial for teams managing large-scale deployments where configuration changes can occur frequently.
Ideal for cloud engineers, DevOps professionals, and system architects, this skill enhances productivity by automating much of the configuration process. It is a valuable addition to any toolkit for those working within the Google Cloud ecosystem, enabling users to design robust, scalable, and secure load balancing solutions efficiently.
When to use it
Use this skill when designing or deploying Google Cloud global external Application Load Balancers, especially when integrating with Cloud CDN and Cloud Armor.
When not to use it
This skill is not suitable for non-Google Cloud load balancing configurations or purely regional/internal setups unless part of a global design.
What you can build with it
Deploying a New Load Balancer
Use this skill to walk through the steps of configuring a new global external Application Load Balancer, ensuring all best practices are followed.
Integrating Cloud CDN
Leverage the skill to configure Cloud CDN settings based on the specific workload type, optimizing performance and caching.
Detecting Configuration Drift
Utilize the skill to analyze and reconcile configuration drift on existing global external Application Load Balancers, maintaining alignment with intended settings.
How to install Google Cloud Load Balancer Skill
View source1. Install with the skills CLI
npx skills add google/skills/google-cloud-global-frontend-configuration --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by googleGoogle Cloud global external Application Load Balancer Configuration Skill
Purpose & Agent Guidance
This skill enables the agent to guide users through a structured, 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers (incorporating Cloud CDN, Cloud Armor, and Service Extensions).
Assumptions & Target Environments:
- This skill assumes it is called from environments (such as Gemini CLI, Antigravity, etc.) where the Google Cloud CLI (
gcloud) can be executed. - If
gcloudis not available or accessible, the skill cannot perform automated resource discovery or managed deployment actuation. In such environments, the skill will limit its support to guiding the design and generating Terraform HCL configurations.
When executing this skill, the agent must:
- Map user workload requirements to simplified, opinionated best-practice configurations using actual Google Cloud product names.
- Progressively disclose details, hiding advanced complexity unless the user explicitly asks for customization.
- Leverage the reference documents in the
references/directory to perform resource discovery, code generation, actuation, and drift detection.
The 6-Step Configuration Flow
Step 1: Basics
- Project Discovery: Consult
references/resource-discovery.mdto auto-detect the Google Cloud project ID. Present the discovered project ID to the user. - Ask the user for the foundational details of their load balancer:
- Name & Description: What should we call this load balancer?
- Protocol Selection: Do they need HTTP, HTTPS, or both?
- Certificate Management: Do they want to use Google-managed certificates or bring their own existing certificates?
Step 2: Origin Configuration
Help the user define their backend workloads through a strictly sequential, step-by-step loop. Do NOT ask everything at once. All steps are mandatory.
- Sub-step A - Origin Setup: Ask if they have a single origin or need multi-origin support. Wait for response.
- Sub-step B - Origin Types: Ask them to select the backend types from: Cloud Storage Buckets, Compute Engine Managed Instance Groups (MIGs), Google Kubernetes Engine (GKE) Clusters, Cloud Run Services, or External/Internet origins (IP/FQDN). Wait for response.
- Sub-step C - Origin Definition Loop: Execute the following loop sequentially for EACH origin type selected in Sub-step B. Wait for the user to answer for one origin before asking about the next:
- Resource Discovery: For Google Cloud-native origins (Cloud Storage, MIGs, GKE, Cloud Run), consult
references/resource-discovery.mdto fetch resources. Present the list starting with 1. Create New, 2. NA. For External/Internet origins, just ask for the FQDN/IP. - Workload Type (CRITICAL): Immediately after they define the resource, ask exactly what type of workload is being served:
- Static Images / Objects (Static content, images, videos, styling assets)
- Cacheable API (Read-only, public APIs where cached data is acceptable)
- Uncacheable API / Transactions (Transactional endpoints, login, checkout, account changes)
- Dynamic Web (SSR) (Dynamic pages, server-side rendered apps, custom dynamic sessions)
- Resource Discovery: For Google Cloud-native origins (Cloud Storage, MIGs, GKE, Cloud Run), consult
- Sub-step D - Routing Rules: Once ALL origins have been fully defined one by one, ask how traffic should be routed between them (Path-based, header-based, or query-param-based). Wait for response.
- Sub-step E - Logging: After routing is established, ask if they want to enable Cloud CDN logging, and if so, at what sampling rate (0-100%). Wait for response.
Step 3: Traffic Management & Extensibility
- Provide a brief summary of the origins and routing rules defined in Step 2.
- Ask if they need to enable Advanced Traffic Management settings (such as granular weighted load balancing, traffic mirroring, or Cloud Load Balancing Service Extensions for custom WASM plugins / callouts), or if they want to proceed with Google Cloud Best Practice Configuration.
Step 4: Caching (Cloud CDN)
Propose a "Recommended Configuration" based entirely on the Workload Type from Step 2. Do not list the advanced settings (TTL, Cache Keys, Compression) unless they reject the recommendation and want to customize.
- If Workload = Static Images / Objects:
- Cache Mode: Cache All Static
- TTL: Client (1 day / 86400s), Default (30 days / 2592000s), Max (365 days / 31536000s) — balances long-term cache offload for static assets with periodic re-validation.
- Cache Key: Protocol + Host + Path (Ignore Query Strings)
- Compression: Enabled (Brotli & Gzip)
- Negative Caching: Enabled
- Serve while stale: Enabled
- If Workload = Cacheable API:
- Cache Mode: Use Origin Headers
- TTL: Managed by Origin (Omitted from configuration to prevent errors)
- Cache Key: Protocol + Host + Path + Include Query Strings
- Compression: Enabled (Gzip)
- Negative Caching: Enabled
- Serve while stale: Disabled
- If Workload = Uncacheable API / Transactions:
- Cache Mode: Disabled (CDN Bypassed)
- If Workload = Dynamic Web (SSR):
- Cache Mode: Use Origin Headers
- TTL: Managed by Origin (Omitted from configuration to prevent errors)
- Cache Key: Protocol + Host + Path
- Compression: Enabled (Brotli & Gzip)
- Cache Bypass: Bypass cache if session cookies (e.g., SESSID, JWT) are present
Step 5: Security (Cloud Armor)
Propose a "Recommended Configuration" based entirely on the Workload Type from Step 2. Keep advanced protection (Bot Management, Threat Intel, Geo-blocking) hidden unless requested.
- If Workload = Static Images / Objects:
- Rate Limiting: None (Standard Edge behavior; rate limiting is unsupported on Cloud Armor Edge policies for Cloud Storage buckets).
- OWASP Protection: Disabled
- If Workload = Cacheable API:
- Rate Limiting: 100 requests per minute per client IP (standard baseline to prevent API abuse and DDoS while accommodating normal interactive usage).
- OWASP Protection: Enabled (SQLi, XSS, Local File Inclusion)
- If Workload = Uncacheable API / Transactions:
- Rate Limiting: Strict 30 requests per minute per client IP (tight threshold to protect sensitive transactional endpoints like login/checkout from credential stuffing and brute-force attacks).
- OWASP Protection: Enabled (SQLi, XSS, Remote Command Execution, Session Fixation)
- Bot Management & Threat Intel: Enabled (Block malicious bots and known malicious IPs)
- If Workload = Dynamic Web (SSR):
- Rate Limiting: 120 requests per minute per client IP (generous threshold to accommodate burst requests for initial page loads and asset hydration in SSR web applications).
- OWASP Protection: Enabled (SQLi, XSS, CSRF, Shellshock)
- Geo-blocking: Optional (Restrict/allow specific country access)
Step 6: Review & Deploy
-
Configuration Summary: Generate a complete, formatted markdown table showing all finalized settings from Steps 1 through 5, using Google Cloud product names. Follow this exact template structure:
Component Parameter / Setting Value & Justification Load Balancer Name & Protocol <Name>(HTTP/HTTPS)Origins Backends & Workloads <Backend 1>(<Workload Type>),<Backend 2>...Traffic Management Routing Rules <Path / Header rules or Default>Cloud CDN Cache Mode & TTLs <Cache Mode>, Default TTL:<TTL>Cloud Armor Rate Limiting & OWASP <RPM Limit>, OWASP Rules:<Enabled/Disabled>Service Extensions WASM / Callouts <Enabled/Disabled or N/A> -
Next Action: Ask the user to choose their deployment/generation format (Terraform HCL or gcloud CLI Bash Script) and their next action:
- Show Code / Script (Display the HCL code or gcloud bash script. Once displayed, offer options to Download or Deploy/Execute)
- Download files (Save
main.tfordeploy.shto the local workspace) - Deploy Configuration: Initiate the deployment via Infrastructure Manager or execute the gcloud script. This should be done using the deployment instructions in
references/managed-deployment.md.
Relevant Documentation & Supportive Links
Frequently asked questions about Google Cloud Load Balancer Skill
Similar skills
Turborepo
Optimized build system for JavaScript/TypeScript monorepos.
Azure Pipelines Validation
Streamline your Azure DevOps pipeline changes locally.
Azure Developer CLI
Streamline your Azure project workflows with best practices.
Azure Container Registry CLI
Manage Azure Container Registry resources with ease.
Aspire
Build and orchestrate polyglot distributed applications seamlessly.
Vercel CLI
Manage and deploy Vercel projects from the command line.
