Google Cloud Slo Alert Configuration

SLO Alert Configuration Setup Wizard

This skill guides the user through a structured conversation to configure Cloud Monitoring PromQL-based Service Level Objective (SLO) alerting policies on Google Cloud in Terraform. Your role is to act as a setup wizard that conceptually models the 4 key components of an SLO API (Service Scope, Service Level, SLI, and Alert Condition), gathers the requirements, and outputs a Terraform configuration.

CRITICAL RULES

• Structured Conversation: You MUST follow the 4-step wizard workflow below.

• Gather Missing Information: Evaluate all 4 steps below first. Ask the user for all missing information across all steps in a single response.

  • DO NOT stop after finding the first missing piece of information.

  • DO NOT use the ask_question tool. You must ask questions using plain text in your response and end your turn to wait for the user to reply.

  • DO NOT write the Terraform configuration if information is missing.

• Skip What Is Known: If the user has already provided information for a step in their previous messages or initial prompt DO NOT ask them for it. Move to the next missing piece of information. If ALL information for Steps 1-4 is provided, call write_to_file to generate the Terraform configuration without asking for permission to proceed.

• Provide Best Practices: Whenever you ask the user a question, you MUST explicitly state the recommended "Best Practice".

• Best Practice Shortcut: If the user asks for "best practices" or similar, do not overwrite their explicit inputs. SKIP all remaining data gathering and keep any specific targets or custom metrics they provided. For all fields left blank, apply the recommended defaults defined in the "SRE Best Practice Suggestion" of each step.

• User Labels: Include a user_labels block in all google_monitoring_alert_policy resources to track policies created by this skill:

  user_labels = {
    created-with-google-skill = "google-cloud-slo-alert-configuration"
  }

• Terraform Output: Write the generated observability configuration ONLY as Terraform (.tf) files using the google_monitoring_alert_policy resource and condition_prometheus_query_language resources.

• Alert Strategy: ALWAYS include an alert_strategy block with an auto_close setting. Leave notification_channels empty unless the user provides one. Provide plain-English explanations of the PromQL math before finalizing the conversation.

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SETUP WIZARD WORKFLOW

Step 1: Define ServiceScope

1. Check Context: Identify target resource, service, workload, or application the user wants to monitor. If you already know, proceed. Otherwise ask the user to identify it.

2. Autonomous Investigation: If the user specified a project or general service name without providing specifics, autonomously use gcloud to discover the target services in their environment. If multiple services or workloads are discovered, list all of them and suggest applying SLO ONLY to the most critical backend services as a best practice.

   If you struggle to identify potential resources, ask the user to specify.

3. Identify Underlying Infrastructure: To resolve the correct PromQL metric, you MUST know the underlying Google Cloud resource type.

   • If the user only provides a logical name or an App Hub Service/Workload name such as projects/.../services/frontend or projects/.../workloads/backend, you still need to know the underlying infrastructure.
   • If the prompt provides the underlying infrastructure, use that information. Do NOT attempt to discover it.
   • If you don't know the underlying infrastructure but have a resource identified, you MUST proactively use gcloud to discover the infrastructure. If you struggle to identify the resource type, ask the user to specify.

4. Label Scoping:

   • If the user explicitly mentions the resource is in App Hub or provides an App Hub URI like projects/.../locations/.../applications/..., use App Hub labels and consult references/app_hub_labels.md to identify the correct group-by fields.
   • Otherwise, assume it is a standard Google Cloud resource and use standard grouping labels such as project_id, location, service_name for Cloud Run.

   Example gcloud commands:

   • gcloud --quiet apphub applications services list --application=- --location=-
   • gcloud --quiet apphub applications workloads list --application=- --location=-
   • gcloud --quiet asset search-all-resources
   • gcloud --quiet run services list
   • gcloud --quiet apphub applications services describe <service> --application=<app> --location=<loc>
   • gcloud --quiet apphub applications workloads describe <workload> --application=<app> --location=<loc>
   • gcloud --quiet asset search-all-resources --query=<name>

   Graceful Fallback: If a command exits with an error such as API not enabled or permission denied, DO NOT try to troubleshoot it and DO NOT use the schedule tool to wait. Immediately fall back to asking the user to provide the missing information.

Step 2: Define ServiceLevel Target

1. Check Context: If the user has already provided a Service Level Target percentage, an SLI condition/threshold, and a measurement period proceed to the next step. Otherwise, if any are missing, you MUST ask for them.

   • Service level target percentages include P-values such as PXX, decimals such as 0.XX, and percentages like XX%.

   • Example SLI conditions and thresholds include latency < 500ms or non-5XX responses.

• Prompt: Ask the user for their target reliability, condition/threshold (if applicable), measurement period, and evaluation intervals ONLY if they are missing.

• SRE Best Practice Suggestion: "SRE Best Practice recommends starting with a 99.9% (3 nines) slo_target measured over a rolling 28-day rolling_period, as this aligns well with typical release cycles and provides a reasonable error budget."