SLO Alert Configuration Setup Wizard

This skill guides the user through a structured conversation to configure PromQL-based Service Level Objective (SLO) alerting policies 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>

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."

Step 3: Define ServiceLevelIndicator / SLI

1. Check Context: Has the user specified the exact metric name such as run.googleapis.com/request_count? If yes, proceed to the next step. Otherwise, if the user only says "availability" or "latency" without specifying the EXACT metric name, you may infer the name from the service type provided a metric for that type is defined in the references. If the user provides a custom metric and a threshold, assume it is a Distribution metric and do not ask for further metric details.

   • You MUST output valid metrics defined in references/service_metrics.md. If the exact resource type and metric is not listed, check the public documentation in references/service_metrics.md to find the exact metric. If you still cannot find it, you MUST stop and ask the user to provide the custom metric.

2. Prompt: Ask the user what specific metric they want to use. You MUST suggest the inferred standard metric as the recommended best practice. When interpreting incomplete requests, you MUST explicitly propose the specific metric string and describe the ratio-based or window-based definition to the user for confirmation before proceeding.

3. Metric Mapping: Consult references/service_metrics.md to find the exact PromQL metric string for the Resource Type identified in Step 1 section 3. If the requested metric type does not exist for the resource in the references or the primary public documentation, you MUST explicitly inform the user that there is no default metric and ask them to provide the specific custom metric name. You MUST provide guidance on how a custom latency metric might be structured.

   • CRITICAL: If the primary documentation does not list a default metric, you MUST NOT try to piece together advanced metrics. Ask the user to provide the custom metric.

4. Evaluation Method: Default the EvaluationType to REQUEST_BASED unless the user specifically describes a window-based requirement, typically denoted by "good minutes" or "bad minutes".

   • Window-Based Lookback Period: If the user indicates a window-based evaluation, you need to know the duration of the lookback windows and the evaluation interval for each window. You MUST ask the user to specify both the lookback duration and the evaluation interval if they have not already provided them. You CANNOT generate an alerting policy without this configuration.

Step 4: Define Alerting Policy

1. Check Context: Has the user specified burn rates? If yes, proceed to the next step. Otherwise, ask the user to specify a burn rate strategy and provide a best practice suggestion.

2. SRE Best Practice Suggestion: SRE Best Practice recommends both a multi-window fast burn and multi-window slow burn.

   • Multi-Window Fast Burn: Factor 14.4 over 1h and 5m windows, catching severe outages quickly without false positives.

   • Multi-Window Slow Burn: Factor 1 over 3d and 6h windows, catching system degradation.

Step 5: Generate Configuration

1. Look up the corresponding PromQL template from references/promql_templates.md based on the user's choices. Use a Window-Based template for window-based SLOs.
2. Populate the template with the ServiceScope labels, ServiceLevel targets, and ServiceLevelIndicator metrics.
3. Wrap it in Terraform (google_monitoring_alert_policy), ensuring the user_labels block includes created-with-google-skill = "google-cloud-slo-alert-configuration".
4. Present the .tf block with a plain English explanation of the math.
5. In the final summary, inform the user that the alert policies have been tagged with the created-with-google-skill = "google-cloud-slo-alert-configuration" user label to track policies created by this skill.
6. CRITICAL: Explicitly warn the user in the final summary if no notification channels are configured. Inform them that you can assist with setting those up if they would like.

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Supporting Links

• Google SRE Workbook: Alerting on SLOs
• Google Cloud Operations: SLO Monitoring
• Prometheus: PromQL Basics

Reporting Issues

Report bugs or improvements for this skill at Google Skills Issues.