Google Cloud Slo Alert Configuration
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AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill acts as a structured setup wizard to help users define and configure Service Level Objective (SLO) alerting policies within Google Cloud. It guides the user through a four-step process: defining the service scope, setting service level targets, identifying the Service Level Indicator (SLI), and establishing alerting strategies. The skill automates the generation of Terraform code, specifically utilizing the google_monitoring_alert_policy resource and PromQL-based conditions, while ensuring best practices for SRE-focused reliability monitoring are applied.
What it does
- Guides users through a 4-step workflow to define SLO parameters.
- Generates Terraform (.tf) files for Cloud Monitoring alerting policies.
- Uses PromQL for defining alert conditions.
- Automatically includes required user labels for policy tracking.
- Provides SRE best practice suggestions for SLO targets and burn rates.
- Integrates with App Hub for resource scoping.
When to use it
- When you need to create a new SLO alerting policy in Google Cloud.
- When you want to standardize SLO configurations using Terraform.
- When you need assistance determining appropriate SLI metrics or burn rate strategies.
- When monitoring resources registered in Google Cloud App Hub.
Advantages
- Ensures consistent SLO configuration through a structured wizard.
- Reduces manual effort by generating ready-to-use Terraform code.
- Promotes SRE best practices by suggesting standard reliability targets.
- Simplifies complex PromQL alert logic for SLOs.
- Maintains auditability by tagging generated policies with specific user labels.
Usage examples
- “I need to set up an SLO alert for my backend service.”
- The agent will initiate the 4-step wizard, asking for the service scope, target reliability, SLI metrics, and burn rate preferences to generate the Terraform configuration.
- “Help me configure an SLO with SRE best practices.”
- The agent will skip manual data gathering and apply recommended defaults, such as a 99.9% target over 28 days, and generate the corresponding Terraform code.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google/google-cloud-slo-alert-configuration/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip google-cloud-slo-alert-configuration.zip -d .agents/skills
Documentation by Google
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.
Attribution
MUST READ: Before executing any gcloud, REST API, or MCP command, you
MUST read Usage Attribution. For commands executed
directly in the terminal (never in generated scripts, Terraform, or user code),
prefix gcloud with
CLOUDSDK_METRICS_REQUEST_ATTRIBUTION="agent-skills/1.0 (skill:google-cloud-slo-alert-configuration)"
and append agent-skills/1.0 (skill:google-cloud-slo-alert-configuration) to User-Agent headers.
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_questiontool. 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_fileto 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_labelsblock in allgoogle_monitoring_alert_policyresources 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 thegoogle_monitoring_alert_policyresource andcondition_prometheus_query_languageresources.Alert Strategy: ALWAYS include an
alert_strategyblock with anauto_closesetting. Leavenotification_channelsempty unless the user provides one. Provide plain-English explanations of the PromQL math before finalizing the conversation.
SETUP WIZARD WORKFLOW
Step 1: Define ServiceScope
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.
Autonomous Investigation: If the user specified a project or general service name without providing specifics, autonomously use
gcloudto 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.
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/frontendorprojects/.../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
gcloudto discover the infrastructure. If you struggle to identify the resource type, ask the user to specify.
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 consultreferences/app_hub_labels.mdto 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_namefor 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-resourcesgcloud --quiet run services listgcloud --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
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 < 500msornon-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_targetmeasured over a rolling 28-dayrolling_period, as this aligns well with typical release cycles and provides a reasonable error budget."
Step 3: Define ServiceLevelIndicator / SLI
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 inreferences/service_metrics.mdto find the exact metric. If you still cannot find it, you MUST stop and ask the user to provide the custom metric.
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.
Metric Mapping: Consult
references/service_metrics.mdto 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.
Evaluation Method: Default the
EvaluationTypetoREQUEST_BASEDunless the user specifically describes awindow-basedrequirement, 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.
SRE Best Practice Suggestion: SRE Best Practice recommends starting with two SLIs:
Availability: a
Ratio SLIcomparing successful requests typically defined asnon-5XXresponses, to total requests evaluated asREQUEST_BASED.Latency: a
Distribution SLIevaluated asWINDOW_BASEDsuch as 99% of 5-minute windows must meet a 300ms threshold.
Step 4: Define Alerting Policy
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.
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
Look up the corresponding PromQL template from
references/promql_templates.mdbased on the user's choices. Use aWindow-Basedtemplate for window-based SLOs.Populate the template with the
ServiceScopelabels,ServiceLeveltargets, andServiceLevelIndicatormetrics.Wrap it in Terraform (
google_monitoring_alert_policy), ensuring theuser_labelsblock includescreated-with-google-skill = "google-cloud-slo-alert-configuration".Present the
.tfblock with a plain English explanation of the math.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.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.
Supporting Links
Reporting Issues
Report bugs or improvements for this skill at Google Skills Issues.
Files
- ATTRIBUTION.mdAdded by AutoGineer890 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md12.4 KiB
- references/app_hub_labels.md2.1 KiB
- references/attribution.md1.9 KiB
- references/promql_templates.md10.5 KiB
- references/service_metrics.md2.0 KiB
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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
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Attribution and licence
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- google/skills · skills/cloud/google-cloud-slo-alert-configuration · 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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