Cloud Monitoring Alert Configuration

GoogleOfficialPublished by Google

Skill
Workflow
Cloud and DevOps
Apache-2.0
Low risk
Has scripts

From the author

Configures Cloud Monitoring alerting policies using Terraform and PromQL across Cloud Run, Google Kubernetes Engine (GKE), Compute Engine, AI Hypercomputer reservations, Vertex AI Agent Engine, and generic Google Cloud resources. Use when creating, reviewing, migrating, or deduplicating Terraform alerting policies for latency, traffic, error rates, saturation, host or reservation maintenance, GPU XID insights, or AI agent telemetry. Don't use for standalone PromQL query authoring without alerting, log querying, or billing cost audits.

— the description by Google

AutoGineer summary

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

This skill provides a structured workflow for defining, reviewing, and managing Cloud Monitoring alerting policies. It leverages Terraform for infrastructure-as-code and PromQL for metric conditions, ensuring consistent observability across Google Cloud resources including Cloud Run, GKE, Compute Engine, and AI Agent platforms. The skill enforces best practices like the Four Golden Signals, dynamic grouping, and proper labeling, while providing clear paths for delegating SLO-specific configurations and notification channel management.

What it does

Generates parameterized Terraform configurations for Cloud Monitoring alert policies.
Implements PromQL-based conditions for high-signal monitoring.
Provides resource-specific guidance for GKE, Cloud Run, Compute Engine, and AI agents.
Enforces standardized labeling for all generated alerting resources.
Integrates with specialized scripts to analyze traffic patterns for AI agent telemetry.
Delegates SLO and notification channel configuration to dedicated skills.

When to use it

When creating or migrating Terraform-based alerting policies for Google Cloud infrastructure.
When setting up monitoring for AI agent reliability, quality, and cost telemetry.
When deduplicating existing alert policies to improve signal-to-noise ratios.
When configuring alerts for latency, traffic, error rates, or saturation across cloud services.

Advantages

Ensures consistent observability standards across diverse cloud environments.
Reduces manual configuration errors through structured Terraform templates.
Promotes best practices like dynamic grouping and the Four Golden Signals.
Maintains clear separation of concerns by delegating SLO and notification tasks.
Provides data-driven traffic analysis for accurate AI agent alert thresholds.

Usage examples

“Create a Terraform alerting policy for high latency on my GKE service.”: The agent will guide you through selecting the appropriate GKE PromQL queries, generate the google_monitoring_alert_policy Terraform resource, and prompt you to attach notification channels.
“Configure a cost alert for my Vertex AI Agent based on token usage.”: The agent will run the traffic analysis script to determine your workload pattern and generate a PromQL-based alert policy tailored to your agent's historical token consumption.

Install

Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/google/cloud-monitoring-alert-configuration/download

Unzip it into the cross-client skills folder of your project:

mkdir -p .agents/skills && unzip cloud-monitoring-alert-configuration.zip -d .agents/skills

Documentation by Google

Configure Cloud Monitoring Alerts

Configure high-signal alerting policies in Cloud Monitoring using Terraform (google_monitoring_alert_policy) and PromQL (condition_prometheus_query_language).

Usage Attribution

For all gcloud CLI, REST API, and MCP attribution requirements when executing commands with this skill, follow the instructions in Usage Attribution.

Enforce Core Alerting Rules

Apply the Four Golden Signals: Cover latency, traffic, errors, and saturation for the target workload.
Use Dynamic Grouping: Aggregate metrics by resource labels and parameterize thresholds with Terraform variables. Never hardcode ephemeral pod or instance identifiers.
Default to PromQL: Use Prometheus Query Language (PromQL) for metric conditions, and use log-based alerts (condition_matched_log or google_logging_metric) for lifecycle and audit events.
Attach Skill Attribution Labels: Always include user_labels with created-with-google-skill = "cloud-monitoring-alert-configuration" on every generated google_monitoring_alert_policy resource.
Require User Confirmation Before Mutating Resources: Never execute commands that modify infrastructure, enable telemetry, create online monitors, or apply Terraform without waiting for explicit user confirmation.
Prompt for Notification Channels: Alerting policies without notification channels incur charges without notifying anyone during an incident. Whenever notification channels are not specified in the user prompt across any workflow, explicitly inform the user that no notification channels are attached to the alerting policies, advise them to configure and attach notification channels before deploying, and ask at the end of your response if they want to configure notification channels.

Route by Target Resource Type

Identify the target resource type and read only the matching reference file directly. Never run directory listings or workspace file searches to look for queries or .tf files unless the prompt explicitly provides a file path. If a user asks to fix or validate a PromQL query without providing the query text or a file path, construct a representative broken-and-corrected query example directly.

Configure Cloud Run Alerts

HTTP Services: cloud_run_services.md
Batch Jobs: cloud_run_jobs.md
Worker Pools: cloud_run_worker_pools.md

Configure Google Kubernetes Engine Alerts

PromQL Queries and Golden Signals: GKE PromQL Queries
Metrics Catalog and Open-Source Alerts: GKE Metrics
Prerequisites and Setup: GKE Setup

Configure AI Agent Alerts

Reliability: Reliability
Quality: Quality
Cost: Cost
Safety: Safety
Security: Security
Telemetry Enablement: Telemetry
Traffic Profiles: Historical and No History

Configure Compute Engine and AI Hypercomputer Alerts

Compute Engine VMs, MIGs, GPU XID Insights, Host or Reservation Maintenance (type=scheduled, unscheduled, or multiple), and Machine or Domain Health: compute_engine_alerts.md

Configure Generic Resource Alerts

Generic Workloads, Custom Metrics, MQL Migration, and HCL Deduplication: generic_alert_policies.md

Delegate Service Level Objective Alerts

Delegate to the google-cloud-slo-alert-configuration skill. That skill owns the SLO setup wizard, the burn-rate PromQL templates (references/promql_templates.md), the SLI metric mapping (references/service_metrics.md), and the App Hub scoping labels (references/app_hub_labels.md). Do NOT duplicate those templates here or claim that this skill owns its own SLO PromQL template library.

Delegate whenever the user asks for an availability or latency SLO, an error budget or burn-rate alert, or a Service Level Objective on any resource. When SLOs are only one part of a broader request (for example, a full production alerting suite for a Cloud Run service), delegate the SLO policies and continue generating the remaining golden-signal policies from the resource-specific reference in the same response.

Every resource reference carries a Context to hand off list naming the SLI metric, its good or bad filter, and the grouping labels — under a ## Delegate Service Level Objective Alerts heading for Cloud Run and generic resources, and inside the SLO section itself for GKE and Agent Platform. Read it and pass that context on, so the SLO skill does not repeat discovery you have already done. When delegating, you MUST also:

State the SLO target and window explicitly if the user gave them, or let the SLO skill apply its SRE defaults (99.9% over a rolling 28 days) if they did not.
Require the returned policies to carry user_labels with created-with-google-skill = "cloud-monitoring-alert-configuration" so attribution stays consistent with the rest of the generated suite. This literal value does NOT change when you delegate: the label records the entry-point skill, not whichever skill supplied the template. Do NOT set created-with-google-skill = "google-cloud-slo-alert-configuration" on policies generated through this delegation.
Preserve the burn-rate duration contract, which google-cloud-slo-alert-configuration does not specify: Fast Burn (1h and 5m windows, factor 14.4) carries duration = "300s", and Slow Burn ([3d] and [6h] lookback, factor 1.0) omits the duration field entirely — do not emit duration = "0s" in its place. Cloud Monitoring rejects a non-zero retest buffer when the lookback window exceeds 25 hours.
When no notification channels are provided, inform the user that no notification channels are attached to the alerting policies, advise them to configure and attach notification channels, and ask if they want to configure notification channels.

[!IMPORTANT] Always tell the user about the referral, even on the fallback path. Some reference files in this skill include standalone burn-rate templates to use when the google-cloud-slo-alert-configuration skill is not installed. If you emit policies from those fallback templates, your response MUST still tell the user that google-cloud-slo-alert-configuration owns SLO configuration, state that the emitted policies use the local fallback templates, and list the handoff context. Never silently present SLO policies as if this skill owned them.

[!NOTE] Exception: Agent Platform (OpenTelemetry-instrumented AI agents on any runtime, including Vertex AI Agent Engine and Cloud Run) is the one resource whose Fast and Slow burn policies live in this skill and remain authoritative. Delegate only for SLO customization — see Reliability.

Notification Channels: Delegate to cloud-monitoring-notification-channels-configuration to configure email, PagerDuty, Pub/Sub, or Slack channels. When an alerting policy has no notification channels attached, inform the user that no notification channels are attached and that they should configure notification channels and attach them to the alerting policy.
PromQL Authoring: Delegate to cloud-monitoring-promql-query for standalone PromQL query authoring or metric exploration.

Follow the Alert Configuration Workflow

Discover: Identify target resources, metric schemas, and scoping projects using the lookup commands in the matching reference file (such as discover_metric_schema.py for Compute Engine metrics and other Google Cloud resource metrics, or gather_agent_info.py, check_telemetry.py, analyze_traffic.py, create_online_monitor.py, list_trace_scope_table_names.py, and list_log_scope_table_names.py for AI agents).
Route: Load the reference file for the target resource type or delegate SLOs to google-cloud-slo-alert-configuration.
Generate: Produce parameterized Terraform HCL configurations with required user_labels.
Validate: When the loaded reference file requires a validation script (such as lint_promql.py, validate_config.py, lint_syntax.py, or scan_duplicates.py), run or print that exact script command. Do not list or search the scripts/ directory.
Explain and Prompt: Explain each alert policy in plain English. When no notification channels are provided, tell the user that no notification channels are attached to the alerting policy, advise them to configure and attach notification channels, and ask if they want to configure notification channels.

Files

ATTRIBUTION.mdAdded by AutoGineer890 B
LICENSELicence file added by AutoGineer11.1 KiB
SKILL.md11.1 KiB
references/agent_platform_cost_alert_policies.md9.7 KiB
references/agent_platform_has_historical_traffic_data.md5.8 KiB
references/agent_platform_no_historical_traffic_data.md4.1 KiB
references/agent_platform_quality_alert_policies.md12.8 KiB
references/agent_platform_reliability_alert_policies.md28.5 KiB
references/agent_platform_safety_alert_policies.md7.0 KiB
references/agent_platform_security_alert_policies.md6.8 KiB
references/agent_platform_telemetry_enablement.md2.6 KiB
references/attribution.md1.8 KiB
references/cloud_run_jobs.md5.3 KiB
references/cloud_run_services.md37.8 KiB
references/cloud_run_worker_pools.md13.7 KiB
references/compute_engine_alerts.md40.5 KiB
references/generic_alert_policies.md18.9 KiB
references/kubernetes_gke_configuration_prerequisites.md13.3 KiB
references/kubernetes_metrics_and_alerts_catalog.md22.0 KiB
references/kubernetes_promql_queries.md18.2 KiB
scripts/agent_platform/analyze_traffic.pyScript12.8 KiB
scripts/agent_platform/analyze_traffic_test.pyScript7.8 KiB
scripts/agent_platform/check_telemetry.pyScript5.8 KiB
scripts/agent_platform/check_telemetry_test.pyScript5.4 KiB
scripts/agent_platform/config_utils.pyScript9.4 KiB
scripts/agent_platform/config_utils_test.pyScript10.2 KiB
scripts/agent_platform/create_online_monitor.pyScript5.8 KiB
scripts/agent_platform/create_online_monitor_test.pyScript5.7 KiB
scripts/agent_platform/gather_agent_info.pyScript14.8 KiB
scripts/agent_platform/gather_agent_info_test.pyScript11.0 KiB
scripts/agent_platform/lint_syntax.pyScript1.9 KiB
scripts/agent_platform/list_log_scope_table_names.pyScript4.7 KiB
scripts/agent_platform/list_log_scope_table_names_test.pyScript8.6 KiB
scripts/agent_platform/list_trace_scope_table_names.pyScript4.3 KiB
scripts/agent_platform/list_trace_scope_table_names_test.pyScript6.4 KiB
scripts/agent_platform/mock_traffic_data.pyScript100.4 KiB
scripts/agent_platform/requirements.txt102 B
scripts/agent_platform/scan_duplicates.pyScript3.7 KiB
scripts/agent_platform/scan_duplicates_test.pyScript3.9 KiB
scripts/discover_metric_schema.pyScript13.1 KiB
scripts/discover_metric_schema_test.pyScript10.4 KiB
scripts/kubernetes/validate_config.pyScript9.2 KiB
scripts/lint_promql.pyScript2.5 KiB

references/agent_platform_has_historical_traffic_data.md

Analyze Historical Traffic Data

Follow these instructions when the agent has historical metrics data available.

Follow Critical Instructions: Lines 13-89
- Run the Traffic Analyzer Script: Lines 15-57
- Map Metrics to Algorithms: Lines 58-89
Notify the User: Lines 90-106
Avoid Common Pitfalls: Lines 107-112

Follow Critical Instructions

Run the Traffic Analyzer Script

Run the analyze_traffic.py script to classify the metrics traffic pattern profile:
- Gather arguments: Use different arguments in your call depending on the target policy you are running the tool for:
  - Latency: Use workload.googleapis.com/gen_ai.invoke_agent.duration as the --metric-type and assign the value of the agent's metric.labels.gen_ai_agent_name as the --reasoning-engine-id.
  - Rapid Token Burn Rate: Use workload.googleapis.com/gen_ai.client.token.usage as the --metric-type and assign the value of the agent's resource.labels.namespace as the --reasoning-engine-id.
- Running the tool: Use one of the following commands (adjust the script path if running from a parent directory):
  - Live Query: python3 scripts/agent_platform/analyze_traffic.py --live --project-id {project_id} --reasoning-engine-id {reasoning_engine_id} --metric-type={metric_type}
  - Metrics File: python3 scripts/agent_platform/analyze_traffic.py --metrics-file {path_to_json} --metric-type={metric_type}
- Parallel Traffic Analysis: If you need to run live traffic analysis for both latency and token usage, call the tool for each case concurrently using background tasks.
- Handling Tool Failures:
  - If the --live command fails with CredentialsMissingError (exit code 1), report the error and instruct the user to run gcloud auth application-default login on their terminal.
  - For other unexpected failures, analyze the error message (such as connection timeouts, invalid permissions, or missing resources). Attempt to dynamically correct parameters (such as verifying or correcting the region, project ID, or metric type) and retry execution before escalating.
Map the traffic pattern profile classified by the script to the corresponding policy:
- Steady / Consistent: Maps to Long-Window Z-Score Baseline (1w Z-Score Baseline, 1-week lookback) (safe since the script verified at least 14 days of history).
- Seasonal / Cyclical: Maps to Seasonal Decomposition (average 1w and 1d).
- Bursty / Inconsistent: Maps to Moving Averages (1h baseline).

Map Metrics to Algorithms

Variance Ratio (`std_dev / mean`)	Autocorrelation (1-week lag)	Traffic Classification	Assigned Latency Algorithm and Baseline
`≤ 2.0`	`≤ 0.75`	Steady / Consistent	Long-Window Z-Score (`1w Z-Score Baseline`, 1-week lookback)
`≤ 2.0`	`> 0.75`	Seasonal / Cyclical	Seasonal Decomposition (average 1w and 1d)
`> 2.0`	Any / Not Applicable	Bursty / Inconsistent	Moving Averages (1-hour window)

Example classifications:

Steady / Consistent: Low variance data (such as steady queries per second) with little or no weekly cyclical pattern.
Seasonal / Cyclical: Clear daily or weekly repeating patterns with high weekly correlation (such as daily peak traffic).
Bursty / Inconsistent: Highly volatile data with rapid spikes and quiet periods (such as batch job workloads).
Fallback for Insufficient Data / No Traffic:
- If the script fails with a ValueError indicating insufficient data points (less than 14 days of history), or if it outputs "New Agent / No Traffic" (inactive agent), you MUST fall back to the user inquiry instructions in agent_platform_no_historical_traffic_data.md to ask the user for the expected traffic pattern.
Regardless of the script's output profile, the other policies MUST use their correct data-class defaults:
- Error Rate: ALWAYS use Multi-Window Multi-Burn Rate SLO Alerting (or ratio-based static limits).

Notify the User

Clearly communicate the findings and selection at the start of your response:

Explain the classified profile (Seasonal / Cyclical, Steady / Consistent, or Bursty / Inconsistent) output by the metrics analysis script (citing indicators such as standard deviation, autocorrelation, or zero-ratio from the script output). If falling back to user inquiry due to zero metrics or insufficient data, explain that.
Propose the corresponding alerting policy mapping (Latency matching the traffic profile, Error Rate using SLO Burn Rate).
Ask the user whether this expected profile mapping is correct or if they would like to customize standard deviation thresholds.
Provide a brief plain-English explanation of what each of the proposed alerts measures and how the underlying algorithms work. Keep this explanation in the conversational response text.

Avoid Common Pitfalls

Sparse Traffic Guidance: If zero_ratio > 0.95 (even if the profile is "Steady / Consistent"), warn the user that Z-score alerts may be unstable. Recommend using Short-Window Z-Score (1h baseline) or Static Thresholds instead of Long-Window Z-Score (1w Z-Score Baseline).

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.

Needs: Needs Python

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Attribution and licence

Published by

Google

Publisher

Google (Official)

Source

google/skills · skills/cloud/cloud-monitoring-alert-configuration · commit 2194d3b

Licence

Apache License 2.0 (redistribution allowed)

LICENSE (added by AutoGineer from LICENSE)
ATTRIBUTION.md (written by AutoGineer)

Synced

2026-11-08

AutoGineer is not affiliated with or endorsed by Google.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download cloud-monitoring-alert-configuration.zip