Cloud Run Alert Configuration logo

Cloud Run Alert Configuration

OrganizationPopular
google
cloud-run-alert-configuration

Configures best-practice, high-signal alerting policies for Google Cloud Run resources (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKE workloads (use gke-alert-configuration) or Compute Engine VMs.

Overview

Publishergoogle
Repositoryskills
Skill namecloud-run-alert-configuration
Stars
20.1K
Forks
1.6K
Bundled files
3
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by google on GitHub. Read the source before you install it.

Installation

Install the Cloud Run Alert Configuration AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/google/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/cloud/cloud-run-alert-configuration .claude/skills/cloud-run-alert-configuration
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Cloud Run Alert Configuration in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Cloud Run Alert Configuration on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Cloud Run Alert Configuration is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Cloud Run Alert Configuration

Production-grade observability for Google Cloud Run using Terraform and PromQL (Cloud Monitoring). Grounded in SRE practices, this skill focuses strictly on actionable user impact and scaling bounds.


CRITICAL RULES

  • Prompt-First Fast Path (Skip Discovery When Named):
    • If the user prompt explicitly specifies the target Cloud Run service, job, or worker pool name (e.g., 'video-encoder', 'nightly-reconciliation', 'web-frontend', 'catalog-service', 'api-gateway', 'order-processor'), SKIP all workspace .tf file scanning (find_by_name, code_search, list_dir) and gcloud CLI discovery commands entirely.
    • Do NOT run gcloud, terraform, or file search tools when the target name is already provided in the prompt. Instead, parameterize the project ID (variable "scoping_project_id" { default = "my-gcp-project" }) and target resource name in Terraform variables and proceed immediately to Step 2 (Configure Alerts).
  • Autonomous Discovery (Only When Target Name is Omitted):
    • Never Scan Root Monorepo or Unbounded Directories: Never run find_by_name or ls across root workspace directories.
    • Config First: Only if the prompt omits the resource name, check .tf files in the immediate working directory for google_cloud_run_v2_service, google_cloud_run_service, or google_cloud_run_v2_job.
    • CLI Second (Graceful Fallback): Only if unconfigured in prompt or local .tf files, attempt gcloud config get-value project and gcloud run services list. If any gcloud command fails (e.g., auth or metadata errors) or terraform is missing, immediately stop running CLI commands and output parameterized HCL using explicit variable defaults.
  • Workload Routing: Always classify the workload target and follow its specific reference guide:
  • Explicit Defaults & User Overrides:
    • Always use explicit defaults for all constants specified in the target workload's reference file (SLO targets, latency thresholds, SLAs, saturation ceilings, rate guards).
    • State the defaults being applied in the final summary output and clearly notify the user that any default constant can be customized or overridden via Terraform variables or prompt input.
  • Metric Scope Centralization: Parameterize project = var.scoping_project_id in all Terraform google_monitoring_alert_policy resources so the policy can target either a single project or a centralized Cloud Monitoring Metrics Scope.
  • PromQL duration (Retest Window) Rules:
    • Lookbacks $\le$ 25h: Set duration = "300s" (5m buffer) to absorb transient blips and scale-up lag (except immediate job failure alerts which use duration = "0s").
    • Lookbacks $> 25$h (e.g. 3d/7d Slow Burn): Omit duration entirely (or set to 0s). Cloud Monitoring rejects PromQL queries with duration set on lookbacks >25h (INVALID_ARGUMENT).
  • Terraform Standards & Mandatory Labels:
    • Output clean, complete .tf configurations using google_monitoring_alert_policy and condition_prometheus_query_language directly in your response.
    • Mandatory User Labels: Every google_monitoring_alert_policy resource MUST include a user_labels block containing:
      hcl
      user_labels = {
        created-with-google-skill = "cloud-run-alert-configuration"
      }
    • Include alert_strategy { auto_close = "604800s" } and parameterize notification_channels = var.notification_channels.

WORKFLOW STEPS

1. Discovery & Target Identification

  • Fast Path (Target Named in Prompt): If the user prompt names the target Cloud Run service, job, or worker pool, skip all discovery commands and file searches and proceed directly to Step 2.
  • Discovery Fallback (Target Unnamed): Only if no resource name is provided in the prompt, check local .tf files or run gcloud to identify the target workload type and name. If gcloud auth fails, fall back immediately to default Terraform variables (var.scoping_project_id).

2. Configure Alerts

  • Route to the corresponding guide to generate the alert policies:
    • HTTP Services: Open services.md. Apply the requested alerting policy or standard suite covering availability SLOs (5xx), request latency (P95/P99), client errors (4xx), container instance saturation, container CPU/memory utilization, traffic anomalies (drop/surge), and billable instance time.
    • Batch Jobs: Open jobs.md. Apply immediate job execution failure alerts (duration = "0s").
    • Worker Pools: Open worker_pools.md. Apply the 4-policy standard suite (Task Success SLO Fast/Slow Burn, Backlog ETD, Message Age SLA).

3. Terraform Generation & Review

  • Provide the complete HCL configuration in your response with explicitly parameterized defaults and the mandatory user_labels block (created-with-google-skill = "cloud-run-alert-configuration").
  • State the applied defaults and remind the user of their ability to override any constant.
  • Provide a clear plain-English breakdown of the PromQL logic and triggering thresholds.

Additional Resources

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Cloud Run Alert Configuration AI skill do?

Configures best-practice, high-signal alerting policies for Google Cloud Run resources (services, jobs, and worker pools) based on seasoned SRE practices. Use when analyzing, recommending, writing, or deploying Terraform PromQL alerting policies to monitor Cloud Run error rates (4xx/5xx), request latency, container instance saturation (warning/critical), container CPU/memory utilization and allocation, billable instance time, job execution status, and worker pool queue backlog. Don't use for GKE workloads (use gke-alert-configuration) or Compute Engine VMs.

Why use Cloud Run Alert Configuration on TypingMind?

Because you install it once and use it with any model. Cloud Run Alert Configuration is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Cloud Run Alert Configuration in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google/skills/tree/main/skills/cloud/cloud-run-alert-configuration. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Cloud Run Alert Configuration?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Cloud Run Alert Configuration?

As many as you like. As long as a model supports skills, you can use Cloud Run Alert Configuration with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Cloud Run Alert Configuration AI skill free?

Yes. It is published on GitHub by google under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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