Gcp Cloud Monitoring

Community

automateyournetwork

gcp-cloud-monitoring

Google Cloud Monitoring — time series metrics, alert policies, active alerts, metric discovery. Use when checking GCP network performance, investigating firing alerts, querying VM CPU or memory metrics, reviewing Cloud VPN tunnel status, or assessing load balancer latency.

Overview

Publisher	automateyournetwork
Repository	`netclaw`
Skill name	`gcp-cloud-monitoring`
Stars	680
Forks	189
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository

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.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by automateyournetwork on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/gcp-cloud-monitoring
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/gcp-cloud-monitoring

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/automateyournetwork/netclaw.git /tmp/netclaw
mkdir -p .claude/skills
cp -r /tmp/netclaw/workspace/skills/gcp-cloud-monitoring .claude/skills/gcp-cloud-monitoring

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Gcp Cloud Monitoring 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 Gcp Cloud Monitoring 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 Gcp Cloud Monitoring 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.

GCP Cloud Monitoring

MCP Server

Endpoint: https://monitoring.googleapis.com/mcp (Streamable HTTP)
Auth: OAuth 2.0 via Google IAM — service account key (GOOGLE_APPLICATION_CREDENTIALS) or gcloud auth application-default login
Requires: GCP_PROJECT_ID environment variable

Available Tools (6)

Tool	What It Does
`list_timeseries`	Query time series data — CPU, memory, network, disk metrics for any GCP resource
`list_metric_descriptors`	Discover available metric types in a project — find what you can monitor
`list_alert_policies`	List all alerting policies — conditions, notification channels, thresholds
`get_alert_policy`	Get details of a specific alerting policy
`list_alerts`	List current and past alert violations — what's firing right now
`get_alert`	Get details of a specific alert violation

Workflow: GCP Network Monitoring

When a user asks "how is our GCP network performing?":

Check alerts: list_alerts to find any active alert violations
VM network metrics: list_timeseries for compute.googleapis.com/instance/network/received_bytes_count and sent_bytes_count
Packet drops: list_timeseries for compute.googleapis.com/instance/network/received_packets_dropped_count
Firewall metrics: list_timeseries for compute.googleapis.com/firewall/dropped_packets_count
Load balancer metrics: list_timeseries for loadbalancing.googleapis.com/https/request_count and total_latencies
Report: Network health dashboard with any issues flagged

Workflow: Alert Investigation

When investigating GCP alerts:

List active alerts: list_alerts — find what's currently firing
Get alert details: get_alert — condition, threshold, resource affected
Get policy: get_alert_policy — what triggers this alert, notification channels
Pull metrics: list_timeseries for the affected metric — see the spike/anomaly
Cross-reference: Use gcp-cloud-logging for correlated log entries
Report: Alert investigation with root cause and timeline

Workflow: Resource Health Check

When checking GCP infrastructure health:

Discover metrics: list_metric_descriptors filtered by service (compute, networking, loadbalancing)
VM CPU/Memory: list_timeseries for compute.googleapis.com/instance/cpu/utilization and memory metrics
Disk I/O: list_timeseries for compute.googleapis.com/instance/disk/read_bytes_count and write metrics
Network throughput: list_timeseries for network sent/received bytes
Alert status: list_alert_policies + list_alerts — any policies in violation?
Report: Infrastructure health dashboard with severity ratings

Common GCP Network Metrics

Metric	What It Tells You
`compute.googleapis.com/instance/network/received_bytes_count`	Inbound network throughput per VM
`compute.googleapis.com/instance/network/sent_bytes_count`	Outbound network throughput per VM
`compute.googleapis.com/instance/network/received_packets_dropped_count`	Dropped inbound packets (congestion)
`compute.googleapis.com/instance/network/sent_packets_dropped_count`	Dropped outbound packets (congestion)
`compute.googleapis.com/firewall/dropped_packets_count`	Packets dropped by VPC firewall rules
`loadbalancing.googleapis.com/https/request_count`	HTTP(S) LB request rate
`loadbalancing.googleapis.com/https/total_latencies`	HTTP(S) LB end-to-end latency
`loadbalancing.googleapis.com/https/backend_latencies`	Backend response time behind LB
`vpn.googleapis.com/tunnel_established`	Cloud VPN tunnel state (1=up, 0=down)
`vpn.googleapis.com/sent_bytes_count`	Bytes sent through VPN tunnel
`router.googleapis.com/bgp/received_routes_count`	BGP routes received by Cloud Router
`interconnect.googleapis.com/link/received_bytes_count`	Cloud Interconnect link throughput

Important Rules

Remote MCP server — hosted by Google, no local install needed
OAuth 2.0 authentication — uses IAM for access control
Project-scoped — metrics are scoped to the configured GCP project
Read-only — monitoring queries don't modify anything
Record in GAIT — log monitoring investigations for audit trail

Environment Variables

GCP_PROJECT_ID — Google Cloud project ID
GOOGLE_APPLICATION_CREDENTIALS — Path to service account key JSON file

Failure Behavior

On a tool error (timeout, unreachable host, malformed response), report the failure and its error message directly to the user rather than fabricating or guessing at results.
For a confirmed read-only call, check connectivity and retry once if appropriate. For any call that changes state or sends a message, a timeout does not prove the action failed: inspect current state or delivery status before retrying, preserve the required approval/change gates, and do not repeat an action whose outcome is unknown.

Frequently asked questions

What does the Gcp Cloud Monitoring AI skill do?

Why use Gcp Cloud Monitoring on TypingMind?

Because you install it once and use it with any model. Gcp Cloud Monitoring 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 Gcp Cloud Monitoring in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/automateyournetwork/netclaw/tree/main/workspace/skills/gcp-cloud-monitoring. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Gcp Cloud Monitoring?

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 Gcp Cloud Monitoring?

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

Is the Gcp Cloud Monitoring AI skill free?

Yes. It is published on GitHub by automateyournetwork 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

GCP Cloud Monitoring

MCP Server

Available Tools (6)

Workflow: GCP Network Monitoring

Workflow: Alert Investigation

Workflow: Resource Health Check

Common GCP Network Metrics

Important Rules

Environment Variables

Failure Behavior

Frequently asked questions

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