What is cloud-monitoring?

A guide for analyzing Google Cloud Monitoring data and reducing large metric responses into short summaries. Google Cloud Monitoring collects measurements such as request counts, errors, and latency.

Use it to query metric ranges, export time-series data to CSV, and review averages, minimums, maximums, and notable patterns.

It prevents large API results from overwhelming the coding agent and surfaces useful statistics and simple text graphs.

cloud-monitoring — Skill for Claude Code by gemini-cli-extensions

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add gemini-cli-extensions/sre --skill cloud-monitoring

Clone the repo

git clone --depth 1 https://github.com/gemini-cli-extensions/sre

Made for: Claude Code.

Or install sre-extension, the plugin that ships this one along with the rest of its 16 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/gemini-cli-extensions/sre/cloud-monitoring/github.svg)](https://agentmods.dev/skills/gemini-cli-extensions/sre/cloud-monitoring)

Your own site

<a href="https://agentmods.dev/skills/gemini-cli-extensions/sre/cloud-monitoring"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/sre/cloud-monitoring/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/gemini-cli-extensions/sre/cloud-monitoring"><img src="https://agentmods.dev/badge/skills/gemini-cli-extensions/sre/cloud-monitoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 37 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 858 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00037	$0.00858
Opus 5	$0.00018	$0.00429
Sonnet 5	$0.00007	$0.00172
Haiku 4.5	$0.00004	$0.00086

Measured 11d ago against content hash 895812a6f643, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

cloud-monitoring scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/export_timeseries_to_csv.py, scripts/setup-frontend-slo.sh), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/cloud-monitoring/SKILL.md · 43 lines

How it starts

The opening of the file, as written. The whole thing — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Cloud Monitoring

⚠️ PREREQUISITE: google-monitoring MCP Server This skill relies on the google-monitoring MCP server being installed and active. Before proceeding with monitoring tasks, assert that the required MCP tools (e.g., mcp_google-monitoring_query_range) are available. If they are missing, immediately inform the user and recommend they enable the MCP server (they can use the gcp-mcp-setup skill if available).

This skill provides utilities for analyzing and extracting data from Google Cloud Monitoring (CM).

Best Practices

Avoid Context Bloat: Cloud Monitoring API responses can be massive. Do not try to read raw JSON responses directly into the LLM context. Try to delegate to a SubAgent anything which pulls monitoring data as they tend to bloat the context.
Use Export Scripts: If you pull monitoring data, make sure to surface this final data (in CSV format) using the scripts/export_timeseries_to_csv.py Python script to surface important stats like AVG, Max, Min and a simplified text-graph of the system (see references/sample_output_dual_metrics.csv) . Also report back that stats header to the main agent, together with any interesting insights you might have found.
Metadata Headers: Exported data should contain metadata (time ranges, metric names) at the top of the file so that the context of "when" the data was pulled is never lost, as now() changes over time.
Target Workloads: Focus your monitoring extractions primarily on GKE and Cloud Run environments. Generic time-series extractions should still allow filtering by specific resources.
Paired Metrics Comparison: The extraction script supports querying multiple metrics simultaneously. For apples-to-apples comparisons, try these recommended pairs:
- Network I/O: compute.googleapis.com/instance/network/received_bytes_count vs compute.googleapis.com/instance/network/sent_bytes_count
- Disk I/O: compute.googleapis.com/instance/disk/read_bytes_count vs compute.googleapis.com/instance/disk/write_bytes_count
- Cloud Run Traffic: run.googleapis.com/request_count vs run.googleapis.com/response_latencies
- GKE Memory vs CPU: kubernetes.io/container/memory/used_bytes vs kubernetes.io/container/cpu/core_usage_time

Read the full file on GitHub · 43 lines

Files

What ships with it

11 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

11d ago First seen · 43 lines · 37 tokens per session scan A 895812a6f643

Subscribe to this mod's changes

cloud-monitoring is a skill published in the GitHub repository gemini-cli-extensions/sre (83 stars, last pushed 8d ago), licensed Apache-2.0. It adds 37 tokens to every session and 858 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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