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.
npx skills add h4vzz/awesome-ai-agent-skills --skill cloud-monitoringgit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-skillsWrote 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.
[](https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/cloud-monitoring)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/cloud-monitoring"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/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.
<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/cloud-monitoring"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/cloud-monitoring.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00030 | $0.02462 |
| Opus 5.5 | $0.00012 | $0.00985 |
| Sonnet 5.5 | $0.00006 | $0.00492 |
| Haiku 4.5 | $0.00003 | $0.00246 |
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 1mo ago.
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.
This is a copy
94% identical to cloud-monitoring — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cloud Monitoring
This skill enables the agent to design and configure comprehensive monitoring and observability solutions for cloud infrastructure and applications. The agent understands the three pillars of observability — metrics, logs, and traces — and can set up dashboards, alerting rules, SLIs, SLOs, and SLAs using tools like Prometheus, Grafana, CloudWatch, Datadog, and OpenTelemetry. The agent also applies alerting best practices to minimize alert fatigue while ensuring critical issues are surfaced promptly.
Workflow
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Identify Monitoring Objectives: The agent works with the user to define what needs to be monitored and why. This includes identifying critical services, establishing Service Level Indicators (SLIs) such as request latency, error rate, and throughput, and setting Service Level Objectives (SLOs) that define acceptable performance thresholds. SLAs (Service Level Agreements) are documented as contractual commitments to customers.
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Select Monitoring Tools and Instrumentation: Based on the cloud provider and application architecture, the agent recommends an appropriate monitoring stack. This may include Prometheus for metrics collection, Grafana for visualization, Loki or CloudWatch Logs for log aggregation, and Jaeger or AWS X-Ray for distributed tracing. The agent configures OpenTelemetry SDKs in application code to emit standardized telemetry data.
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Configure Metrics Collection and Dashboards: The agent defines and deploys metric scrapers, exporters, and custom metrics. It builds dashboards that visualize the golden signals (latency, traffic, errors, saturation) and infrastructure metrics (CPU, memory, disk, network). Dashboards are organized by service tier so teams can quickly triage issues.
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Establish Alerting Rules: The agent configures alerts that trigger on meaningful conditions — such as error budget burn rate exceeding thresholds, sustained latency spikes, or pod restarts — rather than raw metric thresholds alone. Multi-window, multi-burn-rate alerting is used to balance detection speed with false-positive suppression. Alert routing is configured to send critical alerts to PagerDuty or Opsgenie and warnings to Slack.
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.
- 1mo ago First seen · 205 lines · 30 tokens per session scan A 385001234fe8
cloud-monitoring is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (36 stars, last pushed 22d ago), licensed MIT. It adds 30 tokens to every session and 2,462 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 94% identical to cloud-monitoring, differing in 2 lines, and is treated as a copy.
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