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 gke-labs/kube-agents --skill gke-observabilitygit clone --depth 1 https://github.com/gke-labs/kube-agentsWrote 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/gke-labs/kube-agents/gke-observability)<a href="https://agentmods.dev/skills/gke-labs/kube-agents/gke-observability"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-observability/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/gke-labs/kube-agents/gke-observability"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/gke-observability.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00026 | $0.00834 |
| Opus 5 | $0.00013 | $0.00417 |
| Sonnet 5 | $0.00005 | $0.00167 |
| Haiku 4.5 | $0.00003 | $0.00083 |
Grade A, and why
gke-observability 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.
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.
How it starts
The opening of the file, as written. The whole thing — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GKE Observability Skill
This skill provides workflows for ensuring your GKE cluster and workloads have adequate observability for production use.
Workflows
1. Audit Cluster Observability
Check if Cloud Logging and Cloud Monitoring are enabled on the cluster.
Command:
gcloud container clusters describe <cluster-name> --region <region> --project <project-id> --format="json(loggingConfig, monitoringConfig)"
Look for loggingService and monitoringService to be set to something other than none (usually logging.googleapis.com/kubernetes and monitoring.googleapis.com/kubernetes).
2. Enable Managed Service for Prometheus
Google Cloud Managed Service for Prometheus is the recommended way to collect metrics from your applications.
Command to enable:
gcloud container clusters update <cluster-name> \
--enable-managed-prometheus \
--region <region>
Verify installation:
kubectl get pods -n gmp-system
3. Workload Logging Verification
Ensure your workloads are logging to standard output, which Cloud Logging collects automatically.
Check workload logs:
kubectl logs <pod-name> -n <namespace>
Ensure logs are in a structured format (like JSON) if possible, for easier querying.
4. Dashboards and Alerts
Recommend creating dashboards in Cloud Monitoring for key metrics:
- CPU Utilization
- Memory Utilization
- Request Latency
- Error Rate
Set up alerting policies for critical thresholds.
5. Distributed Tracing
Enable distributed tracing to track requests across microservices.
- Action: Recommend using OpenTelemetry in the application to send traces to Cloud Trace.
- Benefit: Helps identify latency bottlenecks in distributed systems.
6. Continuous Profiling
Use continuous profiling to analyze application performance in production with low overhead.
- Action: Recommend integrating the Cloud Profiler agent in your application code.
- Benefit: Helps identify CPU and memory-consuming functions in production.
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 · 129 lines · 26 tokens per session scan A 0a440f0b1382
gke-observability is a skill published in the GitHub repository gke-labs/kube-agents (53 stars, last pushed today), licensed Apache-2.0. It adds 26 tokens to every session and 834 once invoked, about $0.0001 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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