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 kube-agents-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/kube-agents-observability)<a href="https://agentmods.dev/skills/gke-labs/kube-agents/kube-agents-observability"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/kube-agents-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/kube-agents-observability"><img src="https://agentmods.dev/badge/skills/gke-labs/kube-agents/kube-agents-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 | $0.00031 | $0.02225 |
| Opus 5 | $0.00015 | $0.01112 |
| Sonnet 5 | $0.00006 | $0.00445 |
| Haiku 4.5 | $0.00003 | $0.00222 |
Grade A, and why
kube-agents-observability scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
sh -c 'curl -i -s -o /dev/null -w "%{http_code}\n" -X POST "$OTEL_EXPORTER_OTLP_ENDPOINT/v1/traces"' How it starts
The opening of the file, as written. The whole thing — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Task
Audit, verify, and troubleshoot the logging, metrics, and distributed tracing observability of the Platform Agent.
[!TIP] The provided Python scripts in the
scripts/subdirectory are parameterized reference implementations. When troubleshooting, you can run them directly, customize their parameters, or write custom just-in-time scripts/commands to query more specific metrics, endpoints, or time ranges as required by the task context.
Workflow
Logging
1. Audit Agent Main Logs
- Verify that the main agent container is writing logs to
/opt/data/logs/*.log. - View the internal agent log files directly:
kubectl exec <pod-name> -c <agent-container-name> -n kubeagents-system -- tail -n 100 /opt/data/logs/agent.log
2. Inspect Sidecar Log Aggregator (Fluent-bit)
- Verify the
fluent-bitsidecar container tails the log directory and streams to standard output:kubectl logs <pod-name> -c fluent-bit -n kubeagents-system --tail=100 - Retrieve and verify the configuration of the Fluent-bit sidecar:
kubectl get configmap <agent-name>-fluent-bit-config -n kubeagents-system -o yaml - Ensure the shared
/opt/datavolume is mounted to both the agent and Fluent-bit containers:kubectl get pod <pod-name> -n kubeagents-system -o jsonpath='{.spec.containers[*].volumeMounts}'
3. Identify Active Chat Users (Auditing Interactions)
To determine which users have interacted with the system via Google Chat in the last 24 hours (or a custom window):
-
Run the packaged Python helper script to automatically query and parse the GKE container logs from Google Cloud Logging:
python3 ./scripts/get_chat_users.py --project-id <PROJECT_ID> [--hours <HOURS>] -
Alternatively, search Cloud Logging manually (via console or gcloud CLI) for the custom GChat event format emitted by the hermes session store:
gcloud logging read 'resource.type="k8s_container" "Logging incoming GChat event"' --project=<PROJECT_ID> --limit=1000 --format="json"Look for log lines containing the format:
Logging incoming GChat event: User=<email>, Session=<session_id>.
What ships with it
5 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.
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.
- yesterday Changed 0cede5ad755b
- 9d ago First seen · 191 lines · 31 tokens per session scan A f3c65fd22c17
kube-agents-observability is a skill published in the GitHub repository gke-labs/kube-agents (54 stars, last pushed today), licensed Apache-2.0. It adds 31 tokens to every session and 2,225 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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