google-cloud-networking-observability
>- Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs (including cost estimation), NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics. Don't use for generic VM management or non-observability tasks.
Why we recommend it: Investigates Google Cloud networking issues via VPC Flow Logs, NAT, firewall, and threat logs, latency/throughput metrics, and Connectivity Tests for path diagnostics.
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SKILL.md
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---
name: google-cloud-networking-observability
metadata:
category: Compute
description: >-
Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs (including cost estimation), NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics. Don't use for generic VM management or non-observability tasks.
---
# Google Cloud Networking Observability Expert
## 🛑 Core Directive: Results First
1. **Identify the Primary Source**: Quickly determine if the user needs
firewall logs, threat logs, Cloud NAT, VPC Flow logs, or metrics.
2. **Execute & Present**: Perform the minimum required query to get a direct
answer.
3. **Definitive Termination**: Once you identify the requested data, regardless
of the value (including 0, null, or "No traffic"), present the finding and
call the finish tool in the same turn. Do NOT attempt to find "active" or
"busier" resources to provide a "better" answer unless specifically
instructed to troubleshoot a resource that is expected to be busy.
## Log & Telemetry Overview
- **Threat Logs**: Specialized logs from Cloud Firewall Plus and Cloud IDS
that identify malicious traffic patterns (for example, SQL injection or
malware) using deep packet inspection.
- **VPC Flow Logs**: Capture sample IP traffic to and from network interfaces.
Use for traffic analysis, volume trends, and top talkers.
- **Firewall Logs**: Record connection attempts matched by firewall rules. Use
to identify "DENY" events or verify "ALLOW" rules.
- **Cloud NAT Logs**: Audit NAT translations. Use to audit traffic going
through NAT gateways or troubleshoot port exhaustion.
- **Networking Metrics**: Aggregated time-series data for throughput, RTT
(latency), and packet loss. Use for historical trends and performance
monitoring.
- **Connectivity Tests**: Static analysis tool for path diagnostics. Use to
identify firewall or routing misconfigurations between endpoints.
## Procedures
### 0. Log Source Preference
- **ALWAYS** check for BigQuery linked datasets (for example,
`big_query_linked_dataset`, `_AllLogs`) before using Cloud Logging for
high-volume analysis or aggregations. This is the preferred method for
finding trends or top-blocking rules.
- **Metadata Awareness (BigQuery)**: Subnetworks may be configured with
`EXCLUDE_ALL_METADATA`, causing VM names to be NULL in VPC Flow Logs. If a
query by VM name returns nothing, retry using the internal IP address
(`jsonPayload.connection.src_ip`).
### 1. Tool Selection & Discovery
- **MCP Servers First**: Use
[Cloud Monitoring MCP](references/mcp-usage.md#cloud-monitoring-mcp),
[BigQuery MCP](references/mcp-usage.md#bigquery-mcp), or
[Cloud Logging MCP](references/mcp-usage.md#cloud-logging-mcp).
- **Resource Discovery**: If a user-specified resource (for example, NAT
gateway, VPN tunnel) is not found in metrics/logs:
1. Use `run_shell_command` with `gcloud` to list resources in the project.
2. Search [Cloud Logging MCP](references/mcp-usage.md#cloud-logging-mcp)
for the resource name to find correct labels.
- **CLI Fallback**: Use `gcloud` or `bq` only if MCP servers are unavailable.
DO NOT use gcloud monitoring; it is restricted. Immediately use the curl
templates in [metrics-analysis.md](references/metrics-analysis.md).
### 2. Schema Verification & Error Recovery
If a BigQuery query fails with an 'Unrecognized name' error or schema mismatch:
1. **Validate Schema**: Run `bq show --schema --format=json
{project_id}:{dataset_id}.{table_id}` to verify field names and casing (for
example, `jsonPayload` versus `json_payload`). 2. **Dry Run**: Before executing
a corrected query, use `bq query --use_legacy_sql=false --dry_run
"{query_text}"` to verify field references without incurring cost or execution
time. 3.Read the full source on GitHub (opens external page)