| description | Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics. Use when investigating VPC Flow Logs, NAT, firewall, or threat logs, querying latency and throughput metrics, or running Connectivity Tests for path diagnostics. |
| metadata | {"github-path":"skills/cloud/google-cloud-networking-observability","github-ref":"refs/heads/main","github-repo":"https://github.com/google/skills","github-tree-sha":"77e0187913d65aa07259745b4a9aa8cc36ef7955"} |
| name | google-cloud-networking-observability |
Google Cloud Networking Observability Expert
🛑 Core Directive: Results First
- Identify the Primary Source: Quickly determine if the user needs
firewall logs, threat logs, Cloud NAT, VPC Flow logs, or metrics.
- Execute & Present: Perform the minimum required query to get a direct
answer.
- 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,
BigQuery MCP, or
Cloud Logging MCP.
- Resource Discovery: If a user-specified resource (for example, NAT
gateway, VPN tunnel) is not found in metrics/logs:
- Use
run_shell_command with gcloud to list resources in the project.
- Search 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.
2. Schema Verification & Error Recovery
If a BigQuery query fails with an 'Unrecognized name' error or schema mismatch:
- 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. Retry: Apply identified fixes to the original query and execute.
3. Analysis Guides (Read Only When Needed)
For detailed SQL patterns, field definitions, and advanced troubleshooting, read
the corresponding reference file:
Boundaries (CRITICAL)
- ALWAYS present the direct answer as soon as it is identified.
- NEVER run more than 2 exploratory queries before showing results.
- NEVER perform secondary verification (for example, don't check VPC flows
after finding a firewall block) without explicit user permission.
- ALWAYS print the generated SQL for review before execution.
- ALWAYS include a link to the Flow Analyzer in the
Google Cloud Console.
- NEVER query a second data source (such as, BigQuery logs) if the primary
source (for example, Cloud Monitoring metrics) has already provided a
conclusive answer. DO NOT compare metrics and logs to "verify" accuracy
unless the user specifically asks why they differ.
- NO DISCREPANCY LOOPS: If Tool A provides a result (such as, 80,000
counts) and Tool B provides a different result (for example, 1,000 counts),
DO NOT initiate a deep dive to explain the difference. Present the
result from the primary tool and STOP.
- ALWAYS perform time-range calculations (such as, "12 hours ago") during
the first turn to save steps.
- Conclusive Acceptance of Inactivity: Treat a result of "0", "0 traffic",
"No data found", or "No records found" as a conclusive finding for the
requested timeframe and resource. You MUST report this as the definitive
state and terminate immediately.
- Standardized Discovery Path: For all "Top-N" or volume-based discovery
tasks (for example, "highest traffic," "most hits," "top talkers"), you MUST
use BigQuery aggregation on _AllLogs datasets. Manual aggregation of
individual time-series points using the Monitoring API is forbidden due to
step inefficiency.
- Ban on Auxiliary Scripting: Execute all data retrieval and parsing logic
as direct tool calls (bq, curl, gcloud). Do NOT write or execute local shell
scripts (.sh) or python files, as these introduce avoidable environment and
permission errors that lead to investigation timeouts.
- Discovery Efficiency: For volume analysis (for example, "how many
connections" or "top IPs by bytes"), BigQuery aggregation on VPC Flow logs
(_AllLogs) is the Primary Source of Truth. If BigQuery data is
available, it is conclusive. Do NOT query Monitoring API to "double check"
BigQuery counts.
