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Google Cloud Networking Observability

Free

Streamline your Google Cloud networking diagnostics.

by google17.6k stars on google/skills
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Updated Aug 10, 2026
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What Google Cloud Networking Observability does

The Google Cloud Networking Observability skill is designed to assist developers and network engineers in diagnosing and analyzing networking issues within Google Cloud environments. By leveraging various logs, metrics, and diagnostics, this skill focuses on specific areas such as VPC Flow Logs, NAT configurations, firewall rules, and threat logs. It provides a structured approach to quickly identify and retrieve relevant data, ensuring users can address networking concerns efficiently.

This skill operates on a core directive that prioritizes results, guiding users through the process of identifying the primary source of their queriesβ€”whether they need firewall logs, NAT information, or connectivity metrics. Once the required data is determined, the skill executes the necessary queries and presents the findings directly, allowing for swift troubleshooting. This approach minimizes the time spent on exploratory queries and emphasizes delivering actionable insights.

The skill includes detailed procedures for log source preference, tool selection, and schema verification, ensuring users can navigate the complexities of Google Cloud's networking services effectively. It also provides specific reference files for deeper analysis, covering areas such as threat log analysis and VPC flow analysis, which are crucial for maintaining network security and performance.

Ideal for network administrators and cloud engineers, this skill is particularly useful in scenarios where quick diagnostics are essential, such as during incident response or performance monitoring. It streamlines the process of gathering and interpreting networking data, making it a valuable tool for anyone managing Google Cloud infrastructure.

When to use it

Use this skill when you need to analyze networking logs or metrics in Google Cloud, particularly for VPC Flow Logs, NAT, or firewall diagnostics.

When not to use it

This skill is not suitable for general VM management or tasks unrelated to networking observability.

What you can build with it

Analyzing VPC Flow Logs

Use this skill to extract insights from VPC Flow Logs, helping to identify traffic patterns and anomalies.

Firewall Rule Verification

Quickly verify firewall rules and connection attempts to troubleshoot access issues in your Google Cloud environment.

Cost Estimation for VPC Flow Logs

Utilize the skill to estimate costs associated with VPC Flow Logs, ensuring efficient budget management.

How to install Google Cloud Networking Observability

View source

1. Install with the skills CLI

npx skills add google/skills/google-cloud-networking-observability --agent claude-code

2. Or install it manually

Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.

Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs

Inside SKILL.md

Written by google

Google Cloud Networking Observability Expert

πŸ›‘ 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, 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:
    1. Use run_shell_command with gcloud to list resources in the project.
    2. 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:

  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. 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:

CRITICAL: If the user asks for Cost Estimation, you MUST strictly use references/vpc-flow-logs-cost-estimation.md. Do NOT read or use references/vpc-flow-analysis.md for cost estimation tasks.

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.

Frequently asked questions about Google Cloud Networking Observability

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