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Google Cloud Networking Observability

GoogleOfficialPublished by Google

  • Skill
  • Tool integration
  • Cloud and DevOps
  • Apache-2.0
  • No issues found

From the author

Investigates Google Cloud networking issues by analyzing GCP logs, metrics, and diagnostics. Use when investigating dropped network traffic, packet drops, drop reasons, VPC Flow Logs (including Private Service Connect / PSC, serverless / App Engine Direct VPC, and 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.
— the description by Google

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill provides a structured approach for investigating Google Cloud networking issues. It leverages VPC Flow Logs, firewall logs, threat logs, and Cloud NAT logs to diagnose packet drops, latency, and connectivity failures. The agent follows a strict 'results-first' directive, prioritizing efficient data retrieval through BigQuery and Cloud Logging to provide conclusive answers without unnecessary exploration.

What it does

  • Analyzes VPC Flow Logs for traffic patterns, volume trends, and dropped packets.
  • Investigates firewall and threat logs to identify blocked connections or malicious traffic.
  • Troubleshoots Cloud NAT port exhaustion and gateway performance.
  • Performs path diagnostics using Connectivity Tests.
  • Queries networking metrics for latency, throughput, and packet loss trends.
  • Provides cost estimation for VPC Flow Logs.

When to use it

  • When investigating unexplained network traffic drops or packet loss.
  • To audit firewall rule effectiveness or identify blocked connection attempts.
  • When troubleshooting port exhaustion issues on Cloud NAT gateways.
  • To analyze network performance metrics like latency and throughput.
  • When diagnosing routing or firewall misconfigurations between network endpoints.

Advantages

  • Enforces a strict, efficient workflow that prevents redundant queries.
  • Provides standardized procedures for log discovery and schema verification.
  • Prioritizes high-volume analysis using BigQuery for faster insights.
  • Includes specific guidance for cost estimation and path diagnostics.
  • Ensures consistent attribution for all executed commands.

Usage examples

“Why is my VM unable to reach the internet? Check for NAT port exhaustion.”
The agent queries Cloud NAT logs for 'DROPPED' allocation status and reports if the specific VM is experiencing port exhaustion.
“Show me the top talkers in my VPC over the last 6 hours.”
The agent executes a BigQuery aggregation on VPC Flow Logs to identify and list the top source IPs by traffic volume.
“Are there any firewall rules blocking traffic to my web server?”
The agent inspects firewall logs for 'DENY' events matching the server's IP and reports the blocking rule.
“Estimate the cost of enabling VPC Flow Logs for my project.”
The agent uses the dedicated cost estimation reference to calculate and provide an expected cost based on current traffic patterns.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/google/google-cloud-networking-observability/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip google-cloud-networking-observability.zip -d .agents/skills

Documentation by Google

Google Cloud Networking Observability Expert

Attribution

For all gcloud CLI, REST API, and MCP attribution requirements when executing commands with this skill, follow the instructions in Usage Attribution.

🛑 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, top talkers, dropped traffic, and packet loss.

  • 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.

  • Schema & Sample Discovery: Before writing complex aggregations or filter queries in BigQuery or Cloud Logging, first inspect a single sample log record (LIMIT 1 with --format=json in bq query, or --limit=1 in gcloud logging read) to verify the exact payload schema and field paths.

  • 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.

Files

references/mcp-usage.md

MCP Server Usage Reference

This document describes the Model Context Protocol (MCP) servers used for GCP networking observability.

BigQuery MCP

BigQuery is supported by a remote MCP server that provides tools for automated data management and analysis.

Key Tools
  • list_dataset_ids: List BigQuery dataset IDs in a project.

  • list_table_ids: List table IDs in a BigQuery dataset.

  • get_table_info: Get schema and metadata for a specific table.

  • execute_sql_readonly: Run SELECT queries to analyze logs (such as, VPC Flow, Firewall) stored in BigQuery. This is the preferred tool for high-volume aggregations and trend analysis.

Usage Pattern
  1. Use list_dataset_ids to find the logging dataset (for example, _AllLogs).

  2. Use list_table_ids to find the relevant log table.

  3. Use get_table_info to verify field names (for example, jsonPayload versus json_payload).

  4. Use execute_sql_readonly for the final analysis.

Cloud Logging MCP

The Cloud Logging MCP server provides access to log entries across various Google Cloud resources.

Key Tools
  • list_log_entries: Search and retrieve log entries using advanced filters.

  • list_log_names: Discover available logs in a project.

Usage Pattern
  • Use for quick, real-time identification of recent events or exploratory analysis where BigQuery datasets are not linked.

  • Use specific filters for resource.type and logName to narrow down results.

NetworkManagement MCP

The Network Management MCP server allows for reachability analysis and path diagnostics.

Key Tools
  • create_connectivity_test: Start a simulated packet path analysis between two endpoints.

  • get_connectivity_test: Poll for the results of a running test.

  • delete_connectivity_test: Cleanup the test resource after analysis is complete.

Usage Pattern
  • Use when static path analysis is needed to identify firewall or routing blocks.

  • CRITICAL: Always delete the test resource after retrieving the result.

Cloud Monitoring MCP

The GcpMon MCP server provides access to Cloud Monitoring metrics and time-series data.

Key Tools
  • list_metric_descriptors: Discover available metrics using filters.

  • list_timeseries: Retrieve aggregated data points for performance analysis (such as RTT or throughput).

Usage Pattern
  • Use for analyzing performance trends, packet loss, and latency.

  • Prefer ALIGN_MEAN or ALIGN_PERCENTILE_50 for distribution metrics like RTT to simplify parsing.

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs Python

Security

No issues found by our static scan· No issues found

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Attribution and licence

Published by
Google
Publisher
Google (Official)
Source
google/skills · skills/cloud/google-cloud-networking-observability · commit 2194d3b
Licence
Apache License 2.0 (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download google-cloud-networking-observability.zip