SkillAgentSearch skills...

google-cloud-networking-observability

Investigates Google Cloud networking issues by analyzing logs, metrics, and diagnostics

Install / Use

npx skills add google/skills --skill google-cloud-networking-observability

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

87/100

Supported Platforms

Universal

Tags

Our assessment of google-cloud-networking-observability

google-cloud-networking-observability scores 87/100 on our quality scale, 593rd of 1,995 Development & Engineering skills we index (top 30%).

Its SKILL.md is 7.5 KB long, well organised into 9 sections and no code examples: a thorough specification that gives an agent plenty to work with.

With 20,340 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
13/20
Description
12/15
Adoption
18/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 2 days ago, so google-cloud-networking-observability is actively maintained.
  • It is released under the Apache-2.0 license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

google-cloud-networking-observability compared with similar skills

All 4 of these similar skills score higher than google-cloud-networking-observability; compare them before choosing.

SkillScoreStarsUpdatedFormat
google-cloud-networking-observability (this skill)by google8720.3k2d agoSKILL.md
ai-job-searchby MadsLorentzen10044.0k5d agoCLAUDE.md
claude-howtoby luongnv8910041.7ktodayCLAUDE.md
algorithmic-artby anthropics100177.9k3d agoSKILL.md
pptxby anthropics100177.9k3d agoSKILL.md

Frequently asked questions

How do I install google-cloud-networking-observability?
Run npx skills add google/skills --skill google-cloud-networking-observability. The install tabs above show the steps for each supported agent.
Which AI agents does google-cloud-networking-observability work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is google-cloud-networking-observability safe to use?
It is Apache-2.0-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is google-cloud-networking-observability still maintained?
The repository was last updated 2 days ago, so google-cloud-networking-observability is actively maintained.

name: google-cloud-networking-observability metadata: version: "1.0.0" 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, 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.

Related Skills

View on GitHub
GitHub Stars20.3k
CategoryDevelopment
Updated2d ago
Forks1.7k

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions