>- 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.
npx skills add https://github.com/google/skills --skill google-cloud-networking-observability
firewall logs, threat logs, Cloud NAT, VPC Flow logs, or metrics.
answer.
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.
that identify malicious traffic patterns (for example, SQL injection or
malware) using deep packet inspection.
Use for traffic analysis, volume trends, and top talkers.
to identify "DENY" events or verify "ALLOW" rules.
through NAT gateways or troubleshoot port exhaustion.
(latency), and packet loss. Use for historical trends and performance
monitoring.
identify firewall or routing misconfigurations between endpoints.
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.
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).
Cloud Monitoring MCP,
BigQuery MCP, or
Cloud Logging MCP.
gateway, VPN tunnel) is not found in metrics/logs:
run_shell_command with gcloud to list resources in the project.for the resource name to find correct labels.
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.
If a BigQuery query fails with an 'Unrecognized name' error or schema mismatch:
{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.
For detailed SQL patterns, field definitions, and advanced troubleshooting, read
the corresponding reference file:
references/threat-analysis.md
references/vpc-flow-analysis.md
references/vpc-flow-logs-cost-estimation.md
references/cloud-nat-analysis.md
references/firewall-analysis.md
references/metrics-analysis.md
references/connectivity-tests.md
> 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.
after finding a firewall block) without explicit user permission.
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.
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.
the first turn to save steps.
"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.
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.
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.
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.
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take google/google-cloud-networking-observability from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.