Cloud Run deployment, BigQuery optimization, Pub/Sub patterns, IAM best practices
npx skills add https://github.com/vibeeval/vibecosystem --skill gcp-patterns
FROM node:20-slim AS builder
WORKDIR /app
COPY package*.json ./
RUN npm ci --production=false
COPY . .
RUN npm run build
FROM node:20-slim
WORKDIR /app
RUN addgroup --system app && adduser --system --ingroup app app
COPY --from=builder /app/dist ./dist
COPY --from=builder /app/node_modules ./node_modules
COPY --from=builder /app/package.json ./
USER app
EXPOSE 8080
ENV PORT=8080 NODE_ENV=production
CMD ["node", "dist/server.js"]
apiVersion: serving.knative.dev/v1
kind: Service
metadata:
name: order-service
annotations:
run.googleapis.com/launch-stage: GA
spec:
template:
metadata:
annotations:
autoscaling.knative.dev/minScale: "1"
autoscaling.knative.dev/maxScale: "100"
run.googleapis.com/cpu-throttling: "false"
run.googleapis.com/startup-cpu-boost: "true"
spec:
containerConcurrency: 80
timeoutSeconds: 300
serviceAccountName: [email protected]
containers:
- image: gcr.io/project-id/order-service:latest
ports:
- containerPort: 8080
resources:
limits:
cpu: "2"
memory: 1Gi
env:
- name: DB_CONNECTION
valueFrom:
secretKeyRef:
key: latest
name: db-connection-string
startupProbe:
httpGet:
path: /healthz
port: 8080
initialDelaySeconds: 5
periodSeconds: 3
gcloud run deploy order-service \
--image gcr.io/$PROJECT_ID/order-service:$GIT_SHA \
--region us-central1 \
--service-account order-service@$PROJECT_ID.iam.gserviceaccount.com \
--set-secrets "DB_URL=db-connection:latest" \
--min-instances 1 \
--max-instances 100 \
--cpu 2 --memory 1Gi \
--concurrency 80 \
--no-allow-unauthenticated
-- Use partitioning and clustering
CREATE TABLE `project.dataset.events`
PARTITION BY DATE(event_timestamp)
CLUSTER BY user_id, event_type
AS SELECT * FROM `project.dataset.raw_events`;
-- Always filter on partition column
SELECT event_type, COUNT(*) as cnt
FROM `project.dataset.events`
WHERE event_timestamp BETWEEN '2025-01-01' AND '2025-01-31'
AND event_type = 'purchase'
GROUP BY event_type;
-- Use approximate functions for large datasets
SELECT APPROX_COUNT_DISTINCT(user_id) as unique_users
FROM `project.dataset.events`
WHERE DATE(event_timestamp) = CURRENT_DATE();
-- Avoid SELECT * (scans all columns, costs more)
-- Use column selection and LIMIT for exploration
from google.cloud import pubsub_v1
from google.api_core import retry
import json
# Publisher with ordering and retry
publisher = pubsub_v1.PublisherClient()
topic_path = publisher.topic_path("project-id", "order-events")
def publish_event(event: dict, ordering_key: str = "") -> str:
data = json.dumps(event).encode("utf-8")
future = publisher.publish(
topic_path,
data,
ordering_key=ordering_key,
event_type=event["type"],
)
return future.result(timeout=30)
# Subscriber with exactly-once processing
subscriber = pubsub_v1.SubscriberClient()
subscription_path = subscriber.subscription_path("project-id", "order-events-sub")
def callback(message: pubsub_v1.types.PubsubMessage) -> None:
try:
event = json.loads(message.data.decode("utf-8"))
idempotency_key = message.message_id
if already_processed(idempotency_key):
message.ack()
return
process_event(event)
mark_processed(idempotency_key)
message.ack()
except Exception as e:
logger.error(f"Failed to process message: {e}")
message.nack()
subscriber.subscribe(subscription_path, callback=callback)
Principles:
- Least privilege: grant minimum permissions needed
- Service accounts per service (not shared)
- No user accounts in production workloads
- Prefer predefined roles over primitive roles
Per-Service Pattern:
order-service:
roles:
- roles/cloudsql.client # DB access
- roles/pubsub.publisher # Publish events
- roles/secretmanager.secretAccessor # Read secrets
# NOT: roles/editor (too broad)
Workload Identity (GKE):
- Bind K8s SA to GCP SA
- No key files, automatic credential rotation
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 vibeeval/gcp-patterns 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.