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gcp

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UpdatedMay 31, 2026 at 22:00

Google Cloud Platform (GCP) best practices. Use when provisioning GCP infrastructure, configuring IAM, deploying to Cloud Run, GKE (Kubernetes Engine), Cloud Functions, Cloud SQL, or managing BigQuery. Do NOT trigger for generic "deploy my app" requests without clarifying the target platform.

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neverinfamous/memory-journal-mcp

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SKILL.md

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name	gcp
description	Google Cloud Platform (GCP) best practices. Use when provisioning GCP infrastructure, configuring IAM, deploying to Cloud Run, GKE (Kubernetes Engine), Cloud Functions, Cloud SQL, or managing BigQuery. Do NOT trigger for generic "deploy my app" requests without clarifying the target platform.

Google Cloud Platform (GCP) Best Practices

STOP. Your knowledge of GCP may be outdated. Prefer retrieval over pre-training for any version-sensitive GCP configuration. Fetch current documentation when in doubt.

Guidelines for architecting and deploying secure, scalable applications on GCP.

1. Identity and Access Management (IAM)

Service Accounts: Do not use the default Compute Engine service account. Create dedicated, narrowly-scoped service accounts for each application (Cloud Run, GKE node pool, Cloud Function).
Workload Identity: Always use Workload Identity (for GKE) or Workload Identity Federation (for GitHub Actions/CI) instead of generating and storing long-lived JSON service account keys.
Least Privilege: Grant roles at the lowest level possible (Resource level > Project level > Folder level).

2. Serverless (Cloud Run & Cloud Functions)

Cloud Run: Prefer Cloud Run over Cloud Functions for containerized applications. It supports multi-concurrency (handling multiple requests per instance), which significantly lowers costs and cold starts.
Scaling: Configure minimum instances (min-instances) to 1 or more for latency-sensitive applications to mitigate cold starts. Ensure max-instances is set to prevent runaway billing.

3. Data and Storage (Cloud SQL, Cloud Storage, BigQuery)

Cloud SQL: Use private IPs for Cloud SQL instances. Connect to them using the Cloud SQL Auth Proxy or Serverless VPC Access connectors rather than exposing them to the public internet.
Cloud Storage: Enforce Uniform Bucket-Level Access to manage permissions solely via IAM, disabling legacy ACLs. Enable object versioning for critical buckets to protect against accidental overwrites/deletes.
BigQuery: Always partition and cluster large BigQuery tables to minimize query costs (BigQuery charges per byte processed).

4. Networking and Security

VPC Service Controls: Use VPC SC to create secure perimeters around sensitive data (e.g., preventing data exfiltration from Cloud Storage or BigQuery).
Cloud Armor: Attach Google Cloud Armor to Global HTTP(S) Load Balancers to defend against DDoS attacks and implement WAF (Web Application Firewall) rules.

5. Infrastructure as Code

Terraform: Use Terraform as the primary IaC tool for GCP. Store the Terraform state in a centralized, version-enabled Cloud Storage bucket with state locking enabled.

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name	gcp
description	Google Cloud Platform (GCP) best practices. Use when provisioning GCP infrastructure, configuring IAM, deploying to Cloud Run, GKE (Kubernetes Engine), Cloud Functions, Cloud SQL, or managing BigQuery. Do NOT trigger for generic "deploy my app" requests without clarifying the target platform.

Google Cloud Platform (GCP) Best Practices

STOP. Your knowledge of GCP may be outdated. Prefer retrieval over pre-training for any version-sensitive GCP configuration. Fetch current documentation when in doubt.

Guidelines for architecting and deploying secure, scalable applications on GCP.

1. Identity and Access Management (IAM)

Service Accounts: Do not use the default Compute Engine service account. Create dedicated, narrowly-scoped service accounts for each application (Cloud Run, GKE node pool, Cloud Function).
Workload Identity: Always use Workload Identity (for GKE) or Workload Identity Federation (for GitHub Actions/CI) instead of generating and storing long-lived JSON service account keys.
Least Privilege: Grant roles at the lowest level possible (Resource level > Project level > Folder level).

2. Serverless (Cloud Run & Cloud Functions)

Cloud Run: Prefer Cloud Run over Cloud Functions for containerized applications. It supports multi-concurrency (handling multiple requests per instance), which significantly lowers costs and cold starts.
Scaling: Configure minimum instances (min-instances) to 1 or more for latency-sensitive applications to mitigate cold starts. Ensure max-instances is set to prevent runaway billing.

3. Data and Storage (Cloud SQL, Cloud Storage, BigQuery)

Cloud SQL: Use private IPs for Cloud SQL instances. Connect to them using the Cloud SQL Auth Proxy or Serverless VPC Access connectors rather than exposing them to the public internet.
Cloud Storage: Enforce Uniform Bucket-Level Access to manage permissions solely via IAM, disabling legacy ACLs. Enable object versioning for critical buckets to protect against accidental overwrites/deletes.
BigQuery: Always partition and cluster large BigQuery tables to minimize query costs (BigQuery charges per byte processed).

4. Networking and Security

VPC Service Controls: Use VPC SC to create secure perimeters around sensitive data (e.g., preventing data exfiltration from Cloud Storage or BigQuery).
Cloud Armor: Attach Google Cloud Armor to Global HTTP(S) Load Balancers to defend against DDoS attacks and implement WAF (Web Application Firewall) rules.

5. Infrastructure as Code

Terraform: Use Terraform as the primary IaC tool for GCP. Store the Terraform state in a centralized, version-enabled Cloud Storage bucket with state locking enabled.