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gcp

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Aktualisiert31. Mai 2026 um 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

neverinfamous/memory-journal-mcp

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SoftwareentwicklerInformatik- und Mathematikberufe·SOC 15-1252

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 Your knowledge of GCP may be outdated.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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Multi-pass adversarial security audit for entire repositories. Combines structured threat modeling (Agent A) with adversarial attack surface analysis (Agent B) through iterative passes. Merges the security-audit workflow's 10-category checklist with the adversarial-planner's structured critique methodology. Use when running security audits, threat modeling, or when the user says "security audit", "adversarial security", "threat model this repo", "red team this repo", or "find vulnerabilities". NOT for fixing or remediating known security issues (use autonomous-dev instead). NOT for supply chain dependency scanning. NOT for general workflow audits or code quality. If user says 'fix security issues', ask whether they want to audit first or fix known issues.

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Multi-pass adversarial quality audit for agent skill directories. Combines structured evaluation with adversarial stress-testing to assess skill completeness, instruction clarity, trigger accuracy, and security. Use when auditing a skills directory.

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Multi-pass adversarial quality audit for flat markdown workflow files. Combines structured evaluation (Agent A) with adversarial stress-testing (Agent B) to assess sequential clarity, prerequisite validation, HITL (Human-in-the-loop) safety gates, loop prevention, and formatting. Use when auditing workflow scripts (e.g., slash-commands like /bump-deploy), or when the user asks to review or improve operational playbooks.

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Core rules for code quality and specifications of Model Context Protocol (MCP) servers. Use when reviewing MCP code quality, enforcing specification rules, or checking schemas/error responses. Must see the explicit keyword "MCP" or "Model Context Protocol". NOT for general REST APIs or Cloudflare Workers. Do NOT trigger for generic 'build a server' requests unless the type is explicitly specified as an MCP server. (Invoke explicitly with `/mcp-builder` — does not auto-trigger).

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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.