Feature Request: Cloud Storage (GCS) Skill
Summary
Google Cloud Storage is the foundational object storage service for nearly every GCP workload — yet there is currently no dedicated skill to guide agents (and users) on bucket provisioning, security hardening, lifecycle management, or integration patterns with other Google Cloud services.
The Gap
A typical agent workflow today:
User asks about storing files on GCP → Agent loads gcloud or cloud-run-basics skill → Guidance is fragmented and does not cover storage-specific concerns (IAM vs ACL, signed URLs, storage classes, CORS, etc.)
Without a dedicated gcs-basics skill, agents cannot reliably answer:
- Which storage class (Standard, Nearline, Coldline, Archive) fits a given retention and access pattern?
- How to generate signed URLs for temporary, secure public access?
- How to mount a GCS bucket as a file system in GKE (via CSI driver) or Cloud Run (via volume mounts)?
- How to enforce uniform bucket-level access and prevent ACL drift?
- How to set up object lifecycle rules to auto-transition or delete stale data?
Proposed Skill
A gcs-basics skill that agents load when users mention: Cloud Storage, GCS, bucket, object storage, file upload, signed URL, storage class, lifecycle policy, or CORS.
Suggested SKILL.md frontmatter
---
name: gcs-basics
description: >
Use when the user asks about storing, retrieving, or managing objects on Google Cloud.
Covers bucket creation, IAM/ACL permissions, storage class selection, lifecycle policies,
signed URLs, CORS configuration, and integration with GKE (CSI), Cloud Run (volume mounts),
and BigQuery (external tables). WHEN: create bucket, upload file, storage class, lifecycle
rule, signed URL, CORS, GCS mount, GCS security, GCS cost optimization.
compatibility: Requires storage.objectViewer or storage.objectAdmin IAM role and the Cloud Storage API enabled.
---
Key reference topics
- Golden Path Bucket Setup — uniform bucket-level access, IAM-only, no legacy ACLs
- Storage Classes & Cost — decision matrix (Standard vs Nearline vs Coldline vs Archive), autoclass
- Security — public access prevention, VPC Service Controls, encryption (CMEK vs Google-managed)
- Integration Patterns
- GKE: GCS Fuse CSI driver for pod volume mounts
- Cloud Run: GCS volume mounts (second gen)
- BigQuery: External tables over GCS (Parquet/ORC/CSV/JSON)
- Signed URLs — V4 signing, expiration best practices, service account key vs workload identity
- Lifecycle Management — transition rules, deletion rules, abort incomplete multipart upload
- Performance — parallel composite uploads, turbo replication, dual-region buckets
Why Now?
- GCS is referenced implicitly by at least 5 existing skills (GKE, Cloud Run, BigQuery, Firebase, Gemini API media upload) but never explained in depth.
- Agent Platform users increasingly ask about multi-modal pipelines where images, audio, and documents flow through Cloud Storage before reaching Gemini.
- The recent GCS FUSE and Cloud Run volume mount features are not documented in any existing skill.
Reference Implementation
Google Cloud official docs:
Happy to contribute a SKILL.md draft if this direction is accepted.
Feature Request: Cloud Storage (GCS) Skill
Summary
Google Cloud Storage is the foundational object storage service for nearly every GCP workload — yet there is currently no dedicated skill to guide agents (and users) on bucket provisioning, security hardening, lifecycle management, or integration patterns with other Google Cloud services.
The Gap
A typical agent workflow today:
User asks about storing files on GCP → Agent loads
gcloudorcloud-run-basicsskill → Guidance is fragmented and does not cover storage-specific concerns (IAM vs ACL, signed URLs, storage classes, CORS, etc.)Without a dedicated
gcs-basicsskill, agents cannot reliably answer:Proposed Skill
A
gcs-basicsskill that agents load when users mention: Cloud Storage, GCS, bucket, object storage, file upload, signed URL, storage class, lifecycle policy, or CORS.Suggested SKILL.md frontmatter
Key reference topics
Why Now?
Reference Implementation
Google Cloud official docs:
Happy to contribute a SKILL.md draft if this direction is accepted.