Feature Request: Cloud Storage (GCS) Skill

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Assessment

Difficulty
4/5
Estimated time
3-5 days
Newbie friendliness
45/100
Issue type
Feature
Clarity
Mostly clear
Activity status
Quiet
Tech stack
google-cloud, kubernetes
Domain
cloud, documentation

Research direction

Start by reviewing the proposed SKILL.md frontmatter and the linked Google Cloud Storage, GCS Fuse CSI driver, and Cloud Run volume-mount documentation. Define the gcs-basics skill around the listed bucket setup, storage classes, security, integrations, signed URLs, lifecycle, and performance topics. Done means a complete draft covers these reference areas and the stated user triggers.

Written by the indexing model from the issue text.

Description

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
  1. Golden Path Bucket Setup — uniform bucket-level access, IAM-only, no legacy ACLs
  2. Storage Classes & Cost — decision matrix (Standard vs Nearline vs Coldline vs Archive), autoclass
  3. Security — public access prevention, VPC Service Controls, encryption (CMEK vs Google-managed)
  4. 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)
  5. Signed URLs — V4 signing, expiration best practices, service account key vs workload identity
  6. Lifecycle Management — transition rules, deletion rules, abort incomplete multipart upload
  7. 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.

Dominant language
Python
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Getting set up

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

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