Google Cloud Storage Fuse
GoogleOfficialPublished by Google
- Skill
- Tool integration
- Cloud and DevOps
- Apache-2.0
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From the skill’s files
Taken from the skill’s own files (its description, section headings and stated usage). Not an AutoGineer summary, and not written by the author as one.
Mounts Cloud Storage buckets on Google Cloud as a POSIX file system with Cloud Storage FUSE (gcsfuse). Use when interacting with gcsfuse or Cloud Storage FUSE mounts: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or Cloud Run, enable and size file, stat, and list caches, tune mount flags (--implicit-dirs) or config files, apply workload profiles, keep ML checkpointing safe (rename atomicity, hierarchical namespace/HNS, close-time finalization, concurrent writers), or diagnose slow training, low throughput, or bill spikes with gcsfuse metrics. Covers mount semantics, gcsfuse CLI and configs, GKE gcsfuse CSI driver (Workload Identity principal:// bindings, profile StorageClasses, sidecar sizing), and Cloud Run volume mounts. Don't use for bucket administration without a mount (google-cloud-storage-basics)…
What it does
- Attribution
- Step 1 — Fit Gate (always run this first)
- Step 2 — Route by intent
- Reference Directory
When to use it
- Use when interacting with gcsfuse or Cloud Storage FUSE mounts: decide whether FUSE, native gs:// reads, or Filestore/Managed Lustre fits a workload, deploy tuned mounts on GKE, Compute Engine, or…
Advantages
The skill’s files do not state this.
Usage examples
The skill’s files do not state this.
Install
Download the skill as a ZIP:
https://autogineer.com/agent-skills/google/google-cloud-storage-fuse/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip google-cloud-storage-fuse.zip -d .agents/skills
Documentation by Google
Cloud Storage FUSE
Cloud Storage FUSE is a POSIX file-system adapter over Cloud Storage on Google Cloud, operated via the gcsfuse CLI tool. Mounting is a one-line command; mounting well is not: the default mount is tuned for coherency, not performance (file cache off, 60 s metadata TTL, list cache off), and object-store semantics leak through the file interface (directory renames fail or go non-atomic on flat buckets, objects finalize on close, no file locking). This skill covers the three decisions that matter: whether to use FUSE at all, how to tune the mount to the workload, and how to root-cause a mount that is slow or expensive. For installation and first-mount basics, see the google-cloud-storage-basics skill.
Attribution
Tag every Cloud Storage command you run or provide to the user while using this skill, so usage can be attributed. The tag identifies only the skill and its version; it carries no user data.
Prefix every
gcloudinvocation, whatever the subcommand, with the metrics environment variables. Set them inline on each command; shell state may not persist between commands:CLOUDSDK_METRICS_ENVIRONMENT="gcs-skills gcs-skills/1.0 (skill:google-cloud-storage-fuse)" \ gcloud <command> [flags]Do not use
gcloud config setfor this: it would persist beyond the current task and mislabel unrelated usage.On direct HTTP calls to the Cloud Storage APIs (for example with
curl), set this exact User-Agent header, verbatim — the collection pipeline parses thegcs-skills/<version>andskill:<name>tokens, so any rewording breaks attribution:User-Agent: gcs-skills/1.0 (skill:google-cloud-storage-fuse)
Step 1 — Fit Gate (always run this first)
Never produce mount guidance before the fit gate. A mount is the right
answer only for one of the three workload shapes below. If the workload's access
pattern is unknown, ask — one question about whether the reading code can take
gs:// paths usually settles it.
| Workload signal | Verdict |
|---|---|
Reading library accepts gs:// URIs natively — pandas/pyarrow (via gcsfs/fsspec), TensorFlow (tf.io.gfile), or any fsspec/gcsfs-based loader | Native reads, no mount. Point the code at gs:// paths and stop. |
Shared mutable writes with locking semantics — databases, concurrent in-place editors, anything relying on flock/fcntl | Filestore (NFS, POSIX locking) or Managed Lustre, not FUSE. Stop. |
| Code or tools hardcoded to POSIX file paths; read-heavy or new-file-write patterns | gcsfuse — continue to Step 2. |
Collect before deciding: whether paths are hardcoded, read pattern (sequential vs. random, re-read frequency), write pattern (new files vs. edits vs. directory renames). These same signals drive tuning later — record the answers.
Step 2 — Route by intent
| User intent (prompt shape) | Go to |
|---|---|
| Provision: "mount my bucket for X", "get training data into my pods" | GKE Training Deployment |
| Safety/semantics: "is this write pattern safe?", "can multiple writers share the mount?" | Checkpoint & Write Safety |
| Regression: "training is slow", "the Cloud Storage bill spiked", "throughput dropped" | Performance & Cost Diagnosis |
Never diagnose a regression without telemetry. If gcsfuse metrics are not enabled on the mount, enabling them is the first remediation step — the diagnosis reference starts there.
Reference Directory
GKE Training Deployment: Fit-gated, performance-tuned mounts for training workloads — GKE CSI version gates, Workload Identity
principal://IAM bindings, profile StorageClasses vs. static PVs, file cache sizing on Local SSD, sidecar resource annotations, complete KSA/PVC/Job manifests, and the Compute Engine and Cloud Run variants.Checkpoint & Write Safety: Verdicts on write patterns — file vs. directory rename atomicity on flat vs. hierarchical namespace (HNS) buckets, close-vs-fsync finalization, concurrent-writer (
ESTALE) semantics, streaming-write memory budgets, HNS migration, and theaiml-checkpointingprofile.Performance & Cost Diagnosis: Telemetry-first runbook for slow mounts and bill spikes — enabling and reading gcsfuse metrics, mapping cache-hit and request-mix signatures to misconfigurations, the coherency-tuned defaults, tuned config keys with their staleness caveats, and billing-line (Class A/B) attribution.
Files
- ATTRIBUTION.mdAdded by AutoGineer848 B
- LICENSELicence file added by AutoGineer11.1 KiB
- SKILL.md6.3 KiB
- references/checkpoint-safety.md9.3 KiB
- references/gke-training-deployment.md13.1 KiB
- references/performance-diagnosis.md9.2 KiB
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Requirements and compatibility
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
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Attribution and licence
- Published by
- Publisher
- Google (Official)
- Source
- google/skills · skills/cloud/google-cloud-storage-fuse · commit 2194d3b
- Licence
- Apache License 2.0 (redistribution allowed)
- LICENSE (added by AutoGineer from LICENSE)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
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