diff --git a/docs/hub/storage-buckets-access.md b/docs/hub/storage-buckets-access.md
index 1ce890aae..2d93f2163 100644
--- a/docs/hub/storage-buckets-access.md
+++ b/docs/hub/storage-buckets-access.md
@@ -12,6 +12,8 @@ Beyond the [CLI and Python SDK](./storage-buckets#managing-files), there are sev
 | **CLI sync** | Batch transfers, backups | [Sync docs](./storage-buckets#syncing-directories) |
 | **S3 API** | Existing S3 tooling (AWS CLI, boto3, s5cmd) | [S3-Compatible API](./storage-buckets-s3) |
 
+For tools that use buckets as a backend (SkyPilot, Inspect, …), see [Integrations](./storage-buckets-integrations).
+
 ## Mount as a Local Filesystem
 
 [hf-mount](https://github.com/huggingface/hf-mount) lets you mount buckets (and repos) as local filesystems via NFS (recommended) or FUSE. Files are fetched lazily — only the bytes your code reads hit the network.
diff --git a/docs/hub/storage-buckets-integrations.md b/docs/hub/storage-buckets-integrations.md
index 4a26c8a49..24fa653c7 100644
--- a/docs/hub/storage-buckets-integrations.md
+++ b/docs/hub/storage-buckets-integrations.md
@@ -79,6 +79,48 @@ inspect view
 
 See [Inspect's eval logs guide](https://inspect.aisi.org.uk/eval-logs.html#sec-hugging-face-storage-buckets) for details.
 
+## SkyPilot
+
+[SkyPilot](https://docs.skypilot.co/) runs AI workloads across 20+ clouds, Kubernetes, and on-prem, and can use Hugging Face storage as a backend — so one bucket is readable from every cloud with no per-cloud copies. Set `store: hf` on a `file_mounts` entry to mount a bucket read-write or a repo read-only:
+
+```yaml
+# qwen-sft.yaml — launch anywhere: sky launch qwen-sft.yaml --infra aws|gcp|...
+resources:
+  accelerators: H100:1
+
+file_mounts:
+  /base-model:
+    source: hf://Qwen/Qwen2.5-3B           # model repo, read-only
+    store: hf
+    mode: MOUNT
+  /data:
+    source: hf://datasets/username/my-data@v1.0   # dataset repo, pinned to a tag, read-only
+    store: hf
+    mode: MOUNT
+  /checkpoints:
+    source: hf://buckets/username/qwen-sft   # bucket, read-write — checkpoints sync back
+    store: hf
+    mode: MOUNT
+
+run: |
+  python train.py --model /base-model --output_dir /checkpoints
+```
+
+Authenticate once - `hf auth login` (or `export HF_TOKEN=<your-token>`) is all SkyPilot needs. It forwards your local Hugging Face token to every cloud, so the bucket and repo mounts authenticate automatically:
+
+```bash
+pip install "skypilot[huggingface]"
+hf auth login                              # or: export HF_TOKEN=<your-token>
+sky launch qwen-sft.yaml
+```
+
+If your own `run` code pulls gated repos, add `--secret HF_TOKEN` to the launch command to also expose the token as an env var.
+
+> [!TIP]
+> `MOUNT` and `MOUNT_CACHED` behave identically for `hf` and use the [hf-mount](https://github.com/huggingface/hf-mount) FUSE backend, which needs a base image with glibc ≥ 2.34 and `/dev/fuse`. Bare-VM clouds provide both. SkyPilot's default Kubernetes image ships older glibc, so set a newer `image_id` (e.g. `docker:mirror.gcr.io/ubuntu:22.04`). See the [SkyPilot storage docs](https://docs.skypilot.co/en/latest/reference/storage.html) for the current environment requirements.
+
+See the [SkyPilot + Hugging Face storage blog post](https://huggingface.co/blog/skypilot-hf-storage) for benchmarks and a full walkthrough.
+
 ## Filesystem operations
 
 For direct file operations, `huggingface_hub` exposes a pre-instantiated [filesystem object](/docs/huggingface_hub/guides/hf_file_system), `hffs`: