diff --git a/skypilot/README.md b/skypilot/README.md
index 93d7db3..6ced42f 100644
--- a/skypilot/README.md
+++ b/skypilot/README.md
@@ -22,6 +22,12 @@ A multi-node distributed training configuration using PyTorch's Distributed Data
 
 **Use Case:** Large-scale distributed training across multiple nodes for computationally intensive models.
 
+### 4. my-caios-devpod.yaml
+
+A development environment configuration demonstrating CoreWeave Object Storage (CAIOS) integration with boto3 for reading, writing, and listing objects. If you have not yet configured CAIOS credentials, please follow the guidance in [this section](https://github.com/coreweave/reference-architecture/tree/main/storage/caios-credentials) to automatically configure CAIOS credentials. 
+
+**Use Case:** Testing and validating CAIOS bucket access with AWS-compatible tools in a GPU-accelerated development environment. 
+
 ## Getting Started
 
 To use any of these configurations:
diff --git a/skypilot/config-examples/my-caios-devpod.yaml b/skypilot/config-examples/my-caios-devpod.yaml
new file mode 100644
index 0000000..d213b26
--- /dev/null
+++ b/skypilot/config-examples/my-caios-devpod.yaml
@@ -0,0 +1,89 @@
+name: mydevpod
+
+resources:
+  # Modify this below to request different resources
+  accelerators: H200:1  # Use 1 H200
+  image_id: docker:ghcr.io/coreweave/ml-containers/nightly-torch-extras:8b6c417-base-25110205-cuda12.9.1-ubuntu22.04-torch2.10.0a0-vision0.25.0a0-audio2.10.0a0
+  memory: 32+  # Request at least 32GB of RAM
+
+file_mounts:
+  /my_data: # Mount storage bucket to /my_data in the container
+    source: cw://BUCKETNAME # Change this to be your bucket name
+    mode: MOUNT  # MOUNT or COPY or MOUNT_CACHED. Defaults to MOUNT. Optional.
+# Sync data in my-code/ on local machine to ~/sky_workdir in the container
+# workdir: ./my-code
+
+#Environment variables to set in the container
+# These are needed to access CoreWeave Object Storage using the AWS CLI
+envs:
+  AWS_SHARED_CREDENTIALS_FILE: "~/.coreweave/cw.credentials"
+  AWS_CONFIG_FILE: "~/.coreweave/cw.config"
+  AWS_PROFILE: "cw"
+
+
+# Any setup commands to run in the container before 'run'
+# Here we install the AWS CLI to access storage
+setup: |
+  echo "Setting up test storage environment..."
+  # Install AWS CLI
+  apt install python3.10-venv -y
+  curl "https://s3.amazonaws.com/aws-cli/awscli-bundle.zip" -o "awscli-bundle.zip"
+  unzip awscli-bundle.zip
+  sudo ./awscli-bundle/install -i /usr/local/aws -b /usr/local/bin/aws
+  echo export AWS_CONFIG_FILE=$AWS_CONFIG_FILE >> ~/.bashrc
+  echo export AWS_SHARED_CREDENTIALS_FILE=$AWS_SHARED_CREDENTIALS_FILE >> ~/.bashrc
+  echo export AWS_PROFILE=$AWS_PROFILE >> ~/.bashrc
+  # Install boto3
+  pip install boto3
+
+run: |
+  echo "Testing CAIOS bucket access with boto3..."
+  python3 << 'EOF'
+  import boto3
+  from botocore.client import Config
+  import os
+  
+  # LOTA endpoint for CoreWeave Object Storage
+  ENDPOINT_URL = 'http://cwlota.com'
+  BUCKET_NAME = 'BUCKET_NAME'
+  
+  # Read credentials from AWS config files
+  session = boto3.Session(profile_name='cw')
+  credentials = session.get_credentials()
+  
+  # Create S3 client with virtual-hosted style addressing
+  s3_client = boto3.client(
+      's3',
+      endpoint_url=ENDPOINT_URL,
+      aws_access_key_id=credentials.access_key,
+      aws_secret_access_key=credentials.secret_key,
+      config=Config(s3={'addressing_style': 'virtual'})
+  )
+  
+  # Write test
+  print(f"Writing test file to {BUCKET_NAME}...")
+  test_content = "Hello from SkyPilot on CoreWeave!"
+  s3_client.put_object(
+      Bucket=BUCKET_NAME,
+      Key='skypilot_test.txt',
+      Body=test_content.encode('utf-8')
+  )
+  print("✓ Write successful!")
+  
+  # Read test
+  print(f"Reading test file from {BUCKET_NAME}...")
+  response = s3_client.get_object(Bucket=BUCKET_NAME, Key='skypilot_test.txt')
+  content = response['Body'].read().decode('utf-8')
+  print(f"✓ Read successful! Content: {content}")
+  
+  # List objects
+  print(f"\nListing objects in {BUCKET_NAME}:")
+  response = s3_client.list_objects_v2(Bucket=BUCKET_NAME, MaxKeys=10)
+  if 'Contents' in response:
+      for obj in response['Contents']:
+          print(f"  - {obj['Key']} ({obj['Size']} bytes)")
+  else:
+      print("  Bucket is empty")
+  
+  print("\n✓ CAIOS test completed successfully!")
+  EOF
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