Azure Storage Blob Py
Azure Storage Blob Py is an code AI skill with a core value of |. It
helps developers solve real-world problems in the code domain, boosting
efficiency, automating repetitive tasks, and optimizing workflows.
|
Quick Facts
mkdir -p ./skills/azure-storage-blob-py && curl -sfL https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/skills/azure-storage-blob-py/SKILL.md -o ./skills/azure-storage-blob-py/SKILL.md Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).
Skill Content
# Azure Blob Storage SDK for Python
Client library for Azure Blob Storage — object storage for unstructured data.
Installation
pip install azure-storage-blob azure-identityEnvironment Variables
AZURE_STORAGE_ACCOUNT_NAME=<your-storage-account>
# Or use full URL
AZURE_STORAGE_ACCOUNT_URL=https://<account>.blob.core.windows.netAuthentication
from azure.identity import DefaultAzureCredential
from azure.storage.blob import BlobServiceClient
credential = DefaultAzureCredential()
account_url = "https://<account>.blob.core.windows.net"
blob_service_client = BlobServiceClient(account_url, credential=credential)Client Hierarchy
| Client | Purpose | Get From |
|--------|---------|----------|
| `BlobServiceClient` | Account-level operations | Direct instantiation |
| `ContainerClient` | Container operations | `blob_service_client.get_container_client()` |
| `BlobClient` | Single blob operations | `container_client.get_blob_client()` |
Core Workflow
Create Container
container_client = blob_service_client.get_container_client("mycontainer")
container_client.create_container()Upload Blob
# From file path
blob_client = blob_service_client.get_blob_client(
container="mycontainer",
blob="sample.txt"
)
with open("./local-file.txt", "rb") as data:
blob_client.upload_blob(data, overwrite=True)
# From bytes/string
blob_client.upload_blob(b"Hello, World!", overwrite=True)
# From stream
import io
stream = io.BytesIO(b"Stream content")
blob_client.upload_blob(stream, overwrite=True)Download Blob
blob_client = blob_service_client.get_blob_client(
container="mycontainer",
blob="sample.txt"
)
# To file
with open("./downloaded.txt", "wb") as file:
download_stream = blob_client.download_blob()
file.write(download_stream.readall())
# To memory
download_stream = blob_client.download_blob()
content = download_stream.readall() # bytes
# Read into existing buffer
stream = io.BytesIO()
num_bytes = blob_client.download_blob().readinto(stream)List Blobs
container_client = blob_service_client.get_container_client("mycontainer")
# List all blobs
for blob in container_client.list_blobs():
print(f"{blob.name} - {blob.size} bytes")
# List with prefix (folder-like)
for blob in container_client.list_blobs(name_starts_with="logs/"):
print(blob.name)
# Walk blob hierarchy (virtual directories)
for item in container_client.walk_blobs(delimiter="/"):
if item.get("prefix"):
print(f"Directory: {item['prefix']}")
else:
print(f"Blob: {item.name}")Delete Blob
blob_client.delete_blob()
# Delete with snapshots
blob_client.delete_blob(delete_snapshots="include")Performance Tuning
# Configure chunk sizes for large uploads/downloads
blob_client = BlobClient(
account_url=account_url,
container_name="mycontainer",
blob_name="large-file.zip",
credential=credential,
max_block_size=4 * 1024 * 1024, # 4 MiB blocks
max_single_put_size=64 * 1024 * 1024 # 64 MiB single upload limit
)
# Parallel upload
blob_client.upload_blob(data, max_concurrency=4)
# Parallel download
download_stream = blob_client.download_blob(max_concurrency=4)SAS Tokens
from datetime import datetime, timedelta, timezone
from azure.storage.blob import generate_blob_sas, BlobSasPermissions
sas_token = generate_blob_sas(
account_name="<account>",
container_name="mycontainer",
blob_name="sample.txt",
account_key="<account-key>", # Or use user delegation key
permission=BlobSasPermissions(read=True),
expiry=datetime.now(timezone.utc) + timedelta(hours=1)
)
# Use SAS token
blob_url = f"https://<account>.blob.core.windows.net/mycontainer/sample.txt?{sas_token}"Blob Properties and Metadata
# Get properties
properties = blob_client.get_blob_properties()
print(f"Size: {properties.size}🎯 Best For
- Claude users
- Software engineers
- Development teams
- Tech leads
💡 Use Cases
- Code quality improvement
- Best practice enforcement
📖 How to Use This Skill
- 1
Install the Skill
Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.
- 2
Load into Your AI Assistant
Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.
- 3
Apply Azure Storage Blob Py to Your Work
Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.
- 4
Review and Refine
Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.
❓ Frequently Asked Questions
Is Azure Storage Blob Py compatible with Cursor and VS Code?
Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.
Do I need specific dependencies for Azure Storage Blob Py?
Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.
How do I install Azure Storage Blob Py?
Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/azure-storage-blob-py/SKILL.md, ready to use.
Can I customize this skill for my team?
Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.
⚠️ Common Mistakes to Avoid
Skipping validation
Always test AI-generated code changes, even for simple refactors.
Missing dependency updates
Check if the skill requires updated dependencies or new packages.