MR
Mayur Rathi
@mayurrathi
⭐ 40.7k GitHub stars

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.

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Last verified on: 2026-07-08

Quick Facts

Category code
Works With Claude
Source sickn33/antigravity-awesome-skills
Stars ⭐ 40.7k
Last Verified 2026-07-08
Risk Level Low
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


bash
pip install azure-storage-blob azure-identity

Environment Variables


bash
AZURE_STORAGE_ACCOUNT_NAME=<your-storage-account>
# Or use full URL
AZURE_STORAGE_ACCOUNT_URL=https://<account>.blob.core.windows.net

Authentication


python
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


python
container_client = blob_service_client.get_container_client("mycontainer")
container_client.create_container()

Upload Blob


python
# 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


python
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


python
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


python
blob_client.delete_blob()

# Delete with snapshots
blob_client.delete_blob(delete_snapshots="include")

Performance Tuning


python
# 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


python
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


python
# 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. 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. 2

    Load into Your AI Assistant

    Open Claude and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 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. 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.

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