Official agent skill

Azure Storage Blob Py

by microsoft in microsoft/skills

Azure Blob Storage SDK for Python. An agent skill from microsoft/skills.

OfficialMITAuto-check passedBackend & APIs

Install Azure Storage Blob Py

skills CLI
$ npx skills add microsoft/skills --skill azure-storage-blob-py -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install microsoft/skills azure-storage-blob-py --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/microsoft/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py .claude/skills/azure-storage-blob-py && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
azure-storage-blob-py
GitHub stars
3.1k
Token cost
~2.3k tokens
SKILL.md length
354 words
Files
3 (incl. references)
Skills in repo
150
Repo updated
First seen
Licence
MIT

At a glance

Azure Blob Storage SDK for Python. An agent skill from microsoft/skills.

  • Works in 8 steps: Pick sync OR async and stay consistent.… → Always use context managers for clients… → Use DefaultAzureCredential for code that… → …
  • Managing containers
  • SKILL.md covers Installation, Environment Variables, Authentication & Lifecycle and Client Hierarchy, plus 7 more sections
  • Calls pip; reaches learn.microsoft.com; needs AZURE_TOKEN_CREDENTIALS

What it does

Azure Storage Blob Py is an agent skill from microsoft/skills, published by the product's own GitHub organization. Azure Blob Storage SDK for Python. Use for uploading, downloading, listing blobs, managing containers, and blob lifecycle. Triggers: "blob storage", "BlobServiceClient", "ContainerClient", "BlobClient", "upload blob", "download blob".

Its SKILL.md is about 2.3k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/capabilities.md` and `references/non-hero-scenarios.md`).

It sits in Backend & APIs, covering File uploads and storage. It works with Azure Blob Storage, Python, Microsoft Azure and Visual Studio Code. The repository describes itself as: Skills, MCP servers, Custom Agents, Agents.md for SDKs to ground Coding Agents. The licence is MIT.

When your agent uses it

  • Managing containers
  • Tasks that involve File uploads and storage

Example prompts

  • “blob storage”
  • “BlobServiceClient”
  • “ContainerClient”
  • “/azure-storage-blob-py”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Pick sync OR async and stay consistent. Do not mix azure.storage.blob sync clients with azure.storage.blob.aio async clients in the same…
  2. Always use context managers for clients and async credentials. Wrap every client in with BlobServiceClient(...) as client: (sync) or async…
  3. Use DefaultAzureCredential for code that runs locally (instead of connection strings). Use a specific token credential for code that runs…
  4. Set overwrite=True explicitly when re-uploading
  5. Use max_concurrency for large file transfers
  6. Prefer readinto() over readall() for memory efficiency
  7. Use walk_blobs() for hierarchical listing
  8. Set appropriate content types for web-served blobs

What it can do on your machine

Read from SKILL.md and the folder at commit d5741a1. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • learn.microsoft.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • AZURE_TOKEN_CREDENTIALS

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Azure Storage Blob Py loads about 2.3k tokens when it runs, and up to ~3k if it reads all its reference files. Until then it costs about 64 tokens; SKILL.md has 354 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~64
When it runs · the whole SKILL.md, loaded when a task matches
~2.3k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from microsoft/skills at commit d5741a1, republished under its MIT licence (© microsoft). 354 words, ~2,287 tokens.

Download SKILL.mdSave it as .claude/skills/azure-storage-blob-py/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
azure-storage-blob-py
description
Azure Blob Storage SDK for Python. Use for uploading, downloading, listing blobs, managing containers, and blob lifecycle. Triggers: "blob storage", "BlobServiceClient", "ContainerClient", "BlobClient", "upload blob", "download blob".
license
MIT
metadata.author
Microsoft
metadata.version
1.0.0
metadata.package
azure-storage-blob

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>  # Required for all auth methods
# Or use full URL
AZURE_STORAGE_ACCOUNT_URL=https://<account>.blob.core.windows.net  # Alternative to account name
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production

Authentication & Lifecycle

🔑 Two rules apply to every code sample below:

  1. Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation.
    • Local dev: DefaultAzureCredential works as-is.
    • Production: set AZURE_TOKEN_CREDENTIALS=prod (or AZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.
  2. Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:
    • Sync: with <Client>(...) as client:
    • Async: async with <Client>(...) as client: and async with DefaultAzureCredential() as credential: (from azure.identity.aio)

Snippets may abbreviate this setup, but production code should always follow both rules.

python
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.storage.blob import BlobServiceClient

# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
account_url = "https://<account>.blob.core.windows.net"

with BlobServiceClient(account_url, credential=credential) as blob_service_client:
    # Use blob_service_client here (see following sections for operations)
    ...

Client Hierarchy

ClientPurposeGet From
BlobServiceClientAccount-level operationsDirect instantiation
ContainerClientContainer operationsblob_service_client.get_container_client()
BlobClientSingle blob operationscontainer_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
with 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
) as blob_client:
    # Parallel upload
    blob_client.upload_blob(data, max_concurrency=4)

    # Parallel download
    download_stream = blob_client.download_blob(max_concurrency=4)

SAS Tokens (User Delegation)

Generate SAS tokens with a user delegation key signed by Microsoft Entra ID — never with an account key. This keeps SAS issuance tied to Entra audit/rotation.

python
from datetime import datetime, timedelta, timezone
from azure.identity import DefaultAzureCredential
from azure.storage.blob import (
    BlobServiceClient,
    BlobSasPermissions,
    generate_blob_sas,
)

now = datetime.now(timezone.utc)
account_url = "https://<account>.blob.core.windows.net"

with BlobServiceClient(account_url, credential=DefaultAzureCredential()) as service:
    # Get a user delegation key (valid up to 7 days). Caller needs the
    # "Storage Blob Delegator" role on the storage account.
    udk = service.get_user_delegation_key(
        key_start_time=now,
        key_expiry_time=now + timedelta(hours=1),
    )

    sas_token = generate_blob_sas(
        account_name="<account>",
        container_name="mycontainer",
        blob_name="sample.txt",
        user_delegation_key=udk,
        permission=BlobSasPermissions(read=True),
        expiry=now + timedelta(hours=1),
    )

blob_url = f"{account_url}/mycontainer/sample.txt?{sas_token}"

Blob Properties and Metadata

python
# Get properties
properties = blob_client.get_blob_properties()
print(f"Size: {properties.size}")
print(f"Content-Type: {properties.content_settings.content_type}")
print(f"Last modified: {properties.last_modified}")

# Set metadata
blob_client.set_blob_metadata(metadata={"category": "logs", "year": "2024"})

# Set content type
from azure.storage.blob import ContentSettings
blob_client.set_http_headers(
    content_settings=ContentSettings(content_type="application/json")
)

Async Client

python
from azure.identity.aio import DefaultAzureCredential
from azure.storage.blob.aio import BlobServiceClient

async def upload_async():
    async with DefaultAzureCredential() as credential:
        async with BlobServiceClient(account_url, credential=credential) as client:
            blob_client = client.get_blob_client("mycontainer", "sample.txt")
            
            with open("./file.txt", "rb") as data:
                await blob_client.upload_blob(data, overwrite=True)

# Download async
async def download_async():
    async with BlobServiceClient(account_url, credential=credential) as client:
        blob_client = client.get_blob_client("mycontainer", "sample.txt")
        
        stream = await blob_client.download_blob()
        data = await stream.readall()
Show full SKILL.md (152 more words)Show less

Best Practices

  1. Pick sync OR async and stay consistent. Do not mix azure.storage.blob sync clients with azure.storage.blob.aio async clients in the same call path. Choose one mode per module.
  2. Always use context managers for clients and async credentials. Wrap every client in with BlobServiceClient(...) as client: (sync) or async with BlobServiceClient(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
  3. Use DefaultAzureCredential for code that runs locally (instead of connection strings). Use a specific token credential for code that runs in Azure.
  4. Set overwrite=True explicitly when re-uploading
  5. Use max_concurrency for large file transfers
  6. Prefer readinto() over readall() for memory efficiency
  7. Use walk_blobs() for hierarchical listing
  8. Set appropriate content types for web-served blobs

Reference Files

FileContents
references/capabilities.mdCapability index mapping hero flows and non-hero references.
references/non-hero-scenarios.mdDedicated non-hero examples (metadata/properties and async patterns).

© microsoft, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in .github/plugins/azure-sdk-python/skills/azure-storage-blob-py of microsoft/skills.

  • SKILL.md
  • references/capabilities.md
  • references/non-hero-scenarios.md

Open the folder on GitHubat commit d5741a1

Compare with similar skills

Azure Storage Blob Py next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Azure Storage Blob Py compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Azure Storage Blob Py this skillmicrosoft/skills3.1k—~2.3kAutomated safety check: PassMIT
Azure Storagemicrosoft/GitHub-Copilot-for-Azure2552 repos~1.3kAutomated safety check: PassMIT
Edgeone Makers ToolsTencentEdgeOne/edgeone-makers-tools1.9k1 repos~646Automated safety check: PassMIT
Cloud Retention Configmukul975/Privacy-Data-Protection-Skills301—~3.7kAutomated safety check: PassApache-2.0
Azurekid-sid/claude-spellbook190—~3.7kAutomated safety check: NotesMIT
Google Cloud Solution Agentic Analytics Spark Knowledge Cataloggoogle/skills21k—~4.4kAutomated safety check: PassApache-2.0

Similar skills

  • Azure Storage

    microsoft/GitHub-Copilot-for-Azure

    Official

    Azure Storage Services including Blob Storage, File Shares, Queue Storage, Table Storage, and Data Lake.

    255 GitHub starsUsed in 2 repos~1.3k tokens
    DatabasesAuto-check passed
  • Edgeone Makers Tools

    TencentEdgeOne/edgeone-makers-tools

    EdgeOne Makers platform development router — the single entry point for building, storing data, and deploying on Tencent EdgeOne Makers.

    1.9k GitHub starsUsed in 1 repo~646 tokens
    AI & LLM EngineeringAuto-check passed
  • Cloud Retention Config

    mukul975/Privacy-Data-Protection-Skills

    Configures cloud storage retention policies across AWS S3, Azure Blob Storage, and Google Cloud Storage.

    301 GitHub stars~3.7k tokensUpdated 6 mo ago
    Legal & ComplianceAuto-check passed
  • Azure

    kid-sid/claude-spellbook

    A skill your agent uses when writing Python code that integrates with Azure Blob Storage, AI Search, Document Intelligence, or Key Vault — or when configuring Managed Identity auth, designing a…

    190 GitHub stars~3.7k tokensUpdated 2 mo ago
    DevOps & CloudAuto-check: notes
  • Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine).

    21k GitHub stars~4.4k tokensUpdated today
    Data & AnalyticsAuto-check passed
  • Apex Azure Storage

    jonathan-vella/apex

    UTILITY SKILL — Azure Storage: Blob, File Shares, Queue, Table and Data Lake, including access tiers and lifecycle management.

    217 GitHub stars~1.7k tokensUpdated today
    DevOps & CloudAuto-check passed

More from microsoft/skills

All 150 skills in this repo
  • Official

    Covers producer, consumer, and checkpoint-store setup for Azure Event Hubs streaming in Python, with Entra ID auth and partition targeting.

    3.1k GitHub starsUsed in 1 repo~2.3k tokens
    Auto-check passed
  • Official

    Builds podcast-style audio narration from text with Azure OpenAI's GPT Realtime Mini over WebSocket, from a Python FastAPI backend to a React player.

    3.1k GitHub starsUsed in 1 repo~947 tokens
    Auto-check passed
  • Frontend UI Dark TS

    microsoft/skills

    Official

    Build dark-themed React applications using Tailwind CSS with custom theming, glassmorphism effects, and Framer Motion animations.

    3.1k GitHub starsUsed in 5 repos~3.6k tokens
    Auto-check passed
  • Pydantic Models Py

    microsoft/skills

    Official

    Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants.

    3.1k GitHub starsUsed in 5 repos~496 tokens
    Auto-check passed
  • Official

    Reference for building on Microsoft Foundry with the azure-ai-projects Python SDK: project clients, versioned agents, evaluations, connections, datasets and indexes.

    3.1k GitHub stars~2.8k tokensUpdated yesterday
    Auto-check passed
  • Skill Creator

    microsoft/skills

    Official

    Guide for creating effective skills for AI coding agents working with Azure SDKs and Microsoft Foundry services.

    3.1k GitHub starsUsed in 5 repos~17k tokens
    Auto-check passed

Categories

Questions about Azure Storage Blob Py

What does Azure Storage Blob Py do?

Azure Blob Storage SDK for Python. An agent skill from microsoft/skills. Azure Storage Blob Py is an agent skill from microsoft/skills, published by the product's own GitHub organization. Azure Blob Storage SDK for Python.

When should I use Azure Storage Blob Py?

Azure Storage Blob Py fits situations like: managing containers; tasks that involve File uploads and storage.

How do I install Azure Storage Blob Py in Claude Code?

Run `npx skills add microsoft/skills --skill azure-storage-blob-py -a claude-code`. Or copy the skill folder (.github/plugins/azure-sdk-python/skills/azure-storage-blob-py in microsoft/skills) into .claude/skills/azure-storage-blob-py in your project. Claude Code loads it when a task matches its description.

How do I install Azure Storage Blob Py in Codex?

Run `npx skills add microsoft/skills --skill azure-storage-blob-py -a codex`. Or copy the skill folder (.github/plugins/azure-sdk-python/skills/azure-storage-blob-py in microsoft/skills) into .agents/skills/azure-storage-blob-py in your project. Codex loads it when a task matches its description.

Can I use Azure Storage Blob Py in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add microsoft/skills --skill azure-storage-blob-py -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/azure-storage-blob-py, .gemini/skills/azure-storage-blob-py, .github/skills/azure-storage-blob-py and .opencode/skills/azure-storage-blob-py in your project.

What does Azure Storage Blob Py need to run?

Going by SKILL.md and its folder, Azure Storage Blob Py needs the command-line tools its instructions call (pip) and credentials named AZURE_TOKEN_CREDENTIALS. Our summary lists: Python 3.

Does Azure Storage Blob Py access the network?

SKILL.md names 1 domain. In commands or code: learn.microsoft.com; the agent is likely to contact it when it follows the instructions. This is read from the text; nothing was executed.

Is Azure Storage Blob Py safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Azure Storage Blob Py use?

Azure Storage Blob Py is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Azure Storage Blob Py use?

About 2.3k tokens (SKILL.md is roughly 9.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 733 tokens, read only when the agent opens those files.

What are the alternatives to Azure Storage Blob Py?

Skills that share tags, products or a category with Azure Storage Blob Py: Azure Storage (microsoft/GitHub-Copilot-for-Azure, 255 stars), Edgeone Makers Tools (TencentEdgeOne/edgeone-makers-tools, 1.9k stars), Cloud Retention Config (mukul975/Privacy-Data-Protection-Skills, 301 stars) and Azure (kid-sid/claude-spellbook, 190 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Azure Storage Blob Py?

microsoft (a GitHub organization, an official publisher) maintains it in microsoft/skills, which has 3,097 GitHub stars. The repository holds 150 skills in this directory. The repository was last updated on October 9, 2026.

Source: microsoft/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.