Azure Storage
microsoft/GitHub-Copilot-for-Azure
Azure Storage Services including Blob Storage, File Shares, Queue Storage, Table Storage, and Data Lake.
Azure Blob Storage SDK for Python. An agent skill from microsoft/skills.
$ npx skills add microsoft/skills --skill azure-storage-blob-py -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install microsoft/skills azure-storage-blob-py --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "azure-storage-blob-py" agent skill from https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py into .claude/skills/azure-storage-blob-py/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "azure-storage-blob-py", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-storage-blob-pyType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add microsoft/skills --skill azure-storage-blob-py -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install microsoft/skills azure-storage-blob-py --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/microsoft/skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py .agents/skills/azure-storage-blob-py && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "azure-storage-blob-py" agent skill from https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py into .agents/skills/azure-storage-blob-py/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "azure-storage-blob-py", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add microsoft/skills --skill azure-storage-blob-py -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install microsoft/skills azure-storage-blob-py --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/microsoft/skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py .cursor/skills/azure-storage-blob-py && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "azure-storage-blob-py" agent skill from https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py into .cursor/skills/azure-storage-blob-py/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "azure-storage-blob-py", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/microsoft/skills.git --path .github/plugins/azure-sdk-python/skills/azure-storage-blob-py--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add microsoft/skills --skill azure-storage-blob-py -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install microsoft/skills azure-storage-blob-py --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/microsoft/skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py .gemini/skills/azure-storage-blob-py && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "azure-storage-blob-py" agent skill from https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py into .gemini/skills/azure-storage-blob-py/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "azure-storage-blob-py", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install microsoft/skills azure-storage-blob-pyInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add microsoft/skills --skill azure-storage-blob-py -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/microsoft/skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py .github/skills/azure-storage-blob-py && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "azure-storage-blob-py" agent skill from https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py into .github/skills/azure-storage-blob-py/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "azure-storage-blob-py", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add microsoft/skills --skill azure-storage-blob-py -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install microsoft/skills azure-storage-blob-py --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/microsoft/skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py .opencode/skills/azure-storage-blob-py && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "azure-storage-blob-py" agent skill from https://github.com/microsoft/skills/tree/main/.github/plugins/azure-sdk-python/skills/azure-storage-blob-py into .opencode/skills/azure-storage-blob-py/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "azure-storage-blob-py", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
azure-storage-blob-pyAzure 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. 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.
8 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit d5741a1. It shows what the files ask for, not the result of running them.
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.
Shell commands in SKILL.md call:
pipFrom the folder's file list and the shell code blocks in SKILL.md.
Hosts in commands or code, which the agent is likely to contact:
learn.microsoft.comFrom URLs in SKILL.md, links to its own repository left out.
Names these keys or tokens, usually read from environment variables:
AZURE_TOKEN_CREDENTIALSFrom names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from microsoft/skills at commit d5741a1, republished under its MIT licence (© microsoft). 354 words, ~2,287 tokens.
.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.Client library for Azure Blob Storage — object storage for unstructured data.
pip install azure-storage-blob azure-identityAZURE_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🔑 Two rules apply to every code sample below:
- 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:
DefaultAzureCredentialworks as-is.- Production: set
AZURE_TOKEN_CREDENTIALS=prod(orAZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.- 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:andasync with DefaultAzureCredential() as credential:(fromazure.identity.aio)Snippets may abbreviate this setup, but production code should always follow both rules.
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 | 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() |
container_client = blob_service_client.get_container_client("mycontainer")
container_client.create_container()# 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)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)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}")blob_client.delete_blob()
# Delete with snapshots
blob_client.delete_blob(delete_snapshots="include")# 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)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.
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}"# 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")
)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()azure.storage.blob sync clients with azure.storage.blob.aio async clients in the same call path. Choose one mode per module.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.DefaultAzureCredential for code that runs locally (instead of connection strings). Use a specific token credential for code that runs in Azure.overwrite=True explicitly when re-uploadingmax_concurrency for large file transfersreadinto() over readall() for memory efficiencywalk_blobs() for hierarchical listing| File | Contents |
|---|---|
| references/capabilities.md | Capability index mapping hero flows and non-hero references. |
| references/non-hero-scenarios.md | Dedicated 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
SKILL.md and 2 other files (references) in .github/plugins/azure-sdk-python/skills/azure-storage-blob-py of microsoft/skills.
Open the folder on GitHubat commit d5741a1
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Azure Storage Blob Py this skillmicrosoft/skills | 3.1k | — | ~2.3k | Automated safety check: Pass | MIT | |
| Azure Storagemicrosoft/GitHub-Copilot-for-Azure | 255 | 2 repos | ~1.3k | Automated safety check: Pass | MIT | |
| Edgeone Makers ToolsTencentEdgeOne/edgeone-makers-tools | 1.9k | 1 repos | ~646 | Automated safety check: Pass | MIT | |
| Cloud Retention Configmukul975/Privacy-Data-Protection-Skills | 301 | — | ~3.7k | Automated safety check: Pass | Apache-2.0 | |
| Azurekid-sid/claude-spellbook | 190 | — | ~3.7k | Automated safety check: Notes | MIT | |
| Google Cloud Solution Agentic Analytics Spark Knowledge Cataloggoogle/skills | 21k | — | ~4.4k | Automated safety check: Pass | Apache-2.0 |
microsoft/GitHub-Copilot-for-Azure
Azure Storage Services including Blob Storage, File Shares, Queue Storage, Table Storage, and Data Lake.
TencentEdgeOne/edgeone-makers-tools
EdgeOne Makers platform development router — the single entry point for building, storing data, and deploying on Tencent EdgeOne Makers.
mukul975/Privacy-Data-Protection-Skills
Configures cloud storage retention policies across AWS S3, Azure Blob Storage, and Google Cloud Storage.
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…
Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine).
jonathan-vella/apex
UTILITY SKILL — Azure Storage: Blob, File Shares, Queue, Table and Data Lake, including access tiers and lifecycle management.
microsoft/skills
Covers producer, consumer, and checkpoint-store setup for Azure Event Hubs streaming in Python, with Entra ID auth and partition targeting.
microsoft/skills
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.
microsoft/skills
Build dark-themed React applications using Tailwind CSS with custom theming, glassmorphism effects, and Framer Motion animations.
microsoft/skills
Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants.
microsoft/skills
Reference for building on Microsoft Foundry with the azure-ai-projects Python SDK: project clients, versioned agents, evaluations, connections, datasets and indexes.
microsoft/skills
Guide for creating effective skills for AI coding agents working with Azure SDKs and Microsoft Foundry services.
Categories
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.
Azure Storage Blob Py fits situations like: managing containers; tasks that involve File uploads and storage.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.