Official agent skill

Azure Cosmos Py

by microsoft in microsoft/skills

Azure Cosmos DB SDK for Python (NoSQL API). An agent skill from microsoft/skills.

OfficialMITAuto-check passedDatabases

Install Azure Cosmos Py

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

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

GitHub CLI
$ gh skill install microsoft/skills azure-cosmos-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-cosmos-py .claude/skills/azure-cosmos-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-cosmos-py
GitHub stars
3.1k
Token cost
~2.3k tokens
SKILL.md length
377 words
Files
3 (incl. references)
Skills in repo
150
Repo updated
First seen
Licence
MIT

At a glance

Azure Cosmos DB SDK for Python (NoSQL API). An agent skill from microsoft/skills.

  • Works in 10 steps: Pick sync OR async and stay consistent.… → Always use context managers for clients… → Use DefaultAzureCredential for portable… → …
  • Globally distributed data
  • SKILL.md covers Installation, Environment Variables, Authentication & Lifecycle and Client Hierarchy, plus 8 more sections
  • Calls pip; reaches learn.microsoft.com; needs AZURE_TOKEN_CREDENTIALS

What it does

Azure Cosmos Py is an agent skill from microsoft/skills, published by the product's own GitHub organization. Azure Cosmos DB SDK for Python (NoSQL API). Use for document CRUD, queries, containers, and globally distributed data. Triggers: "cosmos db", "CosmosClient", "container", "document", "NoSQL", "partition key".

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/partitioning.md` and `references/query-patterns.md`).

It sits in Databases, covering NoSQL databases. It works with Azure Cosmos DB, 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

  • Globally distributed data
  • Tasks that involve NoSQL databases

Example prompts

  • “cosmos db”
  • “CosmosClient”
  • “container”
  • “/azure-cosmos-py”

Requirements

  • Python 3

Workflow steps

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

  1. Pick sync OR async and stay consistent. Do not mix azure.cosmos sync clients with azure.cosmos.aio async clients in the same call path…
  2. Always use context managers for clients and async credentials. Wrap every client in with CosmosClient(...) as client: (sync) or async with…
  3. Use DefaultAzureCredential for portable auth across local dev and Azure (avoid connection strings / API keys when possible).
  4. Always specify partition key for point reads and queries
  5. Use parameterized queries to prevent injection and improve caching
  6. Avoid cross-partition queries when possible
  7. Use upsert_item for idempotent writes
  8. Use async client for high-throughput scenarios
  9. Design partition key for even data distribution
  10. Use read_item instead of query for single document retrieval

What it can do on your machine

Read from SKILL.md and the folder at commit 3898ec8. 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 Cosmos Py loads about 2.3k tokens when it runs, and up to ~7.7k if it reads all its reference files. Until then it costs about 56 tokens; SKILL.md has 377 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~56
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
~7.7k

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 3898ec8, republished under its MIT licence (© microsoft). 377 words, ~2,328 tokens.

Download SKILL.mdSave it as .claude/skills/azure-cosmos-py/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
azure-cosmos-py
description
Azure Cosmos DB SDK for Python (NoSQL API). Use for document CRUD, queries, containers, and globally distributed data. Triggers: "cosmos db", "CosmosClient", "container", "document", "NoSQL", "partition key".
license
MIT
metadata.author
Microsoft
metadata.version
1.0.0
metadata.package
azure-cosmos

Azure Cosmos DB SDK for Python

Client library for Azure Cosmos DB NoSQL API — globally distributed, multi-model database.

Installation

bash
pip install azure-cosmos azure-identity

Environment Variables

bash
COSMOS_ENDPOINT=https://<account>.documents.azure.com:443/  # Required for all auth methods
COSMOS_DATABASE=mydb  # Required for all auth methods
COSMOS_CONTAINER=mycontainer  # Required for all auth methods
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
import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.cosmos import CosmosClient

# 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()

endpoint = "https://<account>.documents.azure.com:443/"

with CosmosClient(url=endpoint, credential=credential) as client:
    # Use client here (see following sections for operations)
    ...

Client Hierarchy

ClientPurposeGet From
CosmosClientAccount-level operationsDirect instantiation
DatabaseProxyDatabase operationsclient.get_database_client()
ContainerProxyContainer/item operationsdatabase.get_container_client()

Core Workflow

Setup Database and Container
python
# Get or create database
database = client.create_database_if_not_exists(id="mydb")

# Get or create container with partition key
container = database.create_container_if_not_exists(
    id="mycontainer",
    partition_key=PartitionKey(path="/category")
)

# Get existing
database = client.get_database_client("mydb")
container = database.get_container_client("mycontainer")
Create Item
python
item = {
    "id": "item-001",           # Required: unique within partition
    "category": "electronics",   # Partition key value
    "name": "Laptop",
    "price": 999.99,
    "tags": ["computer", "portable"]
}

created = container.create_item(body=item)
print(f"Created: {created['id']}")
Read Item
python
# Read requires id AND partition key
item = container.read_item(
    item="item-001",
    partition_key="electronics"
)
print(f"Name: {item['name']}")
Update Item (Replace)
python
item = container.read_item(item="item-001", partition_key="electronics")
item["price"] = 899.99
item["on_sale"] = True

updated = container.replace_item(item=item["id"], body=item)
Upsert Item
python
# Create if not exists, replace if exists
item = {
    "id": "item-002",
    "category": "electronics",
    "name": "Tablet",
    "price": 499.99
}

result = container.upsert_item(body=item)
Delete Item
python
container.delete_item(
    item="item-001",
    partition_key="electronics"
)

Queries

Basic Query
python
# Query within a partition (efficient)
query = "SELECT * FROM c WHERE c.price < @max_price"
items = container.query_items(
    query=query,
    parameters=[{"name": "@max_price", "value": 500}],
    partition_key="electronics"
)

for item in items:
    print(f"{item['name']}: ${item['price']}")
Cross-Partition Query
python
# Cross-partition (more expensive, use sparingly)
query = "SELECT * FROM c WHERE c.price < @max_price"
items = container.query_items(
    query=query,
    parameters=[{"name": "@max_price", "value": 500}],
    enable_cross_partition_query=True
)

for item in items:
    print(item)
Query with Projection
python
query = "SELECT c.id, c.name, c.price FROM c WHERE c.category = @category"
items = container.query_items(
    query=query,
    parameters=[{"name": "@category", "value": "electronics"}],
    partition_key="electronics"
)
Read All Items
python
# Read all in a partition
items = container.read_all_items()  # Cross-partition
# Or with partition key
items = container.query_items(
    query="SELECT * FROM c",
    partition_key="electronics"
)

Partition Keys

Critical: Always include partition key for efficient operations.

python
from azure.cosmos import PartitionKey

# Single partition key
container = database.create_container_if_not_exists(
    id="orders",
    partition_key=PartitionKey(path="/customer_id")
)

# Hierarchical partition key (preview)
container = database.create_container_if_not_exists(
    id="events",
    partition_key=PartitionKey(path=["/tenant_id", "/user_id"])
)

Throughput

python
# Create container with provisioned throughput
container = database.create_container_if_not_exists(
    id="mycontainer",
    partition_key=PartitionKey(path="/pk"),
    offer_throughput=400  # RU/s
)

# Read current throughput
offer = container.read_offer()
print(f"Throughput: {offer.offer_throughput} RU/s")

# Update throughput
container.replace_throughput(throughput=1000)

Async Client

python
from azure.cosmos.aio import CosmosClient
from azure.identity.aio import DefaultAzureCredential

async def cosmos_operations():
    async with DefaultAzureCredential() as credential:
        async with CosmosClient(endpoint, credential=credential) as client:
            database = client.get_database_client("mydb")
            container = database.get_container_client("mycontainer")
            
            # Create
            await container.create_item(body={"id": "1", "pk": "test"})
            
            # Read
            item = await container.read_item(item="1", partition_key="test")
            
            # Query
            async for item in container.query_items(
                query="SELECT * FROM c",
                partition_key="test"
            ):
                print(item)

import asyncio
asyncio.run(cosmos_operations())

Error Handling

python
from azure.cosmos.exceptions import CosmosHttpResponseError

try:
    item = container.read_item(item="nonexistent", partition_key="pk")
except CosmosHttpResponseError as e:
    if e.status_code == 404:
        print("Item not found")
    elif e.status_code == 429:
        print(f"Rate limited. Retry after: {e.headers.get('x-ms-retry-after-ms')}ms")
    else:
        raise
Show full SKILL.md (182 more words)Show less

Best Practices

  1. Pick sync OR async and stay consistent. Do not mix azure.cosmos sync clients with azure.cosmos.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 CosmosClient(...) as client: (sync) or async with CosmosClient(...) 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 portable auth across local dev and Azure (avoid connection strings / API keys when possible).
  4. Always specify partition key for point reads and queries
  5. Use parameterized queries to prevent injection and improve caching
  6. Avoid cross-partition queries when possible
  7. Use upsert_item for idempotent writes
  8. Use async client for high-throughput scenarios
  9. Design partition key for even data distribution
  10. Use read_item instead of query for single document retrieval

Reference Files

FileContents
references/partitioning.mdPartition key strategies, hierarchical keys, hot partition detection and mitigation
references/query-patterns.mdQuery optimization, aggregations, pagination, transactions, change feed
scripts/setup_cosmos_container.pyCLI tool for creating containers with partitioning, throughput, and indexing

© 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-cosmos-py of microsoft/skills.

  • SKILL.md
  • references/partitioning.md
  • references/query-patterns.md

Open the folder on GitHubat commit 3898ec8

Compare with similar skills

Azure Cosmos 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 Cosmos Py compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Azure Cosmos Py this skillmicrosoft/skills3.1k—~2.3kAutomated safety check: PassMIT
Cosmosdb Best Practicesmicrosoft/vscode-cosmosdb200—~4.3kAutomated safety check: PassMIT
Azure Data Tables Pyaiskillstore/marketplace4304 repos~1.5kAutomated safety check: PassNone
Azure Storagemicrosoft/GitHub-Copilot-for-Azure2552 repos~1.3kAutomated safety check: PassMIT
Telemetry Best Practicesmicrosoft/vscode-cosmosdb200—~3.6kAutomated safety check: PassMIT
Cosmosdb Nosql Query Generationmicrosoft/vscode-cosmosdb200—~4kAutomated safety check: WarnMIT

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Categories

Questions about Azure Cosmos Py

What does Azure Cosmos Py do?

Azure Cosmos DB SDK for Python (NoSQL API). An agent skill from microsoft/skills. Azure Cosmos Py is an agent skill from microsoft/skills, published by the product's own GitHub organization. Azure Cosmos DB SDK for Python (NoSQL API).

When should I use Azure Cosmos Py?

Azure Cosmos Py fits situations like: globally distributed data; tasks that involve NoSQL databases.

How do I install Azure Cosmos Py in Claude Code?

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

How do I install Azure Cosmos Py in Codex?

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

Can I use Azure Cosmos 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-cosmos-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-cosmos-py, .gemini/skills/azure-cosmos-py, .github/skills/azure-cosmos-py and .opencode/skills/azure-cosmos-py in your project.

What does Azure Cosmos Py need to run?

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

Does Azure Cosmos 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 Cosmos 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 Cosmos Py use?

Azure Cosmos 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 Cosmos Py use?

About 2.3k tokens (SKILL.md is roughly 9.3k 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 5.3k tokens, read only when the agent opens those files.

What are the alternatives to Azure Cosmos Py?

Skills that share tags, products or a category with Azure Cosmos Py: Cosmosdb Best Practices (microsoft/vscode-cosmosdb, 200 stars), Azure Data Tables Py (aiskillstore/marketplace, 430 stars), Azure Storage (microsoft/GitHub-Copilot-for-Azure, 255 stars) and Telemetry Best Practices (microsoft/vscode-cosmosdb, 200 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Azure Cosmos Py?

microsoft (a GitHub organization, an official publisher) maintains it in microsoft/skills, which has 3,094 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.