azure-cosmos-py — Claude Code skill for Azure Cosmos DB in Python
Teaches your agent the Azure Cosmos DB Python SDK: document CRUD, queries, containers, partition keys.
npx skills add microsoft/skills --skill azure-cosmos-py -a claude-codeWhat azure-cosmos-py does
Microsoft's skill covers the azure-cosmos client library for the NoSQL API. Your agent gets working patterns for creating a database and container, reading and replacing an item, upserting, deleting, and running parameterised queries with projections. The client hierarchy is spelled out: CosmosClient, DatabaseProxy, ContainerProxy.
Authentication gets its own section. The skill prefers DefaultAzureCredential, which works locally through Azure CLI and in Azure through managed identity with no code change. Connection strings and account keys are pushed aside because they skip Entra audit and rotation. In production you set AZURE_TOKEN_CREDENTIALS=prod so the credential chain stays limited to production-safe options.
Every client goes inside a context manager, sync or async, so HTTP transports and token caches close deterministically. There are also sections on throughput in RU/s and on hierarchical partition keys. Setup pulls two packages: pip install azure-cosmos azure-identity. Cross-partition queries cost more, and the examples say so.
What it helps with
- create a container with a partition key and provisioned throughput
- read an item by id and partition key value
- update fields on an existing document
- run a parameterised query with a projection
- move sync Cosmos code to the async client
Example prompts for the agent
- Show me how to create an orders container with partition key /customer_id using azure-cosmos
- Write a Cosmos DB query for items under 500 in the electronics partition
- Rewrite this snippet with the async CosmosClient and DefaultAzureCredential
How to install azure-cosmos-py
1Skills CLI (Vercel)
npx skills add microsoft/skills --skill azure-cosmos-py -a claude-code2Manually: put the files in place
~/.claude/skills/azure-cosmos-py/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/azure-cosmos-py/
Manually: put the files in place
.agents/skills/azure-cosmos-py/SKILL.mdFor all projects use ~/.agents/skills/
Manually: put the files in place
.cursor/skills/azure-cosmos-py/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install microsoft/skills azure-cosmos-py2Manually: put the files in place
.github/skills/azure-cosmos-py/SKILL.mdPlatform CLI
gemini skills install https://github.com/microsoft/skills --path .github/plugins/azure-sdk-python/skills/azure-cosmos-pyManually: put the files in place
.devin/skills/azure-cosmos-py/SKILL.mdThe legacy .windsurf/skills/ path still works
Claude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about azure-cosmos-py
How do I install azure-cosmos-py in Claude?
Run npx skills add microsoft/skills --skill azure-cosmos-py -a claude-code. The skill file then lives at ~/.claude/skills/azure-cosmos-py/SKILL.md.
What does azure-cosmos-py do?
It gives the agent a reference for the Azure Cosmos DB Python SDK: document operations, queries, containers, partition keys and throughput.
What do I need before the code samples work?
The azure-cosmos and azure-identity packages, plus COSMOS_ENDPOINT, COSMOS_DATABASE and COSMOS_CONTAINER. For production, add AZURE_TOKEN_CREDENTIALS=prod.
Source text
SKILL.md · MIT
---
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
version: "1.0.0"
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
| Client | Purpose | Get From |
|--------|---------|----------|
| `CosmosClient` | Account-level operations | Direct instantiation |
| `DatabaseProxy` | Database operations | `client.get_database_client()` |
| `ContainerProxy` | Container/item operations | `database.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
```
## 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
| File | Contents |
|------|----------|
| [references/partitioning.md](references/partitioning.md) | Partition key strategies, hierarchical keys, hot partition detection and mitigation |
| [references/query-patterns.md](references/query-patterns.md) | Query optimization, aggregations, pagination, transactions, change feed |
| [scripts/setup_cosmos_container.py](scripts/setup_cosmos_container.py) | CLI tool for creating containers with partitioning, throughput, and indexing |
© Microsoft · MIT · microsoft/skills