Files
antigravity-skills-reference/skills/azure-cosmos-py/SKILL.md
Ares 4a5f1234bb fix: harden registry tooling, make tests hermetic, and restore metadata consistency (#168)
* chore: upgrade maintenance scripts to robust PyYAML parsing

- Replaces fragile regex frontmatter parsing with PyYAML/yaml library
- Ensures multi-line descriptions and complex characters are handled safely
- Normalizes quoting and field ordering across all maintenance scripts
- Updates validator to strictly enforce description quality

* fix: restore and refine truncated skill descriptions

- Recovered 223+ truncated descriptions from git history (6.5.0 regression)
- Refined long descriptions into concise, complete sentences (<200 chars)
- Added missing descriptions for brainstorming and orchestration skills
- Manually fixed imagen skill description
- Resolved dangling links in competitor-alternatives skill

* chore: sync generated registry files and document fixes

- Regenerated skills index with normalized forward-slash paths
- Updated README and CATALOG to reflect restored descriptions
- Documented restoration and script improvements in CHANGELOG.md

* fix: restore missing skill and align metadata for full 955 count

- Renamed SKILL.MD to SKILL.md in andruia-skill-smith to ensure indexing
- Fixed risk level and missing section in andruia-skill-smith
- Synchronized all registry files for final 955 skill count

* chore(scripts): add cross-platform runners and hermetic test orchestration

* fix(scripts): harden utf-8 output and clone target writeability

* fix(skills): add missing date metadata for strict validation

* chore(index): sync generated metadata dates

* fix(catalog): normalize skill paths to prevent CI drift

* chore: sync generated registry files

* fix: enforce LF line endings for generated registry files
2026-03-01 09:38:25 +01:00

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Markdown

---
name: azure-cosmos-py
description: Azure Cosmos DB SDK for Python (NoSQL API). Use for document CRUD, queries, containers, and globally distributed data.
risk: unknown
source: community
date_added: '2026-02-27'
---
# 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/
COSMOS_DATABASE=mydb
COSMOS_CONTAINER=mycontainer
```
## Authentication
```python
from azure.identity import DefaultAzureCredential
from azure.cosmos import CosmosClient
credential = DefaultAzureCredential()
endpoint = "https://<account>.documents.azure.com:443/"
client = CosmosClient(url=endpoint, credential=credential)
```
## 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():
credential = DefaultAzureCredential()
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. **Always specify partition key** for point reads and queries
2. **Use parameterized queries** to prevent injection and improve caching
3. **Avoid cross-partition queries** when possible
4. **Use `upsert_item`** for idempotent writes
5. **Use async client** for high-throughput scenarios
6. **Design partition key** for even data distribution
7. **Use `read_item`** instead of query for single document retrieval
## Reference Files
| File | Contents |
|------|----------|
| references/partitioning.md | Partition key strategies, hierarchical keys, hot partition detection and mitigation |
| references/query-patterns.md | Query optimization, aggregations, pagination, transactions, change feed |
| scripts/setup_cosmos_container.py | CLI tool for creating containers with partitioning, throughput, and indexing |
## When to Use
This skill is applicable to execute the workflow or actions described in the overview.