
Azure Cosmos DB Python
FreeEasily implement production-grade Cosmos DB services.
Free · Opens the source repo
What Azure Cosmos DB Python does
The Azure Cosmos DB Python skill provides a structured approach to building production-grade NoSQL services using Azure Cosmos DB. This skill emphasizes clean code principles, security best practices, and test-driven development (TDD) methodologies. It is designed for developers who want to efficiently integrate Azure Cosmos DB into their applications while ensuring maintainable and secure code.
With this skill, you will learn how to set up a Cosmos DB client using both the Azure DefaultAzureCredential for production and a local emulator for development. The architecture includes a FastAPI router for handling requests, a service layer for business logic, and a client module for Cosmos DB interactions. This separation of concerns promotes a clean and organized codebase, making it easier to manage and scale your application.
The skill also outlines essential security requirements, such as using role-based access control (RBAC) and parameterized queries to prevent SQL injection. Additionally, it incorporates clean code conventions, ensuring that your implementation adheres to best practices for readability and maintainability. By following the TDD requirements, you can ensure that your code is reliable and well-tested before deployment.
Overall, this skill is ideal for developers looking to implement Azure Cosmos DB services in their applications while adhering to best practices in security, code quality, and testing.
When to use it
Use this skill when you need to integrate Azure Cosmos DB into your application while following best practices for security and clean code.
When not to use it
This skill may not be suitable for projects that do not require Azure Cosmos DB or for those preferring a different NoSQL solution.
What you can build with it
Integrating Cosmos DB in a Web Application
Use this skill to set up a robust backend for your web application, ensuring secure and efficient data management.
Local Development with Emulator
Quickly prototype your application using the Cosmos DB emulator, allowing for fast iterations without incurring costs.
Implementing Security Best Practices
Follow the security guidelines provided in the skill to protect your data and maintain compliance with industry standards.
How to install Azure Cosmos DB Python
View source1. Install with the skills CLI
npx skills add sickn33/agentic-awesome-skills/azure-cosmos-db-py --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by sickn33Cosmos DB Service Implementation
Build production-grade Azure Cosmos DB NoSQL services following clean code, security best practices, and TDD principles.
Installation
pip install azure-cosmos azure-identity
Environment Variables
COSMOS_ENDPOINT=https://<account>.documents.azure.com:443/
COSMOS_DATABASE_NAME=<database-name>
COSMOS_CONTAINER_ID=<container-id>
# For emulator only (not production)
COSMOS_KEY=<emulator-key>
Authentication
DefaultAzureCredential (preferred):
from azure.cosmos import CosmosClient
from azure.identity import DefaultAzureCredential
client = CosmosClient(
url=os.environ["COSMOS_ENDPOINT"],
credential=DefaultAzureCredential()
)
Emulator (local development):
from azure.cosmos import CosmosClient
client = CosmosClient(
url="https://localhost:8081",
credential=os.environ["COSMOS_KEY"],
connection_verify=False
)
Architecture Overview
┌─────────────────────────────────────────────────────────────────┐
│ FastAPI Router │
│ - Auth dependencies (get_current_user, get_current_user_required)
│ - HTTP error responses (HTTPException) │
└──────────────────────────────┬──────────────────────────────────┘
│
┌──────────────────────────────▼──────────────────────────────────┐
│ Service Layer │
│ - Business logic and validation │
│ - Document ↔ Model conversion │
│ - Graceful degradation when Cosmos unavailable │
└──────────────────────────────┬──────────────────────────────────┘
│
┌──────────────────────────────▼──────────────────────────────────┐
│ Cosmos DB Client Module │
│ - Singleton container initialization │
│ - Dual auth: DefaultAzureCredential (Azure) / Key (emulator) │
│ - Async wrapper via run_in_threadpool │
└─────────────────────────────────────────────────────────────────┘
Quick Start
1. Client Module Setup
Create a singleton Cosmos client with dual authentication:
# db/cosmos.py
from azure.cosmos import CosmosClient
from azure.identity import DefaultAzureCredential
from starlette.concurrency import run_in_threadpool
_cosmos_container = None
def _is_emulator_endpoint(endpoint: str) -> bool:
return "localhost" in endpoint or "127.0.0.1" in endpoint
async def get_container():
global _cosmos_container
if _cosmos_container is None:
if _is_emulator_endpoint(settings.cosmos_endpoint):
client = CosmosClient(
url=settings.cosmos_endpoint,
credential=settings.cosmos_key,
connection_verify=False
)
else:
client = CosmosClient(
url=settings.cosmos_endpoint,
credential=DefaultAzureCredential()
)
db = client.get_database_client(settings.cosmos_database_name)
_cosmos_container = db.get_container_client(settings.cosmos_container_id)
return _cosmos_container
Full implementation: See references/client-setup.md
2. Pydantic Model Hierarchy
Use five-tier model pattern for clean separation:
class ProjectBase(BaseModel): # Shared fields
name: str = Field(..., min_length=1, max_length=200)
class ProjectCreate(ProjectBase): # Creation request
workspace_id: str = Field(..., alias="workspaceId")
class ProjectUpdate(BaseModel): # Partial updates (all optional)
name: Optional[str] = Field(None, min_length=1)
class Project(ProjectBase): # API response
id: str
created_at: datetime = Field(..., alias="createdAt")
class ProjectInDB(Project): # Internal with docType
doc_type: str = "project"
3. Service Layer Pattern
class ProjectService:
def _use_cosmos(self) -> bool:
return get_container() is not None
async def get_by_id(self, project_id: str, workspace_id: str) -> Project | None:
if not self._use_cosmos():
return None
doc = await get_document(project_id, partition_key=workspace_id)
if doc is None:
return None
return self._doc_to_model(doc)
Full patterns: See references/service-layer.md
Core Principles
Security Requirements
- RBAC Authentication: Use
DefaultAzureCredentialin Azure — never store keys in code - Emulator-Only Keys: Hardcode the well-known emulator key only for local development
- Parameterized Queries: Always use
@parametersyntax — never string concatenation - Partition Key Validation: Validate partition key access matches user authorization
Clean Code Conventions
- Single Responsibility: Client module handles connection; services handle business logic
- Graceful Degradation: Services return
None/[]when Cosmos unavailable - Consistent Naming:
_doc_to_model(),_model_to_doc(),_use_cosmos() - Type Hints: Full typing on all public methods
- CamelCase Aliases: Use
Field(alias="camelCase")for JSON serialization
TDD Requirements
Write tests BEFORE implementation using these patterns:
@pytest.fixture
def mock_cosmos_container(mocker):
container = mocker.MagicMock()
mocker.patch("app.db.cosmos.get_container", return_value=container)
return container
@pytest.mark.asyncio
async def test_get_project_by_id_returns_project(mock_cosmos_container):
# Arrange
mock_cosmos_container.read_item.return_value = {"id": "123", "name": "Test"}
# Act
result = await project_service.get_by_id("123", "workspace-1")
# Assert
assert result.id == "123"
assert result.name == "Test"
Full testing guide: See references/testing.md
Reference Files
| File | When to Read |
|---|---|
| references/client-setup.md | Setting up Cosmos client with dual auth, SSL config, singleton pattern |
| references/service-layer.md | Implementing full service class with CRUD, conversions, graceful degradation |
| references/testing.md | Writing pytest tests, mocking Cosmos, integration test setup |
| references/partitioning.md | Choosing partition keys, cross-partition queries, move operations |
| references/error-handling.md | Handling CosmosResourceNotFoundError, logging, HTTP error mapping |
Template Files
| File | Purpose |
|---|---|
| assets/cosmos_client_template.py | Ready-to-use client module |
| assets/service_template.py | Service class skeleton |
| assets/conftest_template.py | pytest fixtures for Cosmos mocking |
Quality Attributes (NFRs)
Reliability
- Graceful degradation when Cosmos unavailable
- Retry logic with exponential backoff for transient failures
- Connection pooling via singleton pattern
Security
- Zero secrets in code (RBAC via DefaultAzureCredential)
- Parameterized queries prevent injection
- Partition key isolation enforces data boundaries
Maintainability
- Five-tier model pattern enables schema evolution
- Service layer decouples business logic from storage
- Consistent patterns across all entity services
Testability
- Dependency injection via
get_container() - Easy mocking with module-level globals
- Clear separation enables unit testing without Cosmos
Performance
- Partition key queries avoid cross-partition scans
- Async wrapping prevents blocking FastAPI event loop
- Minimal document conversion overhead
When to Use
This skill is applicable to execute the workflow or actions described in the overview.
Limitations
- Use this skill only when the task clearly matches the scope described above.
- Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
- Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.
Frequently asked questions about Azure Cosmos DB Python
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