skills/codex/azure-mgmt-apicenter-py/SKILL.md
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: azure-mgmt-apicenter-py description: "Azure API Center Management SDK for Python" --- # Azure API Center Management SDK for Python Manage API inventory, metadata, and governance in Azure API Center. ## Installation ```bash pip install azure-mgmt-apicenter pip install azure-identity ``` ## Environment Variables ```bash AZURE_SUBSCRIPTION_ID=your-subscription-id ``` ## Authentication ```python from azure.identity import De
npx skillsauth add frank-luongt/faos-skills-marketplace skills/codex/azure-mgmt-apicenter-pyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Manage API inventory, metadata, and governance in Azure API Center.
pip install azure-mgmt-apicenter
pip install azure-identity
AZURE_SUBSCRIPTION_ID=your-subscription-id
from azure.identity import DefaultAzureCredential
from azure.mgmt.apicenter import ApiCenterMgmtClient
import os
client = ApiCenterMgmtClient(
credential=DefaultAzureCredential(),
subscription_id=os.environ["AZURE_SUBSCRIPTION_ID"]
)
from azure.mgmt.apicenter.models import Service
api_center = client.services.create_or_update(
resource_group_name="my-resource-group",
service_name="my-api-center",
resource=Service(
location="eastus",
tags={"environment": "production"}
)
)
print(f"Created API Center: {api_center.name}")
api_centers = client.services.list_by_subscription()
for api_center in api_centers:
print(f"{api_center.name} - {api_center.location}")
from azure.mgmt.apicenter.models import Api, ApiKind, LifecycleStage
api = client.apis.create_or_update(
resource_group_name="my-resource-group",
service_name="my-api-center",
workspace_name="default",
api_name="my-api",
resource=Api(
title="My API",
description="A sample API for demonstration",
kind=ApiKind.REST,
lifecycle_stage=LifecycleStage.PRODUCTION,
terms_of_service={"url": "https://example.com/terms"},
contacts=[{"name": "API Team", "email": "[email protected]"}]
)
)
print(f"Registered API: {api.title}")
from azure.mgmt.apicenter.models import ApiVersion, LifecycleStage
version = client.api_versions.create_or_update(
resource_group_name="my-resource-group",
service_name="my-api-center",
workspace_name="default",
api_name="my-api",
version_name="v1",
resource=ApiVersion(
title="Version 1.0",
lifecycle_stage=LifecycleStage.PRODUCTION
)
)
print(f"Created version: {version.title}")
from azure.mgmt.apicenter.models import ApiDefinition
definition = client.api_definitions.create_or_update(
resource_group_name="my-resource-group",
service_name="my-api-center",
workspace_name="default",
api_name="my-api",
version_name="v1",
definition_name="openapi",
resource=ApiDefinition(
title="OpenAPI Definition",
description="OpenAPI 3.0 specification"
)
)
from azure.mgmt.apicenter.models import ApiSpecImportRequest, ApiSpecImportSourceFormat
# Import from inline content
client.api_definitions.import_specification(
resource_group_name="my-resource-group",
service_name="my-api-center",
workspace_name="default",
api_name="my-api",
version_name="v1",
definition_name="openapi",
body=ApiSpecImportRequest(
format=ApiSpecImportSourceFormat.INLINE,
value='{"openapi": "3.0.0", "info": {"title": "My API", "version": "1.0"}, "paths": {}}'
)
)
apis = client.apis.list(
resource_group_name="my-resource-group",
service_name="my-api-center",
workspace_name="default"
)
for api in apis:
print(f"{api.name}: {api.title} ({api.kind})")
from azure.mgmt.apicenter.models import Environment, EnvironmentKind
environment = client.environments.create_or_update(
resource_group_name="my-resource-group",
service_name="my-api-center",
workspace_name="default",
environment_name="production",
resource=Environment(
title="Production",
description="Production environment",
kind=EnvironmentKind.PRODUCTION,
server={"type": "Azure API Management", "management_portal_uri": ["https://portal.azure.com"]}
)
)
from azure.mgmt.apicenter.models import Deployment, DeploymentState
deployment = client.deployments.create_or_update(
resource_group_name="my-resource-group",
service_name="my-api-center",
workspace_name="default",
api_name="my-api",
deployment_name="prod-deployment",
resource=Deployment(
title="Production Deployment",
description="Deployed to production APIM",
environment_id="/workspaces/default/environments/production",
definition_id="/workspaces/default/apis/my-api/versions/v1/definitions/openapi",
state=DeploymentState.ACTIVE,
server={"runtime_uri": ["https://api.example.com"]}
)
)
from azure.mgmt.apicenter.models import MetadataSchema
metadata = client.metadata_schemas.create_or_update(
resource_group_name="my-resource-group",
service_name="my-api-center",
metadata_schema_name="data-classification",
resource=MetadataSchema(
schema='{"type": "string", "title": "Data Classification", "enum": ["public", "internal", "confidential"]}'
)
)
| Client | Purpose |
|--------|---------|
| ApiCenterMgmtClient | Main client for all operations |
| Operation Group | Purpose |
|----------------|---------|
| services | API Center service management |
| workspaces | Workspace management |
| apis | API registration and management |
| api_versions | API version management |
| api_definitions | API definition management |
| deployments | Deployment tracking |
| environments | Environment management |
| metadata_schemas | Custom metadata definitions |
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-mlflow-evaluation --- # MLflow 3 GenAI Evaluation ## Before Writing Any Code 1. **Read GOTCHAS.md** - 15+ common mistakes that cause failures 2. **Read CRITICAL-interfaces.md** - Exact API signatures and data schemas ## End-to-End Workflows Follow these workflows based on your goal. Each step indicates which reference files to read. ### Workflow 1: First-Time Evaluation Setup For users new to MLflow GenAI evalu
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-lakebase-provisioned --- # Lakebase Provisioned Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads. ## When to Use Use this skill when: - Building applications that need a PostgreSQL database for transactional workloads - Adding persistent state to Databricks Apps - Implementing reverse ETL from Delta Lake to an operational database - Storing chat/agent m
tools
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-jobs --- # Databricks Lakeflow Jobs ## Overview Databricks Jobs orchestrate data workflows with multi-task DAGs, flexible triggers, and comprehensive monitoring. Jobs support diverse task types and can be managed via Python SDK, CLI, or Asset Bundles. ## Reference Files | Use Case | Reference File | | ----------------------
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-genie --- # Databricks Genie Create and query Databricks Genie Spaces - natural language interfaces for SQL-based data exploration. ## Overview Genie Spaces allow users to ask natural language questions about structured data in Unity Catalog. The system translates questions into SQL queries, executes them on a SQL warehouse, and presents results conversationally. ## When to Use This Skill Use this skill when: -