Official agent skill

Azure Mgmt Apicenter Py

by microsoft in microsoft/skills

Azure API Center Management SDK for Python. An agent skill from microsoft/skills.

OfficialMITAuto-check passedDevOps & Cloud

Install Azure Mgmt Apicenter Py

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

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

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

At a glance

Azure API Center Management SDK for Python. An agent skill from microsoft/skills.

  • Works in 8 steps: Pick sync OR async and stay consistent.… → Always use context managers for clients… → Use workspaces to organize APIs by team… → …
  • Managing API inventory
  • SKILL.md covers Installation, Environment Variables, Authentication & Lifecycle and Create API Center, plus 13 more sections
  • Calls pip; reaches learn.microsoft.com and portal.azure.com; needs AZURE_TOKEN_CREDENTIALS

What it does

Azure Mgmt Apicenter Py is an agent skill from microsoft/skills, published by the product's own GitHub organization. Azure API Center Management SDK for Python. Use for managing API inventory, metadata, and governance across your organization. Triggers: "azure-mgmt-apicenter", "ApiCenterMgmtClient", "API Center", "API inventory", "API governance".

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/capabilities.md` and `references/non-hero-scenarios.md`).

It sits in DevOps & Cloud. It works with Microsoft Azure, Python 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

  • Managing API inventory
  • Governance across your organization

Example prompts

  • “azure-mgmt-apicenter”
  • “ApiCenterMgmtClient”
  • “API Center”
  • “/azure-mgmt-apicenter-py”

Requirements

  • Python 3

Workflow steps

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

  1. Pick sync OR async and stay consistent. Do not mix azure.xxx sync clients with azure.xxx.aio async clients in the same call path. Choose…
  2. Always use context managers for clients and async credentials. Wrap every client in with Client(...) as client: (sync) or async with…
  3. Use workspaces to organize APIs by team or domain
  4. Define metadata schemas for consistent governance
  5. Track deployments to understand where APIs are running
  6. Import specifications to enable API analysis and linting
  7. Use lifecycle stages to track API maturity
  8. Add contacts for API ownership and support

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
    • portal.azure.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 Mgmt Apicenter Py loads about 2.3k tokens when it runs, and up to ~3.9k if it reads all its reference files. Until then it costs about 64 tokens; SKILL.md has 348 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~64
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
~3.9k

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). 348 words, ~2,342 tokens.

Download SKILL.mdSave it as .claude/skills/azure-mgmt-apicenter-py/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
azure-mgmt-apicenter-py
description
Azure API Center Management SDK for Python. Use for managing API inventory, metadata, and governance across your organization. Triggers: "azure-mgmt-apicenter", "ApiCenterMgmtClient", "API Center", "API inventory", "API governance".
license
MIT
metadata.author
Microsoft
metadata.version
1.0.0
metadata.package
azure-mgmt-apicenter

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  # 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
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.mgmt.apicenter import ApiCenterMgmtClient
import os

# 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()

with ApiCenterMgmtClient(
    credential=credential,
    subscription_id=os.environ["AZURE_SUBSCRIPTION_ID"]
) as client:
    # Use `client` for all subsequent operations (see examples below)
    ...

Create API Center

python
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}")

List API Centers

python
api_centers = client.services.list_by_subscription()

for api_center in api_centers:
    print(f"{api_center.name} - {api_center.location}")

Register an API

python
from azure.mgmt.apicenter.models import Api, ApiKind, ApiProperties

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(
        properties=ApiProperties(
            title="My API",
            description="A sample API for demonstration",
            kind=ApiKind.REST,
            terms_of_service={"url": "https://example.com/terms"},
            contacts=[{"name": "API Team", "email": "api-team@example.com"}],
        )
    ),
)

print(f"Registered API: {api.properties.title}")

Create API Version

python
from azure.mgmt.apicenter.models import ApiVersion, ApiVersionProperties, 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(
        properties=ApiVersionProperties(
            title="Version 1.0",
            lifecycle_stage=LifecycleStage.PRODUCTION,
        )
    ),
)

print(f"Created version: {version.properties.title}")

Add API Definition

python
from azure.mgmt.apicenter.models import ApiDefinition, ApiDefinitionProperties

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(
        properties=ApiDefinitionProperties(
            title="OpenAPI Definition",
            description="OpenAPI 3.0 specification",
        )
    ),
)

Import API Specification

python
from azure.mgmt.apicenter.models import ApiSpecImportRequest, ApiSpecImportSourceFormat

# Import from inline content
client.api_definitions.begin_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": {}}',
    )
).result()

List APIs

python
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})")

Create Environment

python
from azure.mgmt.apicenter.models import Environment, EnvironmentKind, EnvironmentProperties

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(
        properties=EnvironmentProperties(
            title="Production",
            description="Production environment",
            kind=EnvironmentKind.PRODUCTION,
            server={"type": "Azure API Management", "management_portal_uri": ["https://portal.azure.com"]},
        )
    ),
)

Create Deployment

python
from azure.mgmt.apicenter.models import Deployment, DeploymentProperties, 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(
        properties=DeploymentProperties(
            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"]},
        )
    ),
)

Define Custom Metadata

python
from azure.mgmt.apicenter.models import MetadataSchema, MetadataSchemaProperties

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(
        properties=MetadataSchemaProperties(
            schema='{"type": "string", "title": "Data Classification", "enum": ["public", "internal", "confidential"]}'
        )
    ),
)

Client Types

ClientPurpose
ApiCenterMgmtClientMain client for all operations

Operations

Operation GroupPurpose
servicesAPI Center service management
workspacesWorkspace management
apisAPI registration and management
api_versionsAPI version management
api_definitionsAPI definition management
deploymentsDeployment tracking
environmentsEnvironment management
metadata_schemasCustom metadata definitions

Best Practices

  1. Pick sync OR async and stay consistent. Do not mix azure.xxx sync clients with azure.xxx.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 Client(...) as client: (sync) or async with Client(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.
  3. Use workspaces to organize APIs by team or domain
  4. Define metadata schemas for consistent governance
  5. Track deployments to understand where APIs are running
  6. Import specifications to enable API analysis and linting
  7. Use lifecycle stages to track API maturity
  8. Add contacts for API ownership and support

Reference Files

FileContents
references/capabilities.mdAdditional non-hero capabilities, operation-group coverage, and production checklists.
references/non-hero-scenarios.mdDedicated non-hero examples for secondary/advanced scenarios.

© 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-mgmt-apicenter-py of microsoft/skills.

  • SKILL.md
  • references/capabilities.md
  • references/non-hero-scenarios.md

Open the folder on GitHubat commit 3898ec8

Compare with similar skills

Azure Mgmt Apicenter 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 Mgmt Apicenter Py compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Azure Mgmt Apicenter Py this skillmicrosoft/skills3.1k—~2.3kAutomated safety check: PassMIT
Azure Architecture Autopilotgithub/awesome-copilot40k1 repos~1.9kAutomated safety check: PassMIT
Terraform Azurerm Set Diff Analyzergithub/awesome-copilot40k1 repos~547Automated safety check: PassMIT
Osmo Lerobot Trainingmicrosoft/physical-ai-toolchain123—~3.8kAutomated safety check: NotesMIT
Azure AI Deploytimothywarner-org/claude-code224—~731Automated safety check: NotesMIT
Apex Azure Bicep Patternsjonathan-vella/apex217—~2.5kAutomated safety check: PassMIT

Similar skills

  • Azure Architecture Autopilot

    github/awesome-copilot

    Official

    Designs Azure infrastructure from a natural-language description, or diagrams an existing resource group, then refines the design through conversation and deploys it with Bicep.

    40k GitHub starsUsed in 1 repo~1.9k tokens
    DevOps & CloudAuto-check passed
  • Official

    Analyze Terraform plan JSON output for AzureRM Provider to distinguish between false-positive diffs (order-only changes in Set-type attributes) and actual resource changes.

    40k GitHub starsUsed in 1 repo~547 tokens
    DevOps & CloudAuto-check passed
  • Osmo Lerobot Training

    microsoft/physical-ai-toolchain

    Official

    Submit, monitor, analyze, and evaluate LeRobot imitation learning training jobs on OSMO with Azure ML MLflow integration and inference evaluation - Brought to you by microsoft/physical-ai-toolchain

    123 GitHub stars~3.8k tokensUpdated yesterday
    DevOps & CloudAuto-check: notes
  • Azure AI Deploy

    timothywarner-org/claude-code

    Ship a Python generative-AI app to Azure the keyless way, using DefaultAzureCredential and azd.

    224 GitHub stars~731 tokensUpdated 2 mo ago
    DevOps & CloudAuto-check: notes
  • Apex Azure Bicep Patterns

    jonathan-vella/apex

    UTILITY SKILL — Reusable Azure Bicep patterns: hub-spoke, private endpoints, diagnostics, AVM composition.

    217 GitHub stars~2.5k tokensUpdated yesterday
    DevOps & CloudAuto-check passed
  • Python Azure Iot Edge Modules

    github/awesome-copilot

    Official

    Build and operate Python Azure IoT Edge modules with robust messaging, deployment manifests, observability, and production readiness checks.

    40k GitHub starsUsed in 1 repo~1.1k tokens
    DevOps & CloudAuto-check passed

More from microsoft/skills

All 150 skills in this repo
  • Official

    Covers producer, consumer, and checkpoint-store setup for Azure Event Hubs streaming in Python, with Entra ID auth and partition targeting.

    3.1k GitHub starsUsed in 1 repo~2.3k tokens
    Auto-check passed
  • Official

    Builds podcast-style audio narration from text with Azure OpenAI's GPT Realtime Mini over WebSocket, from a Python FastAPI backend to a React player.

    3.1k GitHub starsUsed in 1 repo~947 tokens
    Auto-check passed
  • Frontend UI Dark TS

    microsoft/skills

    Official

    Build dark-themed React applications using Tailwind CSS with custom theming, glassmorphism effects, and Framer Motion animations.

    3.1k GitHub starsUsed in 5 repos~3.6k tokens
    Auto-check passed
  • Pydantic Models Py

    microsoft/skills

    Official

    Create Pydantic models following the multi-model pattern with Base, Create, Update, Response, and InDB variants.

    3.1k GitHub starsUsed in 5 repos~496 tokens
    Auto-check passed
  • Official

    Reference for building on Microsoft Foundry with the azure-ai-projects Python SDK: project clients, versioned agents, evaluations, connections, datasets and indexes.

    3.1k GitHub stars~2.8k tokensUpdated today
    Auto-check passed
  • Skill Creator

    microsoft/skills

    Official

    Guide for creating effective skills for AI coding agents working with Azure SDKs and Microsoft Foundry services.

    3.1k GitHub starsUsed in 5 repos~17k tokens
    Auto-check passed

Categories

Questions about Azure Mgmt Apicenter Py

What does Azure Mgmt Apicenter Py do?

Azure API Center Management SDK for Python. An agent skill from microsoft/skills. Azure Mgmt Apicenter Py is an agent skill from microsoft/skills, published by the product's own GitHub organization. Azure API Center Management SDK for Python.

When should I use Azure Mgmt Apicenter Py?

Azure Mgmt Apicenter Py fits situations like: managing API inventory; governance across your organization.

How do I install Azure Mgmt Apicenter Py in Claude Code?

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

How do I install Azure Mgmt Apicenter Py in Codex?

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

Can I use Azure Mgmt Apicenter 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-mgmt-apicenter-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-mgmt-apicenter-py, .gemini/skills/azure-mgmt-apicenter-py, .github/skills/azure-mgmt-apicenter-py and .opencode/skills/azure-mgmt-apicenter-py in your project.

What does Azure Mgmt Apicenter Py need to run?

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

Does Azure Mgmt Apicenter Py access the network?

SKILL.md names 2 domains. In commands or code: learn.microsoft.com and portal.azure.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Azure Mgmt Apicenter 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 Mgmt Apicenter Py use?

Azure Mgmt Apicenter 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 Mgmt Apicenter Py use?

About 2.3k tokens (SKILL.md is roughly 9.4k 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 1.6k tokens, read only when the agent opens those files.

What are the alternatives to Azure Mgmt Apicenter Py?

Skills that share tags, products or a category with Azure Mgmt Apicenter Py: Azure Architecture Autopilot (github/awesome-copilot, 40k stars), Terraform Azurerm Set Diff Analyzer (github/awesome-copilot, 40k stars), Osmo Lerobot Training (microsoft/physical-ai-toolchain, 123 stars) and Azure AI Deploy (timothywarner-org/claude-code, 224 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Azure Mgmt Apicenter 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.