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Azure AI Contentsafety Py

MicrosoftOfficialCreated by Microsoft

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  • Tool integration
  • Software development
  • MIT
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From the author

Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification. Triggers: "azure-ai-contentsafety", "ContentSafetyClient", "content moderation", "harmful content", "text analysis", "image analysis".
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The azure-ai-contentsafety-py skill provides a Python interface for the Azure AI Content Safety service. It enables developers to analyze text and images for harmful content across categories such as hate, sexual, violence, and self-harm. The SDK supports multi-severity classification and includes functionality for managing custom text blocklists to filter specific terms.

What it does

  • Analyzes text content for harmful categories with configurable severity levels.
  • Analyzes image content provided via raw data or blob URLs.
  • Manages custom text blocklists to filter specific user-defined terms.
  • Supports both synchronous and asynchronous client implementations.
  • Provides integration with Azure identity services for secure authentication.

When to use it

  • When you need to moderate user-generated content in your application.
  • When you need to filter AI-generated outputs before displaying them to users.
  • When you require custom term filtering via blocklists.
  • When building applications on Azure that require content safety analysis.

Advantages

  • Supports multi-severity level detection for granular content filtering.
  • Includes dedicated clients for both general analysis and blocklist management.
  • Integrates with standard Azure authentication patterns.
  • Provides both sync and async client options for flexible integration.

Usage examples

“How do I analyze a string of text for harmful content using the Python SDK?”
The agent provides a code snippet using ContentSafetyClient.analyze_text, demonstrating how to iterate through categories like HATE or VIOLENCE to check severity levels.
“Show me how to create a custom blocklist for filtering terms.”
The agent provides an example using BlocklistClient.create_or_update_text_blocklist to define a new list and add specific terms to it.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/microsoft/azure-ai-contentsafety-py/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip azure-ai-contentsafety-py.zip -d .agents/skills

Documentation by Microsoft

Azure AI Content Safety SDK for Python

Detect harmful user-generated and AI-generated content in applications.

Installation

pip install azure-ai-contentsafety

Environment Variables

CONTENT_SAFETY_ENDPOINT=https://<resource>.cognitiveservices.azure.com  # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
CONTENT_SAFETY_KEY=<your-api-key>  # Only required for the legacy API-key auth path below

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.

import os
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.ai.contentsafety import ContentSafetyClient
from azure.ai.contentsafety.models import AnalyzeTextOptions

# 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 ContentSafetyClient(
    endpoint=os.environ["CONTENT_SAFETY_ENDPOINT"],
    credential=credential,
) as client:
    response = client.analyze_text(AnalyzeTextOptions(text="Hello, world!"))
Legacy: API Key (existing keyed deployments)

New code should use DefaultAzureCredential above. Use AzureKeyCredential only if you have an existing keyed deployment that hasn't been migrated to Entra ID yet — for example, regulated environments still completing their Entra rollout.

import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.contentsafety import ContentSafetyClient
from azure.ai.contentsafety.models import AnalyzeTextOptions

with ContentSafetyClient(
    endpoint=os.environ["CONTENT_SAFETY_ENDPOINT"],
    credential=AzureKeyCredential(os.environ["CONTENT_SAFETY_KEY"]),
) as client:
    response = client.analyze_text(AnalyzeTextOptions(text="Hello, world!"))

The BlocklistClient accepts the same AzureKeyCredential if you also need to manage blocklists with a key.

Analyze Text

from azure.ai.contentsafety import ContentSafetyClient
from azure.ai.contentsafety.models import AnalyzeTextOptions, TextCategory
from azure.identity import DefaultAzureCredential

with ContentSafetyClient(endpoint, DefaultAzureCredential()) as client:
    request = AnalyzeTextOptions(text="Your text content to analyze")
    response = client.analyze_text(request)

    # Check each category
    for category in [TextCategory.HATE, TextCategory.SELF_HARM, 
                     TextCategory.SEXUAL, TextCategory.VIOLENCE]:
        result = next((r for r in response.categories_analysis 
                       if r.category == category), None)
        if result:
            print(f"{category}: severity {result.severity}")

Analyze Image

from azure.ai.contentsafety import ContentSafetyClient
from azure.ai.contentsafety.models import AnalyzeImageOptions, ImageData
from azure.identity import DefaultAzureCredential
import base64

with ContentSafetyClient(endpoint, DefaultAzureCredential()) as client:
    # From file
    with open("image.jpg", "rb") as f:
        image_data = base64.b64encode(f.read()).decode("utf-8")

    request = AnalyzeImageOptions(
        image=ImageData(content=image_data)
    )

    response = client.analyze_image(request)

    for result in response.categories_analysis:
        print(f"{result.category}: severity {result.severity}")
Image from URL
from azure.ai.contentsafety.models import AnalyzeImageOptions, ImageData

request = AnalyzeImageOptions(
    image=ImageData(blob_url="https://example.com/image.jpg")
)

response = client.analyze_image(request)

Text Blocklist Management

Create Blocklist
from azure.ai.contentsafety import BlocklistClient
from azure.ai.contentsafety.models import TextBlocklist
from azure.identity import DefaultAzureCredential

with BlocklistClient(endpoint, DefaultAzureCredential()) as blocklist_client:
    blocklist = TextBlocklist(
        blocklist_name="my-blocklist",
        description="Custom terms to block"
    )

    result = blocklist_client.create_or_update_text_blocklist(
        blocklist_name="my-blocklist",
        options=blocklist
    )
Add Block Items
from azure.ai.contentsafety.models import AddOrUpdateTextBlocklistItemsOptions, TextBlocklistItem

items = AddOrUpdateTextBlocklistItemsOptions(
    blocklist_items=[
        TextBlocklistItem(text="blocked-term-1"),
        TextBlocklistItem(text="blocked-term-2")
    ]
)

result = blocklist_client.add_or_update_blocklist_items(
    blocklist_name="my-blocklist",
    options=items
)
Analyze with Blocklist
from azure.ai.contentsafety.models import AnalyzeTextOptions

request = AnalyzeTextOptions(
    text="Text containing blocked-term-1",
    blocklist_names=["my-blocklist"],
    halt_on_blocklist_hit=True
)

response = client.analyze_text(request)

if response.blocklists_match:
    for match in response.blocklists_match:
        print(f"Blocked: {match.blocklist_item_text}")

Severity Levels

Text analysis returns 4 severity levels (0, 2, 4, 6) by default. For 8 levels (0-7):

from azure.ai.contentsafety.models import AnalyzeTextOptions, AnalyzeTextOutputType

request = AnalyzeTextOptions(
    text="Your text",
    output_type=AnalyzeTextOutputType.EIGHT_SEVERITY_LEVELS
)

Harm Categories

CategoryDescription
HateAttacks based on identity (race, religion, gender, etc.)
SexualSexual content, relationships, anatomy
ViolencePhysical harm, weapons, injury
SelfHarmSelf-injury, suicide, eating disorders

Severity Scale

LevelText RangeImage RangeMeaning
0SafeSafeNo harmful content
2LowLowMild references
4MediumMediumModerate content
6HighHighSevere content

Client Types

ClientPurpose
ContentSafetyClientAnalyze text and images
BlocklistClientManage custom blocklists

Best Practices

  1. Pick sync OR async and stay consistent. Do not mix azure.ai.contentsafety sync clients with azure.ai.contentsafety.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 ContentSafetyClient(...) as client: (sync) or async with ContentSafetyClient(...) as client: (async). For async DefaultAzureCredential from azure.identity.aio, also use async with credential: so tokens and transports are cleaned up.

  3. Use blocklists for domain-specific terms

  4. Set severity thresholds appropriate for your use case

  5. Handle multiple categories — content can be harmful in multiple ways

  6. Use halt_on_blocklist_hit for immediate rejection

  7. Log analysis results for audit and improvement

  8. Consider 8-severity mode for finer-grained control

  9. Pre-moderate AI outputs before showing to users

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.

Files

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key, Needs Python

Security

What our scanner noticed· Low risk

  • Instructions that use the network

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/skills · .github/plugins/azure-sdk-python/skills/azure-ai-contentsafety-py · commit 0696600
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced

AutoGineer is not affiliated with or endorsed by Microsoft or Microsoft.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download azure-ai-contentsafety-py.zip