npx skills add ...
npx skills add microsoft/skills --skill azure-ai-contentsafety-py
npx skills add microsoft/skills --skill azure-ai-contentsafety-py
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 same skill content is published under more than one repo. The install counts are split across them; any of these commands works.
Detect harmful user-generated and AI-generated content in applications.
🔑 Two rules apply to every code sample below:
- 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:
DefaultAzureCredentialworks as-is.- Production: set
AZURE_TOKEN_CREDENTIALS=prod(orAZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.- 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:andasync with DefaultAzureCredential() as credential:(fromazure.identity.aio)Snippets may abbreviate this setup, but production code should always follow both rules.
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.
The BlocklistClient accepts the same AzureKeyCredential if you also need to manage blocklists with a key.
Text analysis returns 4 severity levels (0, 2, 4, 6) by default. For 8 levels (0-7):
| Category | Description |
|---|---|
Hate | Attacks based on identity (race, religion, gender, etc.) |
Sexual | Sexual content, relationships, anatomy |
Violence | Physical harm, weapons, injury |
SelfHarm | Self-injury, suicide, eating disorders |
| Level | Text Range | Image Range | Meaning |
|---|---|---|---|
| 0 | Safe | Safe | No harmful content |
| 2 | Low | Low | Mild references |
| 4 | Medium | Medium | Moderate content |
| 6 | High | High | Severe content |
| Client | Purpose |
|---|---|
ContentSafetyClient | Analyze text and images |
BlocklistClient | Manage custom blocklists |
azure.ai.contentsafety sync clients with azure.ai.contentsafety.aio async clients in the same call path. Choose one mode per module.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.| File | Contents |
|---|---|
| references/capabilities.md | Additional non-hero capabilities, operation-group coverage, and production checklists. |
| references/non-hero-scenarios.md | Dedicated non-hero examples for secondary/advanced scenarios. |
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!"))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!"))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}")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}")from azure.ai.contentsafety.models import AnalyzeImageOptions, ImageData
request = AnalyzeImageOptions(
image=ImageData(blob_url="https://example.com/image.jpg")
)
response = client.analyze_image(request)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
)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
)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}")from azure.ai.contentsafety.models import AnalyzeTextOptions, AnalyzeTextOutputType
request = AnalyzeTextOptions(
text="Your text",
output_type=AnalyzeTextOutputType.EIGHT_SEVERITY_LEVELS
)