skills/azure-ai-contentsafety-py/SKILL.md
--- name: azure-ai-contentsafety-py description: "|" 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". package: azure-ai-contentsafety risk: unknown source: rootcastle-rei --- > ⚠️ **AUTHORIZED USE ONLY** — This skill is intended for authorized security professionals only. Use onl
npx skillsauth add rootcastleco/rei-skills skills/azure-ai-contentsafety-pyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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⚠️ AUTHORIZED USE ONLY — This skill is intended for authorized security professionals only. Use only against systems you own or have explicit written permission to test. Unauthorized use may violate applicable laws.
Detect harmful user-generated and AI-generated content in applications.
pip install azure-ai-contentsafety
CONTENT_SAFETY_ENDPOINT=https://<resource>.cognitiveservices.azure.com
CONTENT_SAFETY_KEY=<your-api-key>
from azure.ai.contentsafety import ContentSafetyClient
from azure.core.credentials import AzureKeyCredential
import os
client = ContentSafetyClient(
endpoint=os.environ["CONTENT_SAFETY_ENDPOINT"],
credential=AzureKeyCredential(os.environ["CONTENT_SAFETY_KEY"])
)
from azure.ai.contentsafety import ContentSafetyClient
from azure.identity import DefaultAzureCredential
client = ContentSafetyClient(
endpoint=os.environ["CONTENT_SAFETY_ENDPOINT"],
credential=DefaultAzureCredential()
)
from azure.ai.contentsafety import ContentSafetyClient
from azure.ai.contentsafety.models import AnalyzeTextOptions, TextCategory
from azure.core.credentials import AzureKeyCredential
client = ContentSafetyClient(endpoint, AzureKeyCredential(key))
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.core.credentials import AzureKeyCredential
import base64
client = ContentSafetyClient(endpoint, AzureKeyCredential(key))
# 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.core.credentials import AzureKeyCredential
blocklist_client = BlocklistClient(endpoint, AzureKeyCredential(key))
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}")
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
)
| 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 |
This skill is applicable to execute the workflow or actions described in the overview.
🏰 Rei Skills — Curated by Rootcastle Engineering & Innovation | Batuhan Ayrıbaş
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