# azure-ai-contentsafety-py

This skill guides Python projects through Azure AI Content Safety for checking text and images. Use it when an app needs harmful-content moderation.

- skill · `type=skill` · Claude Code, GitHub Copilot · 2 installs · ★ 3149
- Author: Microsoft
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-ai-contentsafety-py/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/azure-ai-contentsafety-py

## What it does

The skill covers Azure AI Content Safety SDK for Python. With `ContentSafetyClient`, an application can send text or image data for analysis and inspect returned harm categories with severity values. Image input can come from a local file encoded as Base64 or from a URL.

The included patterns use `DefaultAzureCredential`, `analyze_text`, and `analyze_image`. They also show `BlocklistClient` for creating a text blocklist, adding terms, and testing matches during analysis. The text examples use `HATE`, `SELF_HARM`, `SEXUAL`, and `VIOLENCE` categories, with default severity values 0, 2, 4, and 6.

Install `azure-ai-contentsafety` before using the examples and set `CONTENT_SAFETY_ENDPOINT`. The legacy key path requires `CONTENT_SAFETY_KEY`; production use of `DefaultAzureCredential` sets `AZURE_TOKEN_CREDENTIALS=prod`. Without `CONTENT_SAFETY_ENDPOINT`, the client cannot be created.

## Requirements
- Works with: Azure

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add microsoft/skills --skill azure-ai-contentsafety-py -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/azure-ai-contentsafety-py/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/azure-ai-contentsafety-py/

### Codex

**Manually: put the files in place**
- File: `.agents/skills/azure-ai-contentsafety-py/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

**Manually: put the files in place**
- File: `.cursor/skills/azure-ai-contentsafety-py/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install microsoft/skills azure-ai-contentsafety-py
```

2. **Manually: put the files in place**
- File: `.github/skills/azure-ai-contentsafety-py/SKILL.md`

### Gemini CLI

**Platform CLI**
```bash
gemini skills install https://github.com/microsoft/skills --path .github/plugins/azure-sdk-python/skills/azure-ai-contentsafety-py
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/azure-ai-contentsafety-py/SKILL.md` — The legacy .windsurf/skills/ path still works

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/microsoft/skills/tree/HEAD/.github/plugins/azure-sdk-python/skills/azure-ai-contentsafety-py/SKILL.md

## When to use it
- screen user text for harmful content
- inspect images from a file or URL
- report severity for selected harm categories
- create a list of terms to block
- halt analysis when a blocklist match appears

## Example prompts
- Set up Azure AI Content Safety for this Python project and add text analysis with `ContentSafetyClient`.
- Show how to analyze `image.jpg` and print each returned category with its severity.
- Configure `BlocklistClient` for `my-blocklist`, then test text with `halt_on_blocklist_hit=True`.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 4f7d395b40e0a149.

BEGIN THIRD-PARTY FILE 4f7d395b40e0a149
````text
---
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".
license: MIT
metadata:
  author: Microsoft
  version: "1.0.0"
  package: azure-ai-contentsafety
---

# Azure AI Content Safety SDK for Python

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

## Installation

```bash
pip install azure-ai-contentsafety
```

## Environment Variables

```bash
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.

```python
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.

```python
import os
from azure.core.credentials import AzureKeyCredential
````
END THIRD-PARTY FILE 4f7d395b40e0a149

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