azure-ai-contentsafety-py

azure-ai-contentsafety-py is a skill for Claude Code from STELIORD/agentic-awesome-skills. It costs 31 tokens per session (1,345 once invoked), scanned A, a copy of azure-ai-contentsafety-py, MIT.

A Python SDK for Azure AI Content Safety, which detects harmful content in text and images and classifies it by severity.

In plain words
What is it for?
Use it to screen text and images in Python applications and apply safety checks to generated or submitted content.
Why use it?
It provides an application-level check for unsafe user-generated or AI-generated content before that content reaches other users or systems.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the agentic-awesome-skills plugin — 196 skills shipped together

Good fit Use it to screen text and images in Python applications and apply safety checks to generated or submitted content.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/steliord/agentic-awesome-skills/azure-ai-contentsafety-py
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add STELIORD/agentic-awesome-skills --skill azure-ai-contentsafety-py
Clone the repo
git clone --depth 1 https://github.com/STELIORD/agentic-awesome-skills

Made for: Claude Code.

Or install agentic-awesome-skills, the plugin that ships this one along with the rest of its 196 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for azure-ai-contentsafety-py

README.md
[![agentmods](https://agentmods.dev/badge/skills/steliord/agentic-awesome-skills/azure-ai-contentsafety-py/github.svg)](https://agentmods.dev/skills/steliord/agentic-awesome-skills/azure-ai-contentsafety-py)
Your own site
<a href="https://agentmods.dev/skills/steliord/agentic-awesome-skills/azure-ai-contentsafety-py"><img src="https://agentmods.dev/badge/skills/steliord/agentic-awesome-skills/azure-ai-contentsafety-py/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for azure-ai-contentsafety-py

Your own site · 80×15
<a href="https://agentmods.dev/skills/steliord/agentic-awesome-skills/azure-ai-contentsafety-py"><img src="https://agentmods.dev/badge/skills/steliord/agentic-awesome-skills/azure-ai-contentsafety-py.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,345 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin 83% copy Near-identical to another mod in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00031 $0.01345
Opus 5 $0.00015 $0.00673
Sonnet 5 $0.00006 $0.00269
Haiku 4.5 $0.00003 $0.00135

Measured 9d ago against content hash 1319f983bfd0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

azure-ai-contentsafety-py scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

Origin

This is a copy

83% identical to azure-ai-contentsafety-py — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/agentic-awesome-skills-claude/skills/azure-ai-contentsafety-py/SKILL.md · 223 lines

How it starts

The opening of the file, as written. The whole thing — 223 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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
CONTENT_SAFETY_KEY=<your-api-key>

Authentication

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"])
)

Entra ID

from azure.ai.contentsafety import ContentSafetyClient
from azure.identity import DefaultAzureCredential

client = ContentSafetyClient(
    endpoint=os.environ["CONTENT_SAFETY_ENDPOINT"],
    credential=DefaultAzureCredential()
)

Analyze Text

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}")

Analyze Image

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}")

Read the full file on GitHub · 223 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 9d ago First seen · 223 lines · 31 tokens per session scan A 1319f983bfd0

Subscribe to this mod's changes

azure-ai-contentsafety-py is a skill published in the GitHub repository STELIORD/agentic-awesome-skills (1 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 1,345 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 83% identical to azure-ai-contentsafety-py, differing in 11 lines, and is treated as a copy.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens