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.
npx skills add vinayaklatthe/microsoft-security-skills --skill azure-ai-content-safetygit clone --depth 1 https://github.com/vinayaklatthe/microsoft-security-skillsWrote 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.
[](https://agentmods.dev/skills/vinayaklatthe/microsoft-security-skills/azure-ai-content-safety)<a href="https://agentmods.dev/skills/vinayaklatthe/microsoft-security-skills/azure-ai-content-safety"><img src="https://agentmods.dev/badge/skills/vinayaklatthe/microsoft-security-skills/azure-ai-content-safety/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.
<a href="https://agentmods.dev/skills/vinayaklatthe/microsoft-security-skills/azure-ai-content-safety"><img src="https://agentmods.dev/badge/skills/vinayaklatthe/microsoft-security-skills/azure-ai-content-safety.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00221 | $0.02046 |
| Opus 5 | $0.00111 | $0.01023 |
| Sonnet 5 | $0.00044 | $0.00409 |
| Haiku 4.5 | $0.00022 | $0.00205 |
Grade A, and why
azure-ai-content-safety 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 13d 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.
How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure AI Content Safety
Azure AI Content Safety is the platform for real-time content moderation and generative AI safety. It analyzes text, images, and multimodal content for harm categories, blocks prompt-injection / jailbreak attempts (Prompt Shields), detects ungrounded responses (Groundedness), and flags protected text/code reproduction.
It pairs with Azure OpenAI as built-in content filters (default safety net) and as configurable guardrails for any AI/LLM application (including non-Microsoft models).
When to use
Building or hardening any generative AI application — chat assistant, agent, RAG app, multimodal product — that needs guardrails on inputs and outputs.
Do not use this skill for SOC threat detection on AI workloads
(defender-for-cloud-ai), end-user AI usage governance (purview-ai-hub), or generic AI
application architecture.
Capability map
| Capability | What it does | Where to apply |
|---|---|---|
| Text moderation | Classifies text into hate, violence, sexual, self-harm (severity 0–7) | Input + output |
| Image moderation | Same categories on images | User uploads, generated images |
| Multimodal moderation | Text+image combined context | Multimodal chat |
| Prompt Shields — User Prompt | Detects jailbreak attempts in the user prompt | Input |
| Prompt Shields — Indirect | Detects injection in untrusted grounding content (RAG, tool results, web pages) | Pre-grounding step |
| Groundedness detection | Checks LLM response against grounding sources for hallucination | Output |
| Protected material — text | Detects verbatim copyrighted text | Output |
| Protected material — code | Detects reproduction of public-repo code | Code-generation output |
| Custom categories | Customer-defined harm categories (e.g., off-brand topics) | Input + output |
| Blocklists | Exact-match + regex blocklists | Input + output |
Approach
- Start with default Azure OpenAI content filters. Every Azure OpenAI deployment has a default Content Safety filter at medium severity across all four harm categories. Don't deploy without it.
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.
- 13d ago First seen · 143 lines · 221 tokens per session scan A 33473445ae0b
azure-ai-content-safety is a skill published in the GitHub repository vinayaklatthe/microsoft-security-skills (173 stars, last pushed 2mo ago), licensed MIT. It adds 221 tokens to every session and 2,046 once invoked, about $0.0011 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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