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 ricmmartins/azure-sre-agent-skills --skill 08-ai-foundry-posturegit clone --depth 1 https://github.com/ricmmartins/azure-sre-agent-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/ricmmartins/azure-sre-agent-skills/08-ai-foundry-posture)<a href="https://agentmods.dev/skills/ricmmartins/azure-sre-agent-skills/08-ai-foundry-posture"><img src="https://agentmods.dev/badge/skills/ricmmartins/azure-sre-agent-skills/08-ai-foundry-posture/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/ricmmartins/azure-sre-agent-skills/08-ai-foundry-posture"><img src="https://agentmods.dev/badge/skills/ricmmartins/azure-sre-agent-skills/08-ai-foundry-posture.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Tool Misuse · line 496 Tool defaults are unsafe or overly permissive (e.g. disabled TLS verification, no authentication, world-writable permissions). Unsafe defaults widen the attack surface.Fix: Override unsafe defaults with secure settings (verify=True, auth required, restrictive permissions). Review and harden all tool configurations.
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 | $0.00142 | $0.05465 |
| Opus 5 | $0.00071 | $0.02733 |
| Sonnet 5 | $0.00028 | $0.01093 |
| Haiku 4.5 | $0.00014 | $0.00547 |
Grade A, and why
ai-foundry-posture-check 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 12d 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 — 526 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Foundry & OpenAI Posture Check
Purpose
Assess the security, reliability, and cost efficiency of Azure OpenAI and Microsoft Foundry deployments. Detects the most common anti-patterns that startups make when building AI-powered products — from exposed endpoints to runaway token costs.
Based on the Azure Well-Architected Framework for AI workloads and Microsoft Foundry operational best practices.
When to use this skill
- User asks "is our OpenAI deployment secure?"
- User asks about AI cost optimization or token consumption
- Review before going to production with an AI feature
- User asks about content filtering, model versions, or rate limiting
- Periodic AI workload health check
Pre-check
Confirm with the user:
- Which Azure OpenAI / Cognitive Services accounts to assess (or "all in subscription")
- Whether they use PTU (Provisioned Throughput) or Standard deployments
- Whether they have production AI workloads already live
Assessment procedure
Step 0: Discover AI resources
az cognitiveservices account list \
--subscription <sub-id> \
--query "[?kind=='OpenAI' || kind=='AIServices'].{name:name, kind:kind, rg:resourceGroup, location:location, sku:sku.name}" \
-o table
If no results, try:
az cognitiveservices account list \
--subscription <sub-id> \
--query "[].{name:name, kind:kind, rg:resourceGroup, location:location}" \
-o table
If no Cognitive Services accounts exist, report "No Azure OpenAI or AI Foundry resources found" and end assessment.
For each account found, run the following checks:
🔐 CATEGORY 1 — Security (Critical)
Check 1.1 — Managed Identity enabled (not API keys only)
az cognitiveservices account show \
--name <account> --resource-group <rg> \
--query "{identity:identity.type, disableLocalAuth:properties.disableLocalAuth}" \
-o json
| Finding | Severity | Score |
|---|---|---|
identity.type = SystemAssigned/UserAssigned AND disableLocalAuth = true |
✅ Pass | 12 pts |
identity.type set but disableLocalAuth = false |
⚠️ Partial | 6 pts |
identity.type = None or null |
❌ Fail | 0 pts |
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.
- 12d ago First seen · 526 lines · 142 tokens per session scan A 6c109f6cdea0
ai-foundry-posture-check is a skill published in the GitHub repository ricmmartins/azure-sre-agent-skills (70 stars, last pushed 18d ago), licensed MIT. It adds 142 tokens to every session and 5,465 once invoked, about $0.0007 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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