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 jpantsjoha/ai-native-developer-experience --skill azure-expertgit clone --depth 1 https://github.com/jpantsjoha/ai-native-developer-experienceWrote 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/jpantsjoha/ai-native-developer-experience/azure-expert)<a href="https://agentmods.dev/skills/jpantsjoha/ai-native-developer-experience/azure-expert"><img src="https://agentmods.dev/badge/skills/jpantsjoha/ai-native-developer-experience/azure-expert.svg" alt="Measured on agentmods" 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 | $0.00059 | $0.00985 |
| Opus 5 | $0.00030 | $0.00492 |
| Sonnet 5 | $0.00012 | $0.00197 |
| Haiku 4.5 | $0.00006 | $0.00098 |
Grade A, and why
azure-expert 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 7d 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 — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Expert
Azure is policy-first: Entra ID and Azure Policy are the guardrails; the Foundry is the agent surface. If it is not enforced by policy, it is a wish.
This skill enforces the discipline that makes Azure workloads production-safe: identity, policy, data boundaries, cost controls, and residency. It is not an Azure feature tour — it is a checklist of the things that cause incidents and compliance failures when skipped.
When to use
- Designing any Azure infrastructure (new or modified)
- Before deploying agents or LLM workloads to Azure (AI Foundry, Azure OpenAI, Azure AI Agent Service)
- When reviewing a Bicep/Terraform plan or a landing-zone design
- When a system spans subscriptions, touches regulated data, or crosses geographies
Procedure
-
Identity and access — verify least-privilege for every identity:
- Managed identities over service principals with secrets; no client secrets in code or config.
- Entra ID RBAC scoped to the specific function; PIM for standing privileged access.
- Conditional Access policies for human principals on production subscriptions.
-
Policy and landing zones — confirm governance is mechanical:
- Azure Policy assignments enforce allowed locations, required encryption, and denied public endpoints.
- Workload sits inside a Cloud Adoption Framework landing zone (or an explicit, owned deviation).
-
Data boundaries — for every data store:
- Classification recorded (Purview where in scope); CMK where required.
- Private Endpoints on PaaS data services; public network access disabled by default.
- Cross-tenant or cross-subscription sharing explicit and documented.
-
Data residency — for each resource:
- Allowed-locations policy constrains deployment geography (e.g. EU Data Boundary where required).
- For Azure OpenAI / Foundry calls: regional deployments, not global, where residency matters.
-
Cost controls — for every LLM, compute, or storage resource:
- Cost Management budgets with alerts at 50%, 75%, 90%, 100%.
- Foundry model quotas and rate limits set; autoscale maximums bounded.
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.
- 7d ago First seen · 75 lines · 59 tokens per session scan A 958226a5d7f7
azure-expert is a skill published in the GitHub repository jpantsjoha/ai-native-developer-experience (11 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 59 tokens to every session and 985 once invoked, about $0.0003 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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