Azure Developer CLI (azd) is a command-line tool for building, deploying, and operating applications on Azure. Developers use it to move through application workflows such as coding, building, deployment, and monitoring with Azure templates and resources. Its catalogue entries help AI agents work with azd and Azure application projects.
Borrowing it
Nothing to install: this file belongs to Azure/azure-dev. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Azure/azure-dev/main/.github/skills/sensei/SKILL.mdgit clone --depth 1 https://github.com/Azure/azure-devWrote 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/azure/azure-dev/sensei)<a href="https://agentmods.dev/skills/azure/azure-dev/sensei"><img src="https://agentmods.dev/badge/skills/azure/azure-dev/sensei/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/azure/azure-dev/sensei"><img src="https://agentmods.dev/badge/skills/azure/azure-dev/sensei.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00140 | $0.00340 |
| Opus 5.5 | $0.00056 | $0.00136 |
| Sonnet 5.5 | $0.00028 | $0.00068 |
| Haiku 4.5 | $0.00014 | $0.00034 |
Grade A, and why
sensei 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 1mo 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.
What it actually says
sensei
Evaluates and improves agent skill quality using waza.
Overview
Sensei automates the evaluation and improvement of agent skills in this repository. It uses waza to score compliance, run eval suites, and iteratively improve skills until they meet quality targets.
Prerequisites
- waza:
go install github.com/microsoft/waza/cmd/waza@latestorazd ext install microsoft.azd.waza
{{ references/workflow.md }}
Exit Criteria
- All target skills have been checked and optionally improved
- Compliance scores and eval results are reported
- Changes saved to disk (user decides when to commit)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 43 lines · 140 tokens per session scan A a7a88d36945d
sensei is a skill published in the GitHub repository Azure/azure-dev (570 stars, last pushed today), licensed MIT. It adds 140 tokens to every session and 340 once invoked, about $0.0006 per session on Opus 5.5. 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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