Azure Skills Plugin is an agent plugin that packages Azure-specific guidance with MCP server configurations for carrying out Azure work. Coding agents use it to plan, deploy, troubleshoot, monitor, govern, and optimize Azure applications and services. The catalogue entries are the plugin's skills, instructions, and execution integrations.
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 microsoft/azure-skills --skill azure-kusto-irqlgit clone --depth 1 https://github.com/microsoft/azure-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/microsoft/azure-skills/azure-kusto-irql)<a href="https://agentmods.dev/skills/microsoft/azure-skills/azure-kusto-irql"><img src="https://agentmods.dev/badge/skills/microsoft/azure-skills/azure-kusto-irql/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/microsoft/azure-skills/azure-kusto-irql"><img src="https://agentmods.dev/badge/skills/microsoft/azure-skills/azure-kusto-irql.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk 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 | $0.00086 | $0.02686 |
| Opus 5 | $0.00043 | $0.01343 |
| Sonnet 5 | $0.00017 | $0.00537 |
| Haiku 4.5 | $0.00009 | $0.00269 |
Grade A, and why
azure-kusto-irql 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 11d 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.
This is a copy
100% identical to azure-kusto-irql — 0 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.
How it starts
The opening of the file, as written. The whole thing — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IRQL -- Incident Response Query Language
Compose IRQL function pipelines from selector, extractor, and enricher building blocks. IRQL wraps raw KQL security tables behind intent-revealing, composable functions so analysts (and LLMs) can express hunts without memorizing schemas, cluster locations, or join keys.
Activation Triggers
Use this skill when the user:
- Explicitly mentions IRQL,
Get_*,Extract_*, orEnrich_*functions - Says "use IRQL" or "write an IRQL query"
- Requests a composable hunting pipeline using known IRQL selectors
Do not activate for generic security queries (e.g. "find failed logins") unless the user explicitly asks for IRQL. Route those to azure-kusto instead.
Not a natural-language-to-IRQL converter. This skill composes IRQL function pipelines and may handle basic natural-language requests that map directly to known selectors and simple filters. For general NL-to-KQL or NL-to-IRQL conversion, use a dedicated query-generation skill (available separately).
IRQL Function Preflight
Before generating a pipeline, verify IRQL is available on the target database:
.show functions
| where Name startswith "Get_" or Name startswith "Extract_" or Name startswith "Enrich_"
| project Name
If no IRQL functions are found, inform the user that IRQL is not deployed on the target database and suggest using azure-kusto for raw KQL queries instead. IRQL functions are a prerequisite -- this skill does not deploy base IRQL selectors.
What IRQL Is
IRQL is a function-based dialect on top of KQL. It provides:
- Unified schema -- disparate security tables project into consistent column names regardless of the underlying data source
- Composability -- small functions chain via
| invoketo build complex hunts from simple steps - Portability -- the same IRQL pipeline works across different clusters/databases; only the
Get_*primitives need re-pointing
IRQL is not a separate language. It's KQL functions you invoke. Any valid KQL works alongside IRQL functions.
What ships with it
2 files 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.
- 11d ago First seen · 241 lines · 86 tokens per session scan A 3708f24ab3a0
azure-kusto-irql is a skill published in the GitHub repository microsoft/azure-skills (1,473 stars, last pushed today), licensed MIT. It adds 86 tokens to every session and 2,686 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to azure-kusto-irql, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
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…
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…
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…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…