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Compose IRQL (Incident Response Query Language) queries for Kusto cybersecurity investigations. Translates natural language hunting questions into composable IRQL pipelines using Get_*, Extract_*, and Enrich_* functions. WHEN: IRQL query, security hunt, threat hunting KQL, incident response query, compose hunting pipeline, failed logins, phishing investigation, lateral movement, process execution, file creation events.
license
MIT
metadata
{"author":"Microsoft","version":"1.2.1"}
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_*, or Enrich_* 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 | invoke to 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.
Deploying IRQL
IRQL functions are stored KQL functions (.create-or-alter function). They must already be deployed to the target database before this skill can generate pipelines.
Public example cluster (functions pre-deployed):
Cluster: https://kc7001.eastus.kusto.windows.net
Databases: ValdyTimes, JoJosHospital
To port IRQL to a new cluster/database, create Get_* selectors that project your source tables into the unified schema (column names below), then deploy extractors and enrichers. The extractors and enrichers work unchanged as long as the input schema matches.
Function Catalog
1. Selectors -- Get_*
Return projected, schema-unified views of source tables. Use the minimal form by default; use _All when extra columns are needed.
Get_Event_Authentication
| where Result == "Failed Login"
| summarize FailedCount = count() by Username
| where FailedCount > 19
| invoke Enrich_Username_Employee()
| project Username, Name, Role, Email, FailedCount
| order by FailedCount desc
Phishing triage by recipient seniority
Get_Email
| invoke Extract_Email_Sender_Domain()
| project EnvTime, EmailSender, Domain, Username = EmailRecipient, Subject, Url
| invoke Enrich_Username_Employee()
| extend Seniority = case(
Role has_any ("CEO", "Chief", "Director", "VP", "President"), 3,
Role has_any ("Manager", "Lead", "Senior"), 2,
1)
| summarize
TotalEmails = count(),
SeniorityScore = sum(Seniority),
Recipients = make_set(Name, 50),
DistinctRecipients = dcount(Username)
by Domain
| where DistinctRecipients >= 2
| order by SeniorityScore desc
| take 20
Post-exploitation pivot from an indicator
let victims =
Get_Event_FileCreation_All
| where Filename has "<INDICATOR>"
| distinct Hostname;
Get_Event_Process
| where Hostname in (victims)
| where ProcessCommandLine has_any ("rundll32", "regsvr32", "powershell", "systeminfo")
| project EnvTime, Hostname, Username, ProcessName, ProcessCommandLine
| order by EnvTime asc
Suspicious outbound traffic enriched with identity
Get_Event_NetworkOutbound
| invoke Extract_Event_Network_Domain()
| where DomainName has_any ("<SUSPICIOUS_DOMAIN_1>", "<SUSPICIOUS_DOMAIN_2>")
| invoke Enrich_Ip_Employee()
| project EnvTime, Name, Role, DomainName, Url, ClientIp
| order by EnvTime desc
External IP authentication anomaly
Get_Event_Authentication_All
| where not(ClientIp startswith "10.") and not(ClientIp startswith "192.168.")
| summarize
Attempts = count(),
Failures = countif(Result == "Failed Login"),
Users = make_set(Username)
by ClientIp
| order by Failures desc
| take 20
MCP Tools Used
Tool
Purpose
kusto_query
Execute IRQL pipelines against a Kusto database
kusto_table_schema_get
Discover available tables and columns
kusto_cluster_list
List available ADX clusters
kusto_database_list
List databases in a cluster
Opening Queries in Kusto Explorer (Windows Only)
Optional convenience feature. The default workflow is to output the KQL in chat and let the user copy it into Kusto Explorer or the VS Code Kusto extension manually. Auto-launch is opt-in only.
Always output the complete KQL query in the chat response with Step 1 (connect) and Step 2 (query) clearly labeled:
// Step 1: Connect to your cluster (skip if already connected)
// Example: uncomment to connect to the KC7 training cluster
// #connect cluster('kc7001.eastus.kusto.windows.net').database('ValdyTimes')
// Or replace with your own cluster:
// #connect cluster('<YOUR_CLUSTER>').database('<YOUR_DATABASE>')
// Step 2: Run the query below
<KQL_QUERY>
