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Azure Kusto Irql Graph

MicrosoftOfficialCreated by Microsoft

  • Skill
  • Tool integration
  • Data and AI
  • MIT
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From the author

Apply IRQL graph functions to KQL or IRQL query results for Kusto Explorer visualization. Generates Lift_To_Graph mappings and composes Graph_Render_View, Graph_Fold_By_Property, Extract_Node_*, Enrich_Node_*, and Enrich_Graph_* calls. Accepts a supplied query or limited basic natural-language source request; it is not a general natural-language-to-KQL/IRQL skill. WHEN: Lift_To_Graph, Graph_Render_View, Graph_Fold_By_Property, IRQL graph enrichment, graph mapping for existing query results, icon-decorated graph, fold graph nodes. Use azure-kusto-graph for native make-graph analysis, graph-match, shortest paths, components, or persistent graphs.
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The azure-kusto-irql-graph skill enables users to visualize tabular data from Kusto or IRQL queries as interactive graphs within Kusto Explorer. It works by applying a series of stored functions—specifically Lift_To_Graph, Graph_Render_View, and Graph_Fold_By_Property—to transform query results into node and edge structures. The skill generates the necessary JSON mapping based on the user's description of entities and relationships, ensuring that data is correctly projected for graph rendering. It is designed to complement native graph analysis tools by focusing on the visualization and enrichment of existing query outputs.

What it does

  • Generates Lift_To_Graph mappings from tabular KQL/IRQL results.
  • Composes Graph_Render_View calls to trigger Kusto Explorer graph windows.
  • Applies Graph_Fold_By_Property to collapse nodes based on shared attributes.
  • Integrates Extract_Node_* and Enrich_Node_* functions for data augmentation.
  • Maps query columns to graph entities, properties, and visual icons.
  • Validates the presence of required stored functions before pipeline execution.

When to use it

  • When you have tabular KQL/IRQL results and need to visualize them as a graph.
  • When you want to fold or collapse graph nodes by a specific property.
  • When you need to enrich graph nodes with additional context like employee or domain info.
  • When you need to apply icon-decorated mappings to existing query data.
  • When you need to visualize relationships between entities like users, hosts, IPs, or processes.

Advantages

  • Provides a structured way to turn flat query results into visual graphs.
  • Automates the creation of complex JSON mapping strings for graph construction.
  • Integrates seamlessly with existing Kusto Explorer workflows.
  • Supports node folding and enrichment for cleaner, more insightful visualizations.
  • Maintains compatibility with standard KQL/IRQL query pipelines.

Usage examples

“Given Get_Event_Authentication_All | take 200, create a graph showing users authenticating to hosts.”
The agent generates a KQL pipeline using Lift_To_Graph with a user-host mapping and renders the result in Kusto Explorer.
“Given Get_Email_All | take 400, graph emails and collapse messages by verdict.”
The agent creates a mapping for email flow and appends the Graph_Fold_By_Property function to group messages by their verdict column.
“Graph outbound network connections from IPs to domains and label IPs with employee names.”
The agent builds a pipeline that lifts network data to a graph and applies the Enrich_Node_Ip_Employee function before rendering.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/microsoft/azure-kusto-irql-graph/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip azure-kusto-irql-graph.zip -d .agents/skills

Documentation by Microsoft

IRQL Graph Functions -- Query Results to Visualization

Apply the IRQL graph function family to tabular results. Given a KQL or IRQL query and the user's graph description, generate a Lift_To_Graph mapping and compose only the stored graph functions needed to visualize, fold, extract, or enrich the graph in Kusto Explorer. The source query does not need to use IRQL.

Scope and Routing

RequestUse
Turn supplied KQL/IRQL rows into an icon-decorated visual graphThis skill: Lift_To_Graph + Graph_Render_View
Fold nodes or apply Extract_Node_*, Enrich_Node_*, or Enrich_Graph_*This skill
Use make-graph, graph-match, shortest paths, connected components, graph models, or snapshotsazure-kusto-graph
Author a non-trivial KQL/IRQL investigation from natural languageA Kusto or IRQL query-generation skill, then this skill

If a request mixes visualization and native graph analysis, use this skill for the lift/render portion and azure-kusto-graph for operator semantics. Do not replace graph-lift functions with a hand-built edges-first graph unless the user asks for native graph operators.

Input Contract

  • Preferred input: a working KQL/IRQL query that produces tabular results, plus a natural-language description of the desired nodes, edges, labels, icons, extracts, enrichments, or folds.

  • This skill is not a natural-language-to-KQL or NL-to-IRQL converter. It transforms existing query results into graph visualizations. For general NL-to-KQL or NL-to-IRQL conversion, use a dedicated query-generation skill (available separately).

  • Preserve the supplied query's retrieval, joins, filters, and aggregations. Add only projections or synthetic IDs required by the graph mapping.

  • A basic natural-language source request is supported only when it maps directly to one known table or IRQL Get_* selector with obvious columns and simple filters. State the assumed source, and do not invent joins, schema, or investigation logic.

  • For non-trivial query construction, use a separate Kusto/IRQL query-generation skill first, then apply this skill to its output.

  • If no query or output schema is available and the source is not trivial, request the KQL query or its result columns before generating a mapping.

Activation Triggers

Use this skill when the user:

  • Supplies KQL/IRQL results and asks for an IRQL graph visualization or mapping

  • Mentions Lift_To_Graph, Graph_Render_View, or Graph_Fold_By_Property

  • Asks for icon-decorated node/edge mappings in Kusto Explorer

  • Wants to fold/collapse nodes by a shared property

  • Requests graph extraction or enrichment through Extract_Node_*, Enrich_Node_*, or Enrich_Graph_*

Do not activate this skill solely for graph-match, graph paths/components, persistent graphs, or generic make-graph construction; those belong to azure-kusto-graph.

Not a natural-language-to-KQL/IRQL converter. The input should generally be a working KQL or IRQL query whose results need graph visualization. Basic NL source requests work only for trivial single-table/selector cases. For general NL-to-KQL or NL-to-IRQL, use a dedicated query-generation skill (available separately).

Environment

  • Cluster: https://kc7001.eastus.kusto.windows.net

  • Databases: ValdyTimes, JoJosHospital (graph functions pre-deployed)

  • Rendering: Kusto Explorer desktop app (make-graph visualization window)

  • Tool: kusto_query (via Azure MCP Server)

Function Preflight

Lift_To_Graph and Graph_Render_View are stored functions, not built-in Kusto operators. Before generating or running a lift pipeline against a target database, check what is deployed:

.show functions
| where Name in~ ("Lift_To_Graph", "Graph_Render_View", "Graph_Fold_By_Property")
| project Name
  • Lift_To_Graph and Graph_Render_View are required.

  • Graph_Fold_By_Property is required only when folding is requested.

  • Check any Extract_Node_*, Enrich_Node_*, or Enrich_Graph_* function before using it; omit optional enrichment when unavailable unless the user wants it deployed.

  • If a required function is missing and you have permission to alter the database, ask the user for confirmation before deploying. Then use the .create-or-alter function definitions in references/DEPLOY_IRQL_FUNCTIONS.md. Run the relevant .create-or-alter block, then rerun the preflight check to confirm.

  • If you do not have alter permissions, tell the user which functions are missing and point them to references/DEPLOY_IRQL_FUNCTIONS.md for manual deployment.

IRQL Graph Function Family

Lift_To_Graph(T, mappingJson)

Transforms any tabular KQL result into a unified node + edge table.

Input: Any table T + a JSON mapping string. Output: Rows with EntityType = "node" or "edge", ready for make-graph.

Graph_Render_View(T)

Takes Lift_To_Graph output, splits nodes/edges, and calls make-graph to open Kusto Explorer's graph window.

Graph_Fold_By_Property(T, NodeType, PropertyName)

Collapses nodes of a given type sharing a property value into a single node. Rewires edges automatically.

Graph Extraction and Enrichment Functions

These are additional stored functions that must already be deployed on the target database. They are not bundled in references/DEPLOY_IRQL_FUNCTIONS.md. Use .show functions to verify availability before including in a pipeline.

FunctionOperationKey Property
Extract_Node_Email_Sender_Domain(T, displayName)Adds Domain to node propsEmailSender
Extract_Node_Employee_Firstname(T, displayName)Adds Firstname to node propsName
Extract_Node_Event_Network_Domain(T, displayName)Adds DomainName to node propsUrl
Enrich_Node_Ip_Employee(T, displayName)Adds employee info to IP nodesClientIp
Enrich_Node_Username_Employee(T, displayName)Adds employee info to user nodesUsername
Enrich_Node_Event_Authentication_Username(T, displayName)Adds auth contextUsername
Enrich_Node_Ip_Domain(T, displayName)Adds DNS domainsClientIp
Enrich_Node_Ip_Event_NetworkOutbound(T, displayName)Adds outbound eventsClientIp
Enrich_Graph_Ip_Employee(T, mappingJson)Expands graph with employee nodesClientIp
Enrich_Graph_Username_Employee(T, mappingJson)Expands graph with employee nodesUsername
Enrich_Graph_Event_Authentication_Username(T, mappingJson)Expands with auth nodesUsername

Mapping JSON Schema

The JSON mapping has two arrays: node_types and edges.

node_types[]
FieldRequiredDescription
typeYesNode type label (e.g. "User", "Host", "IP")
idYesPrefix for node ID; usually same as type
keyYesColumn name whose value becomes the node's identity
propsYesArray of columns to carry as node properties
defaultsNoObject of fallback values for null/empty properties
defIconNoDefault icon URL for this node type
displayNameNoColumn to use for display label (defaults to id)
colorNoColumn to source color from
sizeNoColumn to source size from
edges[]
FieldRequiredDescription
typeYesEdge type label (e.g. "AuthenticatesTo", "SentEmail")
sourceYes{"id": "<prefix>", "type": "<NodeType>"}
targetYes{"id": "<prefix>", "type": "<NodeType>"}
propsNoArray of columns to carry as edge properties
displayNameNoColumn for edge label
colorNoColumn for edge color
Icon Repository

Use icons from https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/:

  • IP: Public-IP-Addresses-(Classic).svg

  • Host/VM: Virtual-Machine.svg

  • User: Users.svg

  • Email: Mailbox.svg (or azure-cds/command-1070-Mail.svg)

  • Process: App-Services.svg

  • File: Storage-Accounts.svg

  • Alert: Activity-Log.svg

  • Domain: DNS-Zones.svg

Mapping Generation Rules

Given the supplied query columns and the user's graph description, generate the mapping JSON by:

  1. Identify entities -> each distinct noun becomes a node_type

  2. Identify relationships -> each verb/preposition becomes an edge

  3. Map to columns -> use actual columns produced by the supplied query; never assume unavailable columns

  4. Set direction -> source is the actor, target is the acted-upon

  5. Add properties -> include columns relevant to investigation (timestamps, results, hashes)

  6. Assign icons -> pick from the icon set above based on entity type

Column Reference (IRQL unified schema)
EntityKey ColumnAvailable Props
UserUsernameUsername, Name, Role, Email
HostHostnameHostname
IPClientIpClientIp
Email MessageSubjectEnvTime, Subject, Verdict, Url
SenderEmailSenderEmailSender, Domain
RecipientEmailRecipientEmailRecipient
ProcessProcessNameEnvTime, ProcessName, ProcessCommandLine, ProcessHash
FileFilenameEnvTime, Filename, Path, Sha256
DomainDomainNameDomainName
Auth Event(synthetic ID)EnvTime, UserAgent, Result, Description

Function Selection

  1. Start with the supplied KQL/IRQL tabular pipeline.

  2. Use Lift_To_Graph(mapping) to create graph entities.

  3. Add Extract_Node_*, Enrich_Node_*, or Enrich_Graph_* only when requested and compatible with the mapped keys.

  4. Add Graph_Fold_By_Property() only when grouping/collapse is requested.

  5. End visual output with Graph_Render_View().

  6. Preflight the exact stored functions selected for the pipeline.

Pipeline Pattern

// 1. Preserve the supplied KQL or IRQL query
<input query>
// 2. Lift to graph
| invoke Lift_To_Graph(<mapping_json>)
// 3. Optionally extract or enrich graph entities
| invoke <Extract_Node_* | Enrich_Node_* | Enrich_Graph_*>()
// 4. Optionally fold nodes when requested
| invoke Graph_Fold_By_Property("<NodeType>", "<PropertyName>")
// 5. Render
| invoke Graph_Render_View()

Examples

For additional prompts and worked examples, see references/EXAMPLES.md.

Authentication graph: IP -> AuthEvent -> User -> Host

Input query: Get_Event_Authentication_All | where Result == "Failed Login" | take 200

Graph request: "Show IPs, authentication events, users, and hosts; fold events by result."

let auth_mapping = '{"node_types":[{"type":"SrcIp","id":"SrcIp","key":"ClientIp","props":["ClientIp"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Public-IP-Addresses-(Classic).svg"},{"type":"Host","id":"Host","key":"Hostname","props":["Hostname"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Virtual-Machine.svg"},{"type":"User","id":"User","key":"Username","props":["Username"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Users.svg"},{"type":"AuthEvent","id":"AuthEvent","key":"AuthEventId","props":["AuthEventId","EnvTime","UserAgent","Result","Description"],"defaults":{"Result":"unknown"},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Activity-Log.svg"}],"edges":[{"type":"RequestsAuth","source":{"id":"SrcIp","type":"SrcIp"},"target":{"id":"AuthEvent","type":"AuthEvent"},"props":["EnvTime"]},{"type":"TargetsUser","source":{"id":"AuthEvent","type":"AuthEvent"},"target":{"id":"User","type":"User"},"props":["EnvTime"]},{"type":"AgainstHost","source":{"id":"AuthEvent","type":"AuthEvent"},"target":{"id":"Host","type":"Host"},"props":["EnvTime"]}]}';
Get_Event_Authentication_All
| extend AuthEventId = strcat(Username, "_", Hostname, "_", EnvTime)
| where Result == "Failed Login"
| take 200
| invoke Lift_To_Graph(auth_mapping)
| invoke Graph_Fold_By_Property("AuthEvent", "Result")
| invoke Graph_Render_View()
Email graph: Sender -> Message -> Recipient

Input query: Get_Email_All | take 400

Graph request: "Visualize sender-to-message-to-recipient flow and fold messages by verdict."

let mail_mapping = '{"node_types":[{"type":"EmailMessage","id":"Message","key":"Subject","props":["EnvTime","Subject","Verdict"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Media-File.svg"},{"type":"Sender","id":"Email","key":"EmailSender","props":["EmailSender"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-cds/command-1070-Mail.svg"},{"type":"Recipient","id":"Email","key":"EmailRecipient","props":["EmailRecipient"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-cds/command-1070-Mail.svg"}],"edges":[{"type":"SentBy","source":{"id":"Message","type":"EmailMessage"},"target":{"id":"Email","type":"Sender"},"props":["EnvTime","Verdict"]},{"type":"DeliveredTo","source":{"id":"Message","type":"EmailMessage"},"target":{"id":"Email","type":"Recipient"},"props":["EnvTime","Verdict"]}]}';
Get_Email_All
| take 400
| invoke Lift_To_Graph(mail_mapping)
| invoke Graph_Fold_By_Property("EmailMessage", "Verdict")
| invoke Graph_Render_View()
Suspicious domain investigation (end-to-end)

Basic source request: "Use outbound network events for these suspicious domains and graph IP-to-domain connections enriched with employee names."

This is the limited fallback: one known selector, one extractor, and one direct filter.

let suspicious_domain_mapping = '{"node_types":[{"type":"IP","id":"IP","key":"ClientIp","props":["ClientIp"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Public-IP-Addresses-(Classic).svg"},{"type":"Domain","id":"Domain","key":"DomainName","props":["DomainName"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/DNS-Zones.svg"}],"edges":[{"type":"ConnectsTo","source":{"id":"IP","type":"IP"},"target":{"id":"Domain","type":"Domain"},"props":["EnvTime"]}]}';
Get_Event_NetworkOutbound
| invoke Extract_Event_Network_Domain()
| where DomainName has_any ("raisinkanes.com", "nothing-to-see-here.net", "totally-legit-domain.com")
| invoke Lift_To_Graph(suspicious_domain_mapping)
| invoke Enrich_Node_Ip_Employee("Name")
| invoke Graph_Fold_By_Property("Domain", "DomainName")
| invoke Graph_Render_View()
Process execution graph: User -> Process -> ParentProcess

Input query: Get_Event_Process_All | where ProcessCommandLine has "powershell" | take 300

Graph request: "Visualize process, parent process, host, and user relationships."

let proc_mapping = '{"node_types":[{"type":"Process","id":"Proc","key":"ProcessName","props":["ProcessName","ProcessCommandLine","ProcessHash"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/App-Services.svg"},{"type":"ParentProcess","id":"Proc","key":"ParentProcessName","props":["ParentProcessName","ParentProcessHash"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/App-Services.svg"},{"type":"Host","id":"Host","key":"Hostname","props":["Hostname"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Virtual-Machine.svg"},{"type":"User","id":"User","key":"Username","props":["Username"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Users.svg"}],"edges":[{"type":"SpawnedBy","source":{"id":"Proc","type":"Process"},"target":{"id":"Proc","type":"ParentProcess"},"props":["EnvTime"]},{"type":"RanOn","source":{"id":"Proc","type":"Process"},"target":{"id":"Host","type":"Host"},"props":["EnvTime"]},{"type":"ExecutedBy","source":{"id":"Proc","type":"Process"},"target":{"id":"User","type":"User"},"props":["EnvTime"]}]}';
Get_Event_Process_All
| where ProcessCommandLine has "powershell"
| take 300
| invoke Lift_To_Graph(proc_mapping)
| invoke Graph_Render_View()

Query Results -> Mapping Translation

When the user supplies a query and describes the graph:

  1. Inspect the query's final output columns

  2. Parse the entity nouns and relationship verbs

  3. Generate the mapping JSON using only those columns

  4. Preserve the supplied pipeline and append Lift_To_Graph()

  5. Include Graph_Render_View() at the end

  6. If the user mentions grouping/collapsing and the function exists, add Graph_Fold_By_Property()

Output the complete KQL -- the supplied query plus mapping JSON inline as a string let binding -- after the required-function preflight passes. Clearly mark unverified function dependencies when the target database cannot be checked.

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>

If the user asks to save or open in Kusto Explorer, follow the procedure in references/KUSTO_EXPLORER_LAUNCH.md. Key rules:

  • Use ask_user to confirm before writing files or launching executables

  • Display file contents in chat so the user can review before opening

  • Never use shell interpolation or here-strings — write files via Set-Content/Add-Content

  • Never encode queries into browser URLs

  • On macOS/Linux, save the .kql file and suggest the VS Code Kusto extension or ADX Web Explorer

Files

SKILL.md

IRQL Graph Functions -- Query Results to Visualization

Apply the IRQL graph function family to tabular results. Given a KQL or IRQL query and the user's graph description, generate a Lift_To_Graph mapping and compose only the stored graph functions needed to visualize, fold, extract, or enrich the graph in Kusto Explorer. The source query does not need to use IRQL.

Scope and Routing

RequestUse
Turn supplied KQL/IRQL rows into an icon-decorated visual graphThis skill: Lift_To_Graph + Graph_Render_View
Fold nodes or apply Extract_Node_*, Enrich_Node_*, or Enrich_Graph_*This skill
Use make-graph, graph-match, shortest paths, connected components, graph models, or snapshotsazure-kusto-graph
Author a non-trivial KQL/IRQL investigation from natural languageA Kusto or IRQL query-generation skill, then this skill

If a request mixes visualization and native graph analysis, use this skill for the lift/render portion and azure-kusto-graph for operator semantics. Do not replace graph-lift functions with a hand-built edges-first graph unless the user asks for native graph operators.

Input Contract

  • Preferred input: a working KQL/IRQL query that produces tabular results, plus a natural-language description of the desired nodes, edges, labels, icons, extracts, enrichments, or folds.

  • This skill is not a natural-language-to-KQL or NL-to-IRQL converter. It transforms existing query results into graph visualizations. For general NL-to-KQL or NL-to-IRQL conversion, use a dedicated query-generation skill (available separately).

  • Preserve the supplied query's retrieval, joins, filters, and aggregations. Add only projections or synthetic IDs required by the graph mapping.

  • A basic natural-language source request is supported only when it maps directly to one known table or IRQL Get_* selector with obvious columns and simple filters. State the assumed source, and do not invent joins, schema, or investigation logic.

  • For non-trivial query construction, use a separate Kusto/IRQL query-generation skill first, then apply this skill to its output.

  • If no query or output schema is available and the source is not trivial, request the KQL query or its result columns before generating a mapping.

Activation Triggers

Use this skill when the user:

  • Supplies KQL/IRQL results and asks for an IRQL graph visualization or mapping

  • Mentions Lift_To_Graph, Graph_Render_View, or Graph_Fold_By_Property

  • Asks for icon-decorated node/edge mappings in Kusto Explorer

  • Wants to fold/collapse nodes by a shared property

  • Requests graph extraction or enrichment through Extract_Node_*, Enrich_Node_*, or Enrich_Graph_*

Do not activate this skill solely for graph-match, graph paths/components, persistent graphs, or generic make-graph construction; those belong to azure-kusto-graph.

Not a natural-language-to-KQL/IRQL converter. The input should generally be a working KQL or IRQL query whose results need graph visualization. Basic NL source requests work only for trivial single-table/selector cases. For general NL-to-KQL or NL-to-IRQL, use a dedicated query-generation skill (available separately).

Environment

  • Cluster: https://kc7001.eastus.kusto.windows.net

  • Databases: ValdyTimes, JoJosHospital (graph functions pre-deployed)

  • Rendering: Kusto Explorer desktop app (make-graph visualization window)

  • Tool: kusto_query (via Azure MCP Server)

Function Preflight

Lift_To_Graph and Graph_Render_View are stored functions, not built-in Kusto operators. Before generating or running a lift pipeline against a target database, check what is deployed:

.show functions
| where Name in~ ("Lift_To_Graph", "Graph_Render_View", "Graph_Fold_By_Property")
| project Name
  • Lift_To_Graph and Graph_Render_View are required.

  • Graph_Fold_By_Property is required only when folding is requested.

  • Check any Extract_Node_*, Enrich_Node_*, or Enrich_Graph_* function before using it; omit optional enrichment when unavailable unless the user wants it deployed.

  • If a required function is missing and you have permission to alter the database, ask the user for confirmation before deploying. Then use the .create-or-alter function definitions in references/DEPLOY_IRQL_FUNCTIONS.md. Run the relevant .create-or-alter block, then rerun the preflight check to confirm.

  • If you do not have alter permissions, tell the user which functions are missing and point them to references/DEPLOY_IRQL_FUNCTIONS.md for manual deployment.

IRQL Graph Function Family

Lift_To_Graph(T, mappingJson)

Transforms any tabular KQL result into a unified node + edge table.

Input: Any table T + a JSON mapping string. Output: Rows with EntityType = "node" or "edge", ready for make-graph.

Graph_Render_View(T)

Takes Lift_To_Graph output, splits nodes/edges, and calls make-graph to open Kusto Explorer's graph window.

Graph_Fold_By_Property(T, NodeType, PropertyName)

Collapses nodes of a given type sharing a property value into a single node. Rewires edges automatically.

Graph Extraction and Enrichment Functions

These are additional stored functions that must already be deployed on the target database. They are not bundled in references/DEPLOY_IRQL_FUNCTIONS.md. Use .show functions to verify availability before including in a pipeline.

FunctionOperationKey Property
Extract_Node_Email_Sender_Domain(T, displayName)Adds Domain to node propsEmailSender
Extract_Node_Employee_Firstname(T, displayName)Adds Firstname to node propsName
Extract_Node_Event_Network_Domain(T, displayName)Adds DomainName to node propsUrl
Enrich_Node_Ip_Employee(T, displayName)Adds employee info to IP nodesClientIp
Enrich_Node_Username_Employee(T, displayName)Adds employee info to user nodesUsername
Enrich_Node_Event_Authentication_Username(T, displayName)Adds auth contextUsername
Enrich_Node_Ip_Domain(T, displayName)Adds DNS domainsClientIp
Enrich_Node_Ip_Event_NetworkOutbound(T, displayName)Adds outbound eventsClientIp
Enrich_Graph_Ip_Employee(T, mappingJson)Expands graph with employee nodesClientIp
Enrich_Graph_Username_Employee(T, mappingJson)Expands graph with employee nodesUsername
Enrich_Graph_Event_Authentication_Username(T, mappingJson)Expands with auth nodesUsername

Mapping JSON Schema

The JSON mapping has two arrays: node_types and edges.

node_types[]
FieldRequiredDescription
typeYesNode type label (e.g. "User", "Host", "IP")
idYesPrefix for node ID; usually same as type
keyYesColumn name whose value becomes the node's identity
propsYesArray of columns to carry as node properties
defaultsNoObject of fallback values for null/empty properties
defIconNoDefault icon URL for this node type
displayNameNoColumn to use for display label (defaults to id)
colorNoColumn to source color from
sizeNoColumn to source size from
edges[]
FieldRequiredDescription
typeYesEdge type label (e.g. "AuthenticatesTo", "SentEmail")
sourceYes{"id": "<prefix>", "type": "<NodeType>"}
targetYes{"id": "<prefix>", "type": "<NodeType>"}
propsNoArray of columns to carry as edge properties
displayNameNoColumn for edge label
colorNoColumn for edge color
Icon Repository

Use icons from https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/:

  • IP: Public-IP-Addresses-(Classic).svg

  • Host/VM: Virtual-Machine.svg

  • User: Users.svg

  • Email: Mailbox.svg (or azure-cds/command-1070-Mail.svg)

  • Process: App-Services.svg

  • File: Storage-Accounts.svg

  • Alert: Activity-Log.svg

  • Domain: DNS-Zones.svg

Mapping Generation Rules

Given the supplied query columns and the user's graph description, generate the mapping JSON by:

  1. Identify entities -> each distinct noun becomes a node_type

  2. Identify relationships -> each verb/preposition becomes an edge

  3. Map to columns -> use actual columns produced by the supplied query; never assume unavailable columns

  4. Set direction -> source is the actor, target is the acted-upon

  5. Add properties -> include columns relevant to investigation (timestamps, results, hashes)

  6. Assign icons -> pick from the icon set above based on entity type

Column Reference (IRQL unified schema)
EntityKey ColumnAvailable Props
UserUsernameUsername, Name, Role, Email
HostHostnameHostname
IPClientIpClientIp
Email MessageSubjectEnvTime, Subject, Verdict, Url
SenderEmailSenderEmailSender, Domain
RecipientEmailRecipientEmailRecipient
ProcessProcessNameEnvTime, ProcessName, ProcessCommandLine, ProcessHash
FileFilenameEnvTime, Filename, Path, Sha256
DomainDomainNameDomainName
Auth Event(synthetic ID)EnvTime, UserAgent, Result, Description

Function Selection

  1. Start with the supplied KQL/IRQL tabular pipeline.

  2. Use Lift_To_Graph(mapping) to create graph entities.

  3. Add Extract_Node_*, Enrich_Node_*, or Enrich_Graph_* only when requested and compatible with the mapped keys.

  4. Add Graph_Fold_By_Property() only when grouping/collapse is requested.

  5. End visual output with Graph_Render_View().

  6. Preflight the exact stored functions selected for the pipeline.

Pipeline Pattern

// 1. Preserve the supplied KQL or IRQL query
<input query>
// 2. Lift to graph
| invoke Lift_To_Graph(<mapping_json>)
// 3. Optionally extract or enrich graph entities
| invoke <Extract_Node_* | Enrich_Node_* | Enrich_Graph_*>()
// 4. Optionally fold nodes when requested
| invoke Graph_Fold_By_Property("<NodeType>", "<PropertyName>")
// 5. Render
| invoke Graph_Render_View()

Examples

For additional prompts and worked examples, see references/EXAMPLES.md.

Authentication graph: IP -> AuthEvent -> User -> Host

Input query: Get_Event_Authentication_All | where Result == "Failed Login" | take 200

Graph request: "Show IPs, authentication events, users, and hosts; fold events by result."

let auth_mapping = '{"node_types":[{"type":"SrcIp","id":"SrcIp","key":"ClientIp","props":["ClientIp"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Public-IP-Addresses-(Classic).svg"},{"type":"Host","id":"Host","key":"Hostname","props":["Hostname"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Virtual-Machine.svg"},{"type":"User","id":"User","key":"Username","props":["Username"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Users.svg"},{"type":"AuthEvent","id":"AuthEvent","key":"AuthEventId","props":["AuthEventId","EnvTime","UserAgent","Result","Description"],"defaults":{"Result":"unknown"},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Activity-Log.svg"}],"edges":[{"type":"RequestsAuth","source":{"id":"SrcIp","type":"SrcIp"},"target":{"id":"AuthEvent","type":"AuthEvent"},"props":["EnvTime"]},{"type":"TargetsUser","source":{"id":"AuthEvent","type":"AuthEvent"},"target":{"id":"User","type":"User"},"props":["EnvTime"]},{"type":"AgainstHost","source":{"id":"AuthEvent","type":"AuthEvent"},"target":{"id":"Host","type":"Host"},"props":["EnvTime"]}]}';
Get_Event_Authentication_All
| extend AuthEventId = strcat(Username, "_", Hostname, "_", EnvTime)
| where Result == "Failed Login"
| take 200
| invoke Lift_To_Graph(auth_mapping)
| invoke Graph_Fold_By_Property("AuthEvent", "Result")
| invoke Graph_Render_View()
Email graph: Sender -> Message -> Recipient

Input query: Get_Email_All | take 400

Graph request: "Visualize sender-to-message-to-recipient flow and fold messages by verdict."

let mail_mapping = '{"node_types":[{"type":"EmailMessage","id":"Message","key":"Subject","props":["EnvTime","Subject","Verdict"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Media-File.svg"},{"type":"Sender","id":"Email","key":"EmailSender","props":["EmailSender"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-cds/command-1070-Mail.svg"},{"type":"Recipient","id":"Email","key":"EmailRecipient","props":["EmailRecipient"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-cds/command-1070-Mail.svg"}],"edges":[{"type":"SentBy","source":{"id":"Message","type":"EmailMessage"},"target":{"id":"Email","type":"Sender"},"props":["EnvTime","Verdict"]},{"type":"DeliveredTo","source":{"id":"Message","type":"EmailMessage"},"target":{"id":"Email","type":"Recipient"},"props":["EnvTime","Verdict"]}]}';
Get_Email_All
| take 400
| invoke Lift_To_Graph(mail_mapping)
| invoke Graph_Fold_By_Property("EmailMessage", "Verdict")
| invoke Graph_Render_View()
Suspicious domain investigation (end-to-end)

Basic source request: "Use outbound network events for these suspicious domains and graph IP-to-domain connections enriched with employee names."

This is the limited fallback: one known selector, one extractor, and one direct filter.

let suspicious_domain_mapping = '{"node_types":[{"type":"IP","id":"IP","key":"ClientIp","props":["ClientIp"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Public-IP-Addresses-(Classic).svg"},{"type":"Domain","id":"Domain","key":"DomainName","props":["DomainName"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/DNS-Zones.svg"}],"edges":[{"type":"ConnectsTo","source":{"id":"IP","type":"IP"},"target":{"id":"Domain","type":"Domain"},"props":["EnvTime"]}]}';
Get_Event_NetworkOutbound
| invoke Extract_Event_Network_Domain()
| where DomainName has_any ("raisinkanes.com", "nothing-to-see-here.net", "totally-legit-domain.com")
| invoke Lift_To_Graph(suspicious_domain_mapping)
| invoke Enrich_Node_Ip_Employee("Name")
| invoke Graph_Fold_By_Property("Domain", "DomainName")
| invoke Graph_Render_View()
Process execution graph: User -> Process -> ParentProcess

Input query: Get_Event_Process_All | where ProcessCommandLine has "powershell" | take 300

Graph request: "Visualize process, parent process, host, and user relationships."

let proc_mapping = '{"node_types":[{"type":"Process","id":"Proc","key":"ProcessName","props":["ProcessName","ProcessCommandLine","ProcessHash"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/App-Services.svg"},{"type":"ParentProcess","id":"Proc","key":"ParentProcessName","props":["ParentProcessName","ParentProcessHash"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/App-Services.svg"},{"type":"Host","id":"Host","key":"Hostname","props":["Hostname"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Virtual-Machine.svg"},{"type":"User","id":"User","key":"Username","props":["Username"],"defaults":{},"defIcon":"https://raw.githubusercontent.com/benc-uk/icon-collection/master/azure-icons/Users.svg"}],"edges":[{"type":"SpawnedBy","source":{"id":"Proc","type":"Process"},"target":{"id":"Proc","type":"ParentProcess"},"props":["EnvTime"]},{"type":"RanOn","source":{"id":"Proc","type":"Process"},"target":{"id":"Host","type":"Host"},"props":["EnvTime"]},{"type":"ExecutedBy","source":{"id":"Proc","type":"Process"},"target":{"id":"User","type":"User"},"props":["EnvTime"]}]}';
Get_Event_Process_All
| where ProcessCommandLine has "powershell"
| take 300
| invoke Lift_To_Graph(proc_mapping)
| invoke Graph_Render_View()

Query Results -> Mapping Translation

When the user supplies a query and describes the graph:

  1. Inspect the query's final output columns

  2. Parse the entity nouns and relationship verbs

  3. Generate the mapping JSON using only those columns

  4. Preserve the supplied pipeline and append Lift_To_Graph()

  5. Include Graph_Render_View() at the end

  6. If the user mentions grouping/collapsing and the function exists, add Graph_Fold_By_Property()

Output the complete KQL -- the supplied query plus mapping JSON inline as a string let binding -- after the required-function preflight passes. Clearly mark unverified function dependencies when the target database cannot be checked.

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>

If the user asks to save or open in Kusto Explorer, follow the procedure in references/KUSTO_EXPLORER_LAUNCH.md. Key rules:

  • Use ask_user to confirm before writing files or launching executables

  • Display file contents in chat so the user can review before opening

  • Never use shell interpolation or here-strings — write files via Set-Content/Add-Content

  • Never encode queries into browser URLs

  • On macOS/Linux, save the .kql file and suggest the VS Code Kusto extension or ADX Web Explorer

Requirements and compatibility

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an MCP server, Needs Node.js

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Created by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/skills · .github/plugins/azure-kusto-graph-skills/skills/azure-kusto-irql-graph · commit 0696600
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from .github/plugins/azure-kusto-graph-skills/LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download azure-kusto-irql-graph.zip