Azure Kusto Irql Graph logo

Azure Kusto Irql Graph

Organization
microsoft
azure-kusto-irql-graph

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.

Overview

Publishermicrosoft
RepositoryGitHub-Copilot-for-Azure
Skill nameazure-kusto-irql-graph
Stars
259
Forks
215
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by microsoft on GitHub. Read the source before you install it.

Installation

Install the Azure Kusto Irql Graph AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/microsoft/GitHub-Copilot-for-Azure/tree/main/plugins/azure-kusto-graph-skills/skills/azure-kusto-irql-graph
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/microsoft/GitHub-Copilot-for-Azure.git /tmp/GitHub-Copilot-for-Azure
mkdir -p .claude/skills
cp -r /tmp/GitHub-Copilot-for-Azure/plugins/azure-kusto-graph-skills/skills/azure-kusto-irql-graph .claude/skills/azure-kusto-irql-graph
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azure Kusto Irql Graph in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Azure Kusto Irql Graph on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Azure Kusto Irql Graph is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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:

kql
.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

kql
// 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."

kql
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."

kql
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.

kql
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."

kql
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

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Azure Kusto Irql Graph AI skill do?

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-m...

Why use Azure Kusto Irql Graph on TypingMind?

Because you install it once and use it with any model. Azure Kusto Irql Graph is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Azure Kusto Irql Graph in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/GitHub-Copilot-for-Azure/tree/main/plugins/azure-kusto-graph-skills/skills/azure-kusto-irql-graph. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Azure Kusto Irql Graph?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Azure Kusto Irql Graph?

As many as you like. As long as a model supports skills, you can use Azure Kusto Irql Graph with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Azure Kusto Irql Graph AI skill free?

Yes. It is published on GitHub by microsoft under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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