Official agent skill

Azv Azure To Diagram

by Azure in Azure/AZVerify

Reverse-engineer a live Azure scope (resource group or filtered subscription) into a professional Draw.io architecture diagram following established AzVerify conventions.

OfficialMITAuto-check passedDevelopment

Install Azv Azure To Diagram

skills CLI
$ npx skills add Azure/AZVerify --skill azv-azure-to-diagram -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install Azure/AZVerify azv-azure-to-diagram --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/Azure/AZVerify.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.github/skills/azv-azure-to-diagram .claude/skills/azv-azure-to-diagram && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
azv-azure-to-diagram
GitHub stars
101
Token cost
~5.7k tokens
SKILL.md length
2,894 words
Files
1
Skills in repo
9
Repo updated
First seen
Licence
MIT

At a glance

Reverse-engineer a live Azure scope (resource group or filtered subscription) into a professional Draw.io architecture diagram following established AzVerify conventions.

  • Works in 9 steps: Check Azure Authentication → Accept Inputs → Discover Azure Resources → …
  • The user wants to visualize
  • SKILL.md covers Output Budget Rules, Fallback: pwsh Unavailable and Steps
  • Calls az and pwsh

What it does

Azv Azure To Diagram is an agent skill from Azure/AZVerify, published by the product's own GitHub organization. Reverse-engineer a live Azure scope (resource group or filtered subscription) into a professional Draw.io architecture diagram following established AzVerify conventions. Use when the user wants to visualize or document existing Azure infrastructure as a diagram.

Its SKILL.md is about 5.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts. Compatibility notes: Requires an authenticated Azure session (CLI, Az PowerShell, or Azure MCP).

It sits in Development, covering Diagrams. It works with Microsoft Azure, draw.io, Bicep and PowerShell. The licence is MIT.

When your agent uses it

  • The user wants to visualize
  • Document existing Azure infrastructure as a diagram

Example prompts

  • “/azv-azure-to-diagram”

Requirements

  • Compatibility (from SKILL.md): Requires an authenticated Azure session (CLI, Az PowerShell, or Azure MCP).

Workflow steps

9 steps, taken from the step headings in SKILL.md.

  1. Check Azure Authentication
  2. Accept Inputs
  3. Discover Azure Resources
  4. Filter Infrastructure-Only Resources
  5. Check for Large Scope
  6. Enrich Resource Properties
  7. Infer Relationships
  8. Generate Draw.io Diagram
  9. Create Solution Folder Output

What it can do on your machine

Read from SKILL.md and the folder at commit d6a2b92. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • az
    • pwsh

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use az, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

  • Compatibility

    Requires an authenticated Azure session (CLI, Az PowerShell, or Azure MCP).

    From compatibility in the SKILL.md frontmatter.

Context cost

Azv Azure To Diagram loads about 5.7k tokens when it runs. Until then it costs about 71 tokens; SKILL.md has 2,894 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~71
When it runs · the whole SKILL.md, loaded when a task matches
~5.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from Azure/AZVerify at commit d6a2b92, republished under its MIT licence (© Azure). 2,894 words, ~5,672 tokens.

Download SKILL.mdSave it as .claude/skills/azv-azure-to-diagram/SKILL.md (or your agent's skills folder).
name
azv-azure-to-diagram
description
Reverse-engineer a live Azure scope (resource group or filtered subscription) into a professional Draw.io architecture diagram following established AzVerify conventions. Use when the user wants to visualize or document existing Azure infrastructure as a diagram.
compatibility
Requires an authenticated Azure session (CLI, Az PowerShell, or Azure MCP).
license
MIT

Discover resources in a live Azure scope and generate a Draw.io architecture diagram with proper container hierarchy, verified icons, and inferred relationships.

Input: An Azure scope — a resource group name (primary) or a subscription ID with optional resource type filter. The user can specify the scope or the skill will prompt for it.

Tools required: File system tools (read/write files), Terminal (for running az CLI commands and PowerShell 7 / pwsh shared scripts), Azure MCP server tools (mcp_azure_group_resource_list, mcp_azure_compute, mcp_azure_storage, mcp_azure_subscription_list, etc.), Draw.io MCP (mcp_drawio_create_diagram or mcp_draw_io_create_diagram)


Tool Preflight

Before discovery, verify the capabilities used by this workflow:

  1. Call azure-get_azure_bestpractices with get_azure_bestpractices_get for general code generation guidance.
  2. Call azure-bicepschema with bicepschema_get for every resource type whose API version or deployable schema is uncertain.
  3. Use azure-documentation search and fetch for current service guidance when a schema call does not answer the question.

If a capability is unavailable, continue only when the matching shared reference plus Bicep CLI validation can provide the same check; report the fallback in the verification summary.

Output Budget Rules

Follow .github/skills/shared/procedures/output-budget.md strictly — this skill frequently handles 20-40+ resources and can hit the LLM response length limit. In addition to the shared rules:

  • Build the Draw.io diagram directly. Write the assembled XML to the .drawio file via the Draw.io MCP tool. Do NOT echo full Draw.io XML content in the response — show only the file path and a summary of what was generated.
  • Delete intermediate files on the skill's own schedule. Intermediate extraction files (resource-model.json, extract-*.json, and any other temporary resource model JSON) are never deliverables. Keep them available until Step 9c has written original-request.md (it needs the resource counts and relationship tables), then delete them all in Step 9d. Never leave the resource model JSON behind after the skill completes.

Fallback: pwsh Unavailable

If pwsh/powershell.exe or a shared script cannot be executed, use the fallback that matches the step you are on, then continue the workflow normally:

StepFallback source
1 — Auth checkMCP auth probe fallback in .github/skills/shared/procedures/azure-authentication.md
3 — Discovery"Script/pwsh Unavailable — MCP Fallback" in .github/skills/shared/azure-resource-configs.md (see Step 3e)
4 — FilteringInline fallback in .github/skills/shared/procedures/resource-filtering.md
6a — Property extraction"Script/pwsh Unavailable — MCP Fallback" in .github/skills/shared/azure-resource-configs.md
6b — Read-only stripping and secretsManual strip rules listed in Step 6b, using .github/skills/shared/data/arm-readonly-properties.json
7a — Relationships"Manual Relationship Inference — Script Unavailable Fallback" in .github/skills/shared/azure-resource-model.md

Stop only if Azure MCP is also unavailable, using the prerequisite message in .github/skills/shared/azure-resource-configs.md.

Steps

1. Check Azure Authentication

Run pwsh .github/skills/shared/scripts/Test-AzureAuth.ps1 — see .github/skills/shared/procedures/azure-authentication.md for the script contract. The script writes a JSON status object to stdout and exits non-zero when no Azure session is found. A non-zero exit code is a HARD GATE: present the authentication instructions from the contract doc and stop. (If pwsh or the script is unavailable, see "Fallback: pwsh Unavailable".)

2. Accept Inputs

Identify the Azure scope to discover resources from.

2a. Identify the Azure Scope

If the user specifies a resource group name:

  • Use that resource group as the discovery scope
  • Verify the resource group exists: run az group show --name <name> (or, if az is unavailable, Get-AzResourceGroup -Name <name> via Az PowerShell, or mcp_azure_group_list/mcp_azure_group_resource_list via Azure MCP) — if all available methods fail, report an error and stop

If the user specifies a subscription ID:

  • Use that subscription as the discovery scope
  • Note: subscription-level discovery can produce many resources — the skill will apply filtering and warnings (see Step 4)

If no scope is specified:

  • Ask the user:
Which Azure resource group should I generate a diagram from?

If you want subscription-level discovery, provide a subscription ID instead.
  • Wait for user input
2b. Identify Optional Filters

Resource type filters: If the user provides a resource type filter (e.g., "only compute and networking resources"):

  • Map the filter to Azure resource type prefixes (e.g., Microsoft.Compute/*, Microsoft.Network/*)
  • Apply these filters during discovery

Resource exclusions: If the user wants to exclude specific resources or types from the diagram:

  • Accept a list of resource names or type patterns to skip
  • Apply exclusions during the filtering step (Step 4), in addition to the standard "Exclude for Diagrams" rules
3. Discover Azure Resources

Enumerate all resources in the specified Azure scope.

3a. Create Solution Folder

Create the solution folder now, before any intermediate files are written.

  • Name the folder using the scope name in kebab-case:
    • Resource group MyAppRG → folder my-app-rg/
    • If a folder with that name already exists, increment a numeric suffix until a unique name is found (e.g., my-app-rg-2/, my-app-rg-3/, …). If more than 5 collisions are detected, ask the user to confirm the output folder name.
  • Use this folder for all intermediate files and final deliverables throughout the skill.

3b. Resource group scope

Run the shared discovery script and write the resource model to a temporary JSON file in the output folder:

pwsh .github/skills/shared/scripts/Get-AzureResourceModel.ps1 -ResourceGroup <rg-name> -OutFile <output-folder>/resource-model.json

The script emits the shared resource model contract (id, name, type, location, tags, sku) documented in .github/skills/shared/azure-resource-model.md. Treat the emitted JSON as the source of truth for Steps 4-7. Do not print the model contents.

If the script exits non-zero or produces unparseable output, use "Script/pwsh unavailable — MCP fallback" (Step 3e) instead of stopping. Only stop if Azure MCP is also unavailable, per "Fallback: pwsh Unavailable" above. If the result count is zero and the resource group was confirmed to exist in Step 2a, warn the user that the authenticated identity may lack Reader permissions.

Display a single progress line:

Found **N resources** in `<rg-name>` — now filtering and enriching.

3c. Subscription scope

Run the same script with -SubscriptionId <sub-id> instead of -ResourceGroup. Do not apply user-specified exclusions here — those are applied exactly once, in Step 4, alongside the standard exclusion rules, to avoid filtering the same resource list twice. Resource type inclusion filters (Step 2b) are applied uniformly for both scopes in Step 3d below.

If the script exits non-zero or produces unparseable output, use "Script/pwsh unavailable — MCP fallback" (Step 3e) instead of stopping. Only stop if Azure MCP is also unavailable, per "Fallback: pwsh Unavailable" above. If the result count is zero, warn the user that the authenticated identity may lack Reader permissions on this subscription.

Display a single progress line:

Found **N resources** in subscription `<sub-id>` — now filtering and enriching.

3d. Apply Resource Type Inclusion Filters

Get-AzureResourceModel.ps1 has no -ResourceTypeFilter parameter — it never narrows results by type on its own, regardless of scope. If the user specified a resource type inclusion filter in Step 2b (e.g., "only show networking resources" → Microsoft.Network/*), apply it now, before the resource model is treated as the source of truth for the remaining steps:

  1. Read the resource model JSON written in Step 3b/3c (<output-folder>/resource-model.json).
  2. Keep only resources whose type matches one of the mapped inclusion prefixes from Step 2b (case-insensitive, wildcard * match). Discard the rest.
  3. Overwrite <output-folder>/resource-model.json with the filtered result, preserving the same JSON shape (id, name, type, location, tags, sku, relationships).
  4. If no resources match the inclusion filter, treat this the same as "no resources found" (see Step 3f) rather than continuing with an empty model.

If the user did not specify an inclusion filter in Step 2b, skip this step — the model from Step 3b/3c passes through unchanged.

3e. Script/pwsh unavailable — MCP fallback

If pwsh/powershell.exe or the script cannot be executed, build the same resource model through Azure MCP as described in "Fallback: pwsh Unavailable" (list resources with mcp_azure_group_resource_list, then assemble the model shape by hand, applying the same inclusion-filter logic from Step 3d before treating the result as final).

3f. Handle empty results

If no resources are found (or none remain after filtering):

## No Resources Found

No resources were found in `<scope-name>`.

If you expected resources here, verify:
- The resource group name is spelled correctly
- You're connected to the correct subscription (`az account show` or `Get-AzContext`)
- Resources have been deployed to this scope
  • Stop execution
4. Filter Infrastructure-Only Resources

Run pwsh .github/skills/shared/scripts/Select-AzureResources.ps1 -InputFile <resource-model.json> -Mode diagram — see .github/skills/shared/procedures/resource-filtering.md for the script contract. The script applies the shared exclusion rules using the "Exclude for Diagrams" column, writes the filtered resource model JSON to stdout, and should be treated as the source of truth for the remaining steps. (If pwsh or the script is unavailable, see "Fallback: pwsh Unavailable".)

If the script exits non-zero or produces unparseable output, report the error message to the user and stop. Do not proceed with an empty or partial resource list.

Also apply any user-specified exclusion filters from Step 2b now — this is the only place exclusions are applied (inclusion filters, if any, were already applied in Step 3d).

If all resources are filtered out, report "No Diagram-Worthy Resources" and stop execution.

Display the filtered resource list:

  • If the filtered count is 10 or fewer: display the full table inline with columns: #, Resource, Type, Location, SKU.
  • If the filtered count exceeds 10: print only the count and a resource-type breakdown summary in the chat response. Write the full resource table to the temporary resource model file in the solution folder instead of echoing it.

Write the temporary resource model JSON to the solution folder created in Step 3a. It is an intermediate artifact only and must be deleted before the skill finishes.

5. Check for Large Scope

If the filtered resource count exceeds 20, warn and offer:

  1. Filter by type — present unique resource types with counts, let user select
  2. Generate full diagram — proceed with all resources

Re-filter if the user selects option 1, then continue.

If the user's response does not map to option 1 or option 2, re-present the two options with the instruction: "Please reply with 1 to filter by type or 2 to generate the full diagram." If after two re-prompts no valid choice is received, default to option 2 and note this in the completion summary.

6. Enrich Resource Properties

Use resource-type-specific Azure MCP tools to retrieve detailed properties for relationship inference.

Enrichment targets (by resource type): look up each in-scope resource's resourceTypes[] entry in .github/skills/shared/data/azure-property-paths.json for the MCP tool (or CLI fallback) and the ARM JSON paths to extract. That mapping is the authoritative source — see "Property Mapping Source of Truth" in .github/skills/shared/azure-resource-configs.md for how to consume it. Do not duplicate the mapping table here.

Enrichment process:

Prefer batch CLI enrichment over per-resource MCP calls to reduce output volume:

  1. Batch approach (preferred): Run a single az resource list --resource-group <rg> -o json with --query to get all resources with their full properties in one call (or, if az is unavailable, Get-AzResource -ResourceGroupName <rg> -ExpandProperties via Az PowerShell). Parse the output to extract relationship-relevant properties. As each resource is enriched this way, record its enrichment source (batch) in a local tracking variable (e.g., a map of resource ID → batch/targeted/failed).
  2. Determine remaining gaps: After the batch query, before making any MCP calls, produce a list of resource types still needing targeted calls — i.e., types whose relationship-relevant properties are not present in the batch output (per .github/skills/shared/data/azure-property-paths.json).
  3. Targeted MCP calls: Only use per-resource MCP calls for the resource types identified in step 2 that need specific APIs not available in the batch output (e.g., az webapp config appsettings list for App Service app settings). Update the tracking variable to targeted for each resource enriched this way, or failed if the call errors or is unavailable.
  4. Store enriched properties alongside the base resource information in the temporary resource model file.
  5. Use the tracking variable (not re-derivation) to produce the accurate counts (K batch, J targeted, W failed/warnings) in the summary line below.

Output discipline during enrichment:

  • Do NOT print a status line per resource
  • Print a single summary after all enrichment is complete:
    Enriched N resources (K via batch query, J via targeted API calls, W warnings).
  • If any resources could not be enriched, list only the warnings (not successes)

Graceful fallback: If a resource-type-specific MCP tool fails or is unavailable:

  • Track the warning internally
  • Continue with the base resource information from the list operation
  • Do not stop execution due to enrichment failures
  • Report all warnings in the single summary line above
Show full SKILL.md (1,003 more words)Show less
7. Infer Relationships

The discovery script (Step 3b/3c) already populates a baseline relationships array on each resource — parent/child contains links plus any relationship it detects by matching ARM resource IDs inside properties (see .github/skills/shared/azure-resource-model.md). That baseline was computed before enrichment, so it only saw the often-sparse az resource list property bags — it can miss direct ARM ID references (NIC subnet IDs, private-endpoint targets, App Service Plan IDs, etc.) that only appear once Step 6 enrichment fills in the fuller properties. Before layering on the patterns below, re-run the generic ID-matching detection (the patterns in the "Manual Relationship Inference" table of .github/skills/shared/azure-resource-model.md) against the enriched properties and merge any newly-found relationships into the baseline array, de-duplicating against relationships already present. Then use the patterns below to add diagram-specific edge styles and detect relationships the generic ID matching cannot see (connection strings, Key Vault reference syntax, RBAC scope strings, co-location).

Analyze enriched resource properties to discover the remaining relationships the baseline cannot see — none of these are direct ARM ID references, so generic ID matching misses them. (Edge styling for the resulting relationship types is defined once in drawio-diagram-conventions.md §5 — do not re-specify colors here.)

PatternDetectionRelationship
Key Vault ReferencesConfig contains @Microsoft.KeyVault(SecretUri=...)secures
App InsightsApp settings contain APPLICATIONINSIGHTS_CONNECTION_STRING matching AI resourceconnects
Connection StringsApp settings contain SQL/Cosmos/Storage/Redis server names matching discovered resourcesconnects
Named Connection Stringsaz webapp config connection-string list entries reference database or storage resources in the same RGconnects
RBAC Role AssignmentManaged identity has role assignments whose scope matches a discovered resource's ARM IDsecures

Co-located Resource Inference

After completing explicit relationship detection using the table above, check for implicit co-location connections using this numbered checklist:

  1. Count the filtered resources — proceed only if ≤15 resources remain (small, focused resource groups imply intentional co-location).
  2. Identify all Key Vault and Storage Account resources that have no explicit reference detected (no app settings, connection strings, or Key Vault reference patterns among resources in this resource group pointing to them).
  3. Identify all Web App and Function App resources.
  4. For each unmatched pair (Key Vault/Storage ↔ Web App/Function App), add an inferred connects edge and label it (inferred) in the relationship output so the user can verify.

Resources with no relationships are placed directly inside their resource group container.

Output (concise): Follow Output Budget Rules for display format. Example count summary:

Inferred **N relationships** (saved to a temporary resource model file). Key: 3 subnet placements, 2 PEs, 4 data connections.
8. Generate Draw.io Diagram

Build the Draw.io diagram XML from the resource model and inferred relationships following the shared conventions in .github/skills/shared/drawio-diagram-conventions.md.

Follow all diagram construction rules: canvas format, stencil mapping lookup, resource shapes and icon paths, container hierarchy (Resource Group → VNet → Subnet), edge rules, VNet Integration special case, and layout patterns (left-to-right flow, 2×2 zone grid, hub-and-spoke, semantic proximity, sizing).

If a resource type has no entry in azure-stencil-mapping.json, use the generic Azure resource stencil mxgraph.azure2.general and append a warning to the completion summary listing unmapped types so the user can update the mapping file.

Multi-page diagrams: When the resource group includes networking subnets or monitoring resources that survived filtering, generate additional pages alongside "Architecture Overview":

  • Network Topology page: Generated when the filtered resources include VNets with subnets. Follow the network topology layout rules in .github/skills/shared/drawio-diagram-conventions.md section 7e. Use a 3-column × N-row subnet grid grouped by function tier. Place resources inside subnets only when a confirmed VNet Integration or subnet delegation exists (e.g., a Managed Identity or WAF with an actual subnet association); ASPs and other resources without VNet Integration should be placed in a separate swimlane outside the VNet container — never in a scattered row at the bottom of the VNet. Add per-subnet route table icons instead of radiating edges from a central icon. Target pageWidth="1800" pageHeight="1600" — the page MUST NOT require horizontal scrolling on a 1920px display.
  • Monitoring page: Generated when alert rules or action groups are present in the filtered model. Do not assume that a Step 2b inclusion filter overrides Step 4's standard exclusions. Layout: single flat row of alert rules linked to their telemetry resources. pageWidth="1800" is sufficient.

8f. Generate the diagram

Use the Draw.io MCP tool (mcp_drawio_create_diagram or mcp_draw_io_create_diagram) to create the .drawio file with the assembled XML. Output discipline for diagram generation:

  • Do NOT echo or print the Draw.io XML in the response — it is passed directly to the MCP tool
  • After the diagram is created, confirm with a single line: Diagram created with N resource cells and M edges.

If the Draw.io MCP tool is unavailable or returns an error, write the assembled XML string to <folder-name>.drawio directly using the file system tool and note in the completion summary that the file was written without MCP validation. If both the Draw.io MCP tool and the file system tool are unavailable, report the failure to the user and stop without outputting the raw XML.

9. Create Solution Folder Output

Create a solution folder containing the generated diagram and metadata.

9a. Confirm the solution folder

The solution folder was created in Step 3a. Save the diagram file here now (see Step 9b).

9b. Save the diagram

  • Save the .drawio file inside the solution folder
  • Name it using the folder name: <folder-name>.drawio

9c. Create original-request.md

Document the discovery in original-request.md with: source scope, subscription, discovery date, resource counts, full resource table (Resource, Type, Location), full relationship table (Source, Relationship, Target), and notes pointing to related skills (azv-bicep-diagram-sync, azv-diagram-to-bicep). Full tables go here, not in the chat response. Do not transcribe raw enrichment JSON properties — only the structured resource and relationship tables are required.

9d. Clean up intermediate files

Delete all intermediate files from the solution folder before you finish — only final deliverables should remain. This includes resource-model.json (written in Step 3b/3c), any filtered copy written in Step 4, and any extract-*.json files written during enrichment. The raw JSON properties from enrichment do not need to be transcribed to original-request.md; only the structured tables from Step 9c are required there.

9e. Present completion (concise)

Show: folder path, diagram file with resource count, original-request.md, resource/relationship/excluded counts, and next steps pointing to azv-diagram-to-bicep and azv-diagram-azure-sync.

© Azure, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .github/skills/azv-azure-to-diagram of Azure/AZVerify.

Open the folder on GitHubat commit d6a2b92

Compare with similar skills

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Questions about Azv Azure To Diagram

What does Azv Azure To Diagram do?

Reverse-engineer a live Azure scope (resource group or filtered subscription) into a professional Draw.io architecture diagram following established AzVerify conventions. Azv Azure To Diagram is an agent skill from Azure/AZVerify, published by the product's own GitHub organization.io architecture diagram following established AzVerify conventions.

When should I use Azv Azure To Diagram?

Azv Azure To Diagram fits situations like: the user wants to visualize; document existing Azure infrastructure as a diagram.

How do I install Azv Azure To Diagram in Claude Code?

Run `npx skills add Azure/AZVerify --skill azv-azure-to-diagram -a claude-code`. Or copy the skill folder (.github/skills/azv-azure-to-diagram in Azure/AZVerify) into .claude/skills/azv-azure-to-diagram in your project. Claude Code loads it when a task matches its description.

How do I install Azv Azure To Diagram in Codex?

Run `npx skills add Azure/AZVerify --skill azv-azure-to-diagram -a codex`. Or copy the skill folder (.github/skills/azv-azure-to-diagram in Azure/AZVerify) into .agents/skills/azv-azure-to-diagram in your project. Codex loads it when a task matches its description.

Can I use Azv Azure To Diagram in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add Azure/AZVerify --skill azv-azure-to-diagram -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/azv-azure-to-diagram, .gemini/skills/azv-azure-to-diagram, .github/skills/azv-azure-to-diagram and .opencode/skills/azv-azure-to-diagram in your project.

What does Azv Azure To Diagram need to run?

Going by SKILL.md and its folder, Azv Azure To Diagram needs the command-line tools its instructions call (az and pwsh). Compatibility (from SKILL.md): Requires an authenticated Azure session (CLI, Az PowerShell, or Azure MCP)..

Does Azv Azure To Diagram access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Azv Azure To Diagram safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Azv Azure To Diagram use?

Azv Azure To Diagram is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Azv Azure To Diagram use?

About 5.7k tokens (SKILL.md is roughly 23k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Azv Azure To Diagram?

Skills that share tags, products or a category with Azv Azure To Diagram: Azure Draw.io MCP Diagrams (thomast1906/github-copilot-agent-skills, 202 stars), Drawio MCP Diagramming (thomast1906/github-copilot-agent-skills, 202 stars), Drawio Azure (sparklabx/drawio-ai-kit, 655 stars) and Terravision Cloud Diagrams (patrickchugh/terravision, 1.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Azv Azure To Diagram?

Azure (a GitHub organization, an official publisher) maintains it in Azure/AZVerify, which has 101 GitHub stars. The repository holds 9 skills in this directory. The repository was last updated on August 27, 2026.

Source: Azure/AZVerify on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.