Azure Functions logo

Azure Functions

Organization
managedcode
azure-functions

Build, review, or migrate Azure Functions in .NET with correct execution model, isolated worker setup, bindings, DI, and Durable Functions patterns. USE FOR: working on Azure Functions in .NET; migrating from the in-process model to the isolated worker model; adding Durable Functions, bindings, or host configuration. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

Overview

Publishermanagedcode
Repositorydotnet-skills
Skill nameazure-functions
Stars
492
Forks
38
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Azure Functions 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/managedcode/dotnet-skills/tree/main/catalog/Frameworks/Azure-Functions/skills/azure-functions
  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/managedcode/dotnet-skills.git /tmp/dotnet-skills
mkdir -p .claude/skills
cp -r /tmp/dotnet-skills/catalog/Frameworks/Azure-Functions/skills/azure-functions .claude/skills/azure-functions
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Azure Functions 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 Functions 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 Functions 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.

Azure Functions for .NET

Trigger On

  • working on Azure Functions in .NET
  • migrating from the in-process model to the isolated worker model
  • adding Durable Functions, bindings, or host configuration

Documentation

References

  • Patterns - Isolated worker patterns, Durable Functions patterns, advanced binding patterns
  • Anti-Patterns - Common Azure Functions mistakes and how to avoid them

Workflow

  1. Use isolated worker model for all new work:

    • In-process model reaches end of support on November 10, 2026
    • Runtime v1.x ends support on September 14, 2026
    • Target .NET 8+ for longest support window
  2. Detect current project shape:

    • Target framework and runtime version
    • Worker model (isolated vs in-process)
    • Binding packages and host configuration
  3. Use standard .NET patterns in isolated model:

    • Normal dependency injection
    • Middleware pipeline
    • IOptions<T> for configuration
    • ILogger<T> for logging
  4. For Durable Functions:

    • Validate orchestration determinism constraints
    • Handle replay behavior correctly
    • Use typed activity patterns
  5. Verify both local and deployment behavior.

Isolated Worker Model Setup

Basic Function with DI

csharp
// Program.cs
var host = new HostBuilder()
    .ConfigureFunctionsWebApplication()
    .ConfigureServices(services =>
    {
        services.AddApplicationInsightsTelemetryWorkerService();
        services.ConfigureFunctionsApplicationInsights();
        services.AddSingleton<IMyService, MyService>();
    })
    .Build();

host.Run();

HTTP Trigger Function

csharp
public class HttpFunctions(ILogger<HttpFunctions> logger, IMyService myService)
{
    [Function("GetItems")]
    public async Task<IActionResult> GetItems(
        [HttpTrigger(AuthorizationLevel.Function, "get", Route = "items")] HttpRequest req)
    {
        logger.LogInformation("Processing GetItems request");
        var items = await myService.GetItemsAsync();
        return new OkObjectResult(items);
    }
}

Queue Trigger with Options

csharp
public class QueueFunctions(ILogger<QueueFunctions> logger, IOptions<ProcessingOptions> options)
{
    [Function("ProcessMessage")]
    public async Task ProcessMessage(
        [QueueTrigger("myqueue", Connection = "AzureWebJobsStorage")] string message)
    {
        logger.LogInformation("Processing message: {Message}", message);
        // Process with retry policy from options
    }
}

Middleware Pattern

Custom Middleware

csharp
// Program.cs
var host = new HostBuilder()
    .ConfigureFunctionsWebApplication(builder =>
    {
        builder.UseMiddleware<ExceptionHandlingMiddleware>();
        builder.UseMiddleware<CorrelationIdMiddleware>();
    })
    .Build();

// CorrelationIdMiddleware.cs
public class CorrelationIdMiddleware : IFunctionsWorkerMiddleware
{
    public async Task Invoke(FunctionContext context, FunctionExecutionDelegate next)
    {
        var correlationId = context.Features.Get<IHttpRequestFeature>()?.Headers["X-Correlation-Id"]
            ?? Guid.NewGuid().ToString();

        context.Items["CorrelationId"] = correlationId;

        await next(context);
    }
}

Durable Functions Patterns

Function Chaining

csharp
[Function(nameof(ChainOrchestrator))]
public static async Task<string> ChainOrchestrator(
    [OrchestrationTrigger] TaskOrchestrationContext context)
{
    var result1 = await context.CallActivityAsync<string>(nameof(Step1), "input");
    var result2 = await context.CallActivityAsync<string>(nameof(Step2), result1);
    var result3 = await context.CallActivityAsync<string>(nameof(Step3), result2);
    return result3;
}

[Function(nameof(Step1))]
public static string Step1([ActivityTrigger] string input) => $"Step1({input})";

[Function(nameof(Step2))]
public static string Step2([ActivityTrigger] string input) => $"Step2({input})";

[Function(nameof(Step3))]
public static string Step3([ActivityTrigger] string input) => $"Step3({input})";

Fan-Out/Fan-In

csharp
[Function(nameof(FanOutFanInOrchestrator))]
public static async Task<int[]> FanOutFanInOrchestrator(
    [OrchestrationTrigger] TaskOrchestrationContext context)
{
    var workItems = await context.CallActivityAsync<string[]>(nameof(GetWorkItems), null);

    // Fan out - process all items in parallel
    var tasks = workItems.Select(item =>
        context.CallActivityAsync<int>(nameof(ProcessWorkItem), item));

    // Fan in - wait for all to complete
    var results = await Task.WhenAll(tasks);

    return results;
}

[Function(nameof(ProcessWorkItem))]
public static int ProcessWorkItem([ActivityTrigger] string item)
{
    // Process item and return result
    return item.Length;
}

Human Interaction Pattern

csharp
[Function(nameof(ApprovalOrchestrator))]
public static async Task<string> ApprovalOrchestrator(
    [OrchestrationTrigger] TaskOrchestrationContext context)
{
    var request = context.GetInput<ApprovalRequest>();

    // Send notification
    await context.CallActivityAsync(nameof(SendApprovalRequest), request);

    // Wait for external event with timeout
    using var cts = new CancellationTokenSource();
    var approvalTask = context.WaitForExternalEvent<bool>("ApprovalEvent");
    var timeoutTask = context.CreateTimer(context.CurrentUtcDateTime.AddDays(7), cts.Token);

    var winner = await Task.WhenAny(approvalTask, timeoutTask);

    if (winner == approvalTask)
    {
        cts.Cancel();
        return approvalTask.Result ? "Approved" : "Rejected";
    }

    return "Timed out";
}

Best Practices

  1. Use isolated worker model for new development - Full .NET ecosystem access, middleware support, and longer support lifecycle
  2. Inject dependencies via constructor - Use ILogger<T> and service interfaces for testability
  3. Keep orchestrator code deterministic - No I/O, random, DateTime.Now, or Guid.NewGuid() in orchestrators
  4. Handle sensitive data in activities - Fetch secrets from Key Vault in activity functions, never in orchestrators
  5. Use unique task hub names - Prevent accidental sharing when multiple apps use the same storage
  6. Avoid large inputs/outputs - Serialize to blob storage for large payloads to prevent history bloat
  7. Configure concurrency limits - Set appropriate limits in host.json for resource-intensive functions
  8. Keep SDK and extensions updated - Latest versions include performance improvements and bug fixes

Anti-Patterns to Avoid

Anti-PatternWhy It's BadBetter Approach
Mixing in-process and isolated guidanceIncompatible APIs and patternsChoose one model consistently
Non-deterministic orchestrator codeReplay failures, stuck orchestrationsUse context.CurrentUtcDateTime, no I/O
Large orchestrator inputs/outputsHistory bloat, memory issuesStore large data in blob storage
Shared task hub namesMessage conflicts, stuck orchestrationsUse unique names per app
Secrets in orchestrator historySecurity risk, exposed in logsFetch secrets in activity functions
Blocking calls in async functionsThread pool exhaustionUse await throughout
Missing retry policiesTransient failures cause job lossConfigure retry in bindings or code
Ignoring execution model migrationEOL November 2026 for in-processMigrate to isolated worker model

Deployment Considerations

Linux Consumption Plan Limitations

.NET 10+ apps cannot run on Linux Consumption plan.
Use Flex Consumption plan or App Service for .NET 10+.
.NET 9 is the last version supported on Linux Consumption.

host.json Configuration

json
{
  "version": "2.0",
  "extensions": {
    "durableTask": {
      "hubName": "MyUniqueTaskHub",
      "maxConcurrentActivityFunctions": 10,
      "maxConcurrentOrchestratorFunctions": 5
    }
  },
  "logging": {
    "applicationInsights": {
      "samplingSettings": {
        "isEnabled": true,
        "excludedTypes": "Request"
      }
    }
  }
}

Deliver

  • correct Functions project setup for the isolated worker model
  • clear binding and host configuration
  • middleware for cross-cutting concerns
  • Durable Functions with proper orchestration patterns
  • migration-safe guidance when upgrading execution models

Validate

  • execution model guidance is consistent (isolated only for new work)
  • orchestrator code is deterministic
  • bindings and host settings match the target runtime
  • large payloads are externalized to blob storage
  • retry policies are configured for transient failures
  • local and deployment behavior are both verified

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 Functions AI skill do?

Build, review, or migrate Azure Functions in .NET with correct execution model, isolated worker setup, bindings, DI, and Durable Functions patterns. USE FOR: working on Azure Functions in .NET; migrating from the in-process model to the isolated worker model; adding Durable Functions, bindings, or host configuration. DO NOT USE FOR: unrelated stacks; generic tasks that do not need this specific guidance. INVOKES: inspect the repository context, edit targeted files, and run relevant build, test, lint, or validation commands when changes are made.

Why use Azure Functions on TypingMind?

Because you install it once and use it with any model. Azure Functions 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 Functions in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/managedcode/dotnet-skills/tree/main/catalog/Frameworks/Azure-Functions/skills/azure-functions. 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 Functions?

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 Functions?

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

Is the Azure Functions AI skill free?

Yes. It is published on GitHub by managedcode 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇