csharp-concurrency-patterns

Q: How do I install csharp-concurrency-patterns?

Run npx skills add https://github.com/wshaddix/dotnet-skills --skill csharp-concurrency-patterns in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/csharp-concurrency-patterns/ (personal) or .claude/skills/csharp-concurrency-patterns/ (project).

by wshaddixBackend & APIs

Choosing the right concurrency abstraction in .NET - from async/await for I/O to Channels for producer/consumer to Akka.NET for stateful entity management. Covers both high-level abstractions and low-level synchronization primitives. Use when deciding how to handle concurrent operations in .NET, evaluating whether to use async/await, Channels, or Akka.NET, or managing state across multiple concurrent entities.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/wshaddix/dotnet-skills --skill csharp-concurrency-patterns

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads csharp-concurrency-patterns automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

.NET Concurrency: Choosing the Right Tool

When to Use This Skill

Use this skill when:

Deciding how to handle concurrent operations in .NET
Evaluating whether to use async/await, Channels, Akka.NET, or other abstractions
Tempted to use locks, semaphores, or other synchronization primitives
Need to process streams of data with backpressure, batching, or debouncing
Managing state across multiple concurrent entities

The Philosophy

Start simple, escalate only when needed.

Most concurrency problems can be solved with async/await. Only reach for more sophisticated tools when you have a specific need that async/await can't address cleanly.

Try to avoid shared mutable state. The best way to handle concurrency is to design it away. Immutable data, message passing, and isolated state (like actors) eliminate entire categories of bugs.

Locks should be the exception, not the rule. When you can't avoid shared mutable state, using a lock occasionally isn't the end of the world. But if you find yourself reaching for lock, SemaphoreSlim, or other synchronization primitives regularly, step back and reconsider your design.

When you truly need shared mutable state:

First choice: Redesign to avoid it (immutability, message passing, actor isolation)
Second choice: Use System.Collections.Concurrent (ConcurrentDictionary, ConcurrentQueue, etc.)
Third choice: Use Channel<T> to serialize access through message passing
Last resort: Use lock for simple, short-lived critical sections

Decision Tree

What are you trying to do?
│
├─► Wait for I/O (HTTP, database, file)?
│   └─► Use async/await
│
├─► Process a collection in parallel (CPU-bound)?
│   └─► Use Parallel.ForEachAsync
│
├─► Producer/consumer pattern (work queue)?
│   └─► Use System.Threading.Channels
│
├─► UI event handling (debounce, throttle, combine)?
│   └─► Use Reactive Extensions (Rx)
│
├─► Server-side stream processing (backpressure, batching)?
│   └─► Use Akka.NET Streams
│
├─► State machines with complex transitions?
│   └─► Use Akka.NET Actors (Become pattern)
│
├─► Manage state for many independent entities?
│   └─► Use Akka.NET Actors (entity-per-actor)
│
├─► Coordinate multiple async operations?
│   └─► Use Task.WhenAll / Task.WhenAny
│
├─► Need to protect shared mutable state with synchronization?
│   └─► Is the shared state a single scalar (int, long, reference)?
│       YES -> Use Interlocked (lock-free, lowest overhead)
│
│       Is the shared state a key-value lookup or queue?
│       YES -> Use ConcurrentDictionary / ConcurrentQueue (thread-safe by design)
│
│       Does the critical section contain `await`?
│       YES -> Use SemaphoreSlim (async-compatible via WaitAsync)
│       NO  -> Does the critical section need many readers, few writers?
│                YES -> Use ReaderWriterLockSlim (only if profiling shows lock contention)
│                NO  -> Use lock (simplest, lowest cognitive overhead)
│
│       Is the critical section extremely short (< 100 ns) with high contention?
│       YES -> Consider SpinLock (advanced, measure first)
│
└─► None of the above fits?
    └─► Ask yourself: "Do I really need shared mutable state?"
        ├─► Yes -> Consider redesigning to avoid it
        └─► Truly unavoidable -> Use Channels or Actors to serialize access

Level 1: async/await (Default Choice)

Use for: I/O-bound operations, non-blocking waits, most everyday concurrency.

public async Task<Order> GetOrderAsync(string orderId, CancellationToken ct)
{
    var order = await _database.GetAsync(orderId, ct);
    var customer = await _customerService.GetAsync(order.CustomerId, ct);
    return order with { Customer = customer };
}

public async Task<Dashboard> LoadDashboardAsync(string userId, CancellationToken ct)
{
    var ordersTask = _orderService.GetRecentOrdersAsync(userId, ct);
    var notificationsTask = _notificationService.GetUnreadAsync(userId, ct);
    var statsTask = _statsService.GetUserStatsAsync(userId, ct);

    await Task.WhenAll(ordersTask, notificationsTask, statsTask);

    return new Dashboard(
        Orders: await ordersTask,
        Notifications: await notificationsTask,
        Stats: await statsTask);
}

Key principles:

Always accept CancellationToken
Use ConfigureAwait(false) in library code
Don't block on async code (no .Result or .Wait())

Level 2: Parallel.ForEachAsync (CPU-Bound Parallelism)

Use for: Processing collections in parallel when work is CPU-bound or you need controlled concurrency.

public async Task ProcessOrdersAsync(
    IEnumerable<Order> orders,
    CancellationToken ct)
{
    await Parallel.ForEachAsync(
        orders,
        new ParallelOptions
        {
            MaxDegreeOfParallelism = Environment.ProcessorCount,
            CancellationToken = ct
        },
        async (order, token) =>
        {
            await ProcessOrderAsync(order, token);
        });
}

public async Task<IReadOnlyList<ProcessedImage>> ProcessImagesAsync(
    IEnumerable<string> imagePaths,
    CancellationToken ct)
{
    var results = new ConcurrentBag<ProcessedImage>();

    await Parallel.ForEachAsync(
        imagePaths,
        new ParallelOptions { MaxDegreeOfParallelism = 4, CancellationToken = ct },
        async (path, token) =>
        {
            var image = await File.ReadAllBytesAsync(path, token);
            var processed = ProcessImage(image);
            results.Add(processed);
        });

    return results.ToList();
}

When NOT to use:

Pure I/O operations (async/await is sufficient)
When order matters (Parallel doesn't preserve order)
When you need backpressure or flow control

Level 3: System.Threading.Channels (Producer/Consumer)

Use for: Work queues, producer/consumer patterns, decoupling producers from consumers, simple stream-like processing.

public class OrderProcessor
{
    private readonly Channel<Order> _channel;

    public OrderProcessor()
    {
        _channel = Channel.CreateBounded<Order>(new BoundedChannelOptions(100)
        {
            FullMode = BoundedChannelFullMode.Wait
        });
    }

    public async Task EnqueueOrderAsync(Order order, CancellationToken ct)
    {
        await _channel.Writer.WriteAsync(order, ct);
    }

    public async Task ProcessOrdersAsync(CancellationToken ct)
    {
        await foreach (var order in _channel.Reader.ReadAllAsync(ct))
        {
            await ProcessOrderAsync(order, ct);
        }
    }

    public void Complete() => _channel.Writer.Complete();
}

public class WorkerPool
{
    private readonly Channel<WorkItem> _channel;
    private readonly List<Task> _workers = new();

    public WorkerPool(int workerCount)
    {
        _channel = Channel.CreateUnbounded<WorkItem>();

        for (int i = 0; i < workerCount; i++)
        {
            _workers.Add(Task.Run(() => ConsumeAsync()));
        }
    }

    private async Task ConsumeAsync()
    {
        await foreach (var item in _channel.Reader.ReadAllAsync())
        {
            await ProcessAsync(item);
        }
    }

    public ValueTask EnqueueAsync(WorkItem item)
        => _channel.Writer.WriteAsync(item);
}

Channels are good for:

Decoupling producer speed from consumer speed
Buffering work with backpressure
Simple fan-out to multiple workers
Background processing queues

Channels are NOT good for:

Complex stream operations (batching, windowing, merging)
Stateful processing per entity
When you need sophisticated error handling/supervision

Level 4: Akka.NET Streams (Complex Stream Processing)

Use for: Backpressure, batching, debouncing, throttling, merging streams, complex transformations.

using Akka.Streams;
using Akka.Streams.Dsl;

public Source<IReadOnlyList<Event>, NotUsed> BatchEvents(
    Source<Event, NotUsed> events)
{
    return events
        .GroupedWithin(100, TimeSpan.FromSeconds(1))
        .Select(batch => batch.ToList() as IReadOnlyList<Event>);
}

public Source<Request, NotUsed> ThrottleRequests(
    Source<Request, NotUsed> requests)
{
    return requests
        .Throttle(10, TimeSpan.FromSeconds(1), 5, ThrottleMode.Shaping);
}

public Source<ProcessedItem, NotUsed> ProcessWithParallelism(
    Source<Item, NotUsed> items)
{
    return items
        .SelectAsync(4, async item => await ProcessAsync(item));
}

public IRunnableGraph<Task<Done>> CreatePipeline(
    Source<RawEvent, NotUsed> events,
    Sink<ProcessedEvent, Task<Done>> sink)
{
    return events
        .Where(e => e.IsValid)
        .GroupedWithin(50, TimeSpan.FromMilliseconds(500))
        .SelectAsync(4, batch => ProcessBatchAsync(batch))
        .SelectMany(results => results)
        .ToMaterialized(sink, Keep.Right);
}

Level 4b: Reactive Extensions (UI and Event Composition)

Use for: UI event handling, composing event streams, time-based operations in client applications.

using System.Reactive.Linq;

public class SearchViewModel
{
    public SearchViewModel(ISearchService searchService)
    {
        SearchResults = SearchText
            .Throttle(TimeSpan.FromMilliseconds(300))
            .DistinctUntilChanged()
            .Where(text => text.Length >= 3)
            .SelectMany(text => searchService.SearchAsync(text).ToObservable())
            .ObserveOn(RxApp.MainThreadScheduler);
    }

    public IObservable<string> SearchText { get; }
    public IObservable<IList<SearchResult>> SearchResults { get; }
}

public IObservable<bool> CanSubmit =>
    Observable.CombineLatest(
        UsernameValid,
        PasswordValid,
        EmailValid,
        (user, pass, email) => user && pass && email);

public IObservable<Point> DoubleClicks =>
    MouseClicks
        .Buffer(TimeSpan.FromMilliseconds(300))
        .Where(clicks => clicks.Count >= 2)
        .Select(clicks => clicks.Last());

public IDisposable AutoSave =>
    DocumentChanges
        .Throttle(TimeSpan.FromSeconds(2))
        .Subscribe(async doc => await SaveAsync(doc));

Rx vs Akka.NET Streams:

Scenario	Rx	Akka.NET Streams
UI events	Best choice	Overkill
Client-side composition	Best choice	Overkill
Server-side pipelines	Works but limited	Better backpressure
Distributed processing	Not designed for	Built for this
Hot observables	Native support	Requires more setup

Level 5: Akka.NET Actors (Stateful Concurrency)

Use for: Managing state for multiple entities, state machines, push-based updates, complex coordination, supervision and fault tolerance.

Entity-Per-Actor Pattern

public class OrderActor : ReceiveActor
{
    private OrderState _state;

    public OrderActor(string orderId)
    {
        _state = new OrderState(orderId);

        Receive<AddItem>(msg =>
        {
            _state = _state.AddItem(msg.Item);
            Sender.Tell(new ItemAdded(msg.Item));
        });

        Receive<Checkout>(msg =>
        {
            if (_state.CanCheckout)
            {
                _state = _state.Checkout();
                Sender.Tell(new CheckoutSucceeded(_state.Total));
            }
            else
            {
                Sender.Tell(new CheckoutFailed("Cart is empty"));
            }
        });

        Receive<GetState>(_ => Sender.Tell(_state));
    }
}

State Machines with Become

public class PaymentActor : ReceiveActor
{
    private PaymentData _payment;

    public PaymentActor(string paymentId)
    {
        _payment = new PaymentData(paymentId);
        Pending();
    }

    private void Pending()
    {
        Receive<AuthorizePayment>(msg =>
        {
            _payment = _payment with { Amount = msg.Amount };
            Become(Authorizing);
            Self.Tell(new ProcessAuthorization());
        });

        Receive<CancelPayment>(_ =>
        {
            Become(Cancelled);
            Sender.Tell(new PaymentCancelled(_payment.Id));
        });
    }

    private void Authorizing()
    {
        Receive<ProcessAuthorization>(async _ =>
        {
            var result = await _gateway.AuthorizeAsync(_payment);
            if (result.Success)
            {
                _payment = _payment with { AuthCode = result.AuthCode };
                Become(Authorized);
            }
            else
            {
                Become(Failed);
            }
        });

        Receive<CancelPayment>(_ =>
        {
            Sender.Tell(new PaymentError("Cannot cancel during authorization"));
        });
    }

    private void Authorized()
    {
        Receive<CapturePayment>(_ =>
        {
            Become(Capturing);
            Self.Tell(new ProcessCapture());
        });

        Receive<VoidPayment>(_ =>
        {
            Become(Voiding);
            Self.Tell(new ProcessVoid());
        });
    }

    private void Capturing() { }
    private void Voiding() { }
    private void Cancelled() { }
    private void Failed() { }
}

Synchronization Primitives

When you must use shared mutable state, choose the simplest primitive that meets the requirement.

Quick Reference Table

Primitive	Async-Safe	Reentrant	Use Case
`lock` / `Monitor`	No	Yes (same thread)	Short critical sections without `await`
`SemaphoreSlim`	Yes (`WaitAsync`)	No	Async-compatible mutual exclusion, throttling
`Interlocked`	N/A (lock-free)	N/A	Atomic scalar operations (increment, compare-exchange)
`ConcurrentDictionary<K,V>`	N/A (thread-safe)	N/A	Thread-safe key-value cache/lookup
`ConcurrentQueue<T>`	N/A (thread-safe)	N/A	Thread-safe FIFO queue
`ReaderWriterLockSlim`	No	Optional (`LockRecursionPolicy`)	Many-readers/few-writers (profile-driven only)
`SpinLock`	No	No	Ultra-short critical sections under extreme contention

lock and Monitor

public sealed class Counter
{
    private readonly object _lock = new();
    private int _count;

    public void Increment()
    {
        lock (_lock)
        {
            _count++;
        }
    }

    public int GetCount()
    {
        lock (_lock)
        {
            return _count;
        }
    }
}

Lock Object Rules:

Use a private, dedicated object field
Never lock on this
Never lock on typeof(T)
Never lock on string literals
Never lock on value types

SemaphoreSlim

The only built-in .NET synchronization primitive that supports await:

public sealed class AsyncCache
{
    private readonly SemaphoreSlim _semaphore = new(1, 1);
    private readonly Dictionary<string, object> _cache = new();

    public async Task<T> GetOrAddAsync<T>(string key,
        Func<CancellationToken, Task<T>> factory,
        CancellationToken ct = default)
    {
        await _semaphore.WaitAsync(ct);
        try
        {
            if (_cache.TryGetValue(key, out var existing))
                return (T)existing;

            var value = await factory(ct);
            _cache[key] = value!;
            return value;
        }
        finally
        {
            _semaphore.Release();
        }
    }
}

Interlocked Operations

Lock-free atomic operations for scalar values:

private int _counter;
private long _totalBytes;
private object? _current;

Interlocked.Increment(ref _counter);
Interlocked.Decrement(ref _counter);
Interlocked.Add(ref _totalBytes, bytesRead);
var previous = Interlocked.Exchange(ref _current, newValue);
var original = Interlocked.CompareExchange(ref _counter, newValue: 10, comparand: 0);

ConcurrentDictionary

private readonly ConcurrentDictionary<int, Widget> _cache = new();

var widget = _cache.GetOrAdd(id, key => LoadWidget(key));
var updated = _cache.AddOrUpdate(id,
    addValueFactory: key => CreateDefault(key),
    updateValueFactory: (key, existing) => existing with { LastAccessed = DateTime.UtcNow });

if (_cache.TryRemove(id, out var removed))
{
}

Important: GetOrAdd factory delegates may execute multiple times under contention. Use Lazy<T> wrapping for exactly-once semantics.

Anti-Patterns: What to Avoid

Locks for Business Logic

// BAD: Using locks to protect shared state
private readonly object _lock = new();
private Dictionary<string, Order> _orders = new();

public void UpdateOrder(string id, Action<Order> update)
{
    lock (_lock)
    {
        if (_orders.TryGetValue(id, out var order))
        {
            update(order);
        }
    }
}

// GOOD: Use an actor or Channel to serialize access

Blocking in Async Code

// BAD: Blocking on async
var result = GetDataAsync().Result;
GetDataAsync().Wait();

// GOOD: Async all the way
var result = await GetDataAsync();

Shared Mutable State Without Protection

// BAD: Multiple tasks mutating shared state
var results = new List<Result>();
await Parallel.ForEachAsync(items, async (item, ct) =>
{
    var result = await ProcessAsync(item, ct);
    results.Add(result); // Race condition!
});

// GOOD: Use ConcurrentBag or collect results differently
var results = new ConcurrentBag<Result>();

Do not use `lock` inside `async` methods

lock is thread-affine; the continuation after await may resume on a different thread, causing SynchronizationLockException. Use SemaphoreSlim.WaitAsync instead.

Prefer Async Local Functions

Use async local functions instead of Task.Run(async () => ...) or ContinueWith():

private void HandleCommand(MyCommand cmd)
{
    async Task<WorkCompleted> ExecuteAsync()
    {
        var result = await DoWorkAsync();
        return new WorkCompleted(result);
    }

    ExecuteAsync().PipeTo(Self);
}

Quick Reference: Which Tool When?

Need	Tool	Example
Wait for I/O	`async/await`	HTTP calls, database queries
Parallel CPU work	`Parallel.ForEachAsync`	Image processing, calculations
Work queue	`Channel<T>`	Background job processing
UI events with debounce/throttle	Reactive Extensions	Search-as-you-type, auto-save
Server-side batching/throttling	Akka.NET Streams	Event aggregation, rate limiting
State machines	Akka.NET Actors	Payment flows, order lifecycles
Entity state management	Akka.NET Actors	Order management, user sessions
Fire multiple async ops	`Task.WhenAll`	Loading dashboard data
Race multiple async ops	`Task.WhenAny`	Timeout with fallback
Periodic work	`PeriodicTimer`	Health checks, polling
Protect single scalar	`Interlocked`	Counters, flags
Protect key-value state	`ConcurrentDictionary`	Caches, lookups
Async-compatible mutex	`SemaphoreSlim`	Async critical sections
Simple synchronous mutex	`lock`	Short critical sections without `await`

The Escalation Path

async/await (start here)
    │
    ├─► Need parallelism? → Parallel.ForEachAsync
    │
    ├─► Need producer/consumer? → Channel<T>
    │
    ├─► Need UI event composition? → Reactive Extensions
    │
    ├─► Need server-side stream processing? → Akka.NET Streams
    │
    └─► Need state machines or entity management? → Akka.NET Actors

Only escalate when you have a concrete need. Don't reach for actors or streams "just in case" - start with async/await and move up only when the simpler approach doesn't fit.

References

FAQ

What does the csharp-concurrency-patterns skill do?

How do I install csharp-concurrency-patterns?

Run npx skills add https://github.com/wshaddix/dotnet-skills --skill csharp-concurrency-patterns in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/csharp-concurrency-patterns/ (personal) or .claude/skills/csharp-concurrency-patterns/ (project).

Which AI agents can use csharp-concurrency-patterns?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is csharp-concurrency-patterns safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains csharp-concurrency-patterns?

It is published by wshaddix in wshaddix/dotnet-skills. It has 87 recorded installs.

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csharp-concurrency-patterns

Install

Install

Describe the task

Review the result

SKILL.md

.NET Concurrency: Choosing the Right Tool

When to Use This Skill

The Philosophy

Decision Tree

Level 1: async/await (Default Choice)

Level 2: Parallel.ForEachAsync (CPU-Bound Parallelism)

Level 3: System.Threading.Channels (Producer/Consumer)

Level 4: Akka.NET Streams (Complex Stream Processing)

Level 4b: Reactive Extensions (UI and Event Composition)

Level 5: Akka.NET Actors (Stateful Concurrency)

Entity-Per-Actor Pattern

State Machines with Become

Synchronization Primitives

Quick Reference Table

lock and Monitor

SemaphoreSlim

Interlocked Operations

ConcurrentDictionary

Anti-Patterns: What to Avoid

Locks for Business Logic

Blocking in Async Code

Shared Mutable State Without Protection

Do not use lock inside async methods

Prefer Async Local Functions

Quick Reference: Which Tool When?

The Escalation Path

References

FAQ

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Do not use `lock` inside `async` methods