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Golang Concurrency Patterns

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bobmatnyc
golang-concurrency-patterns

Go concurrency patterns for production services: context cancellation, errgroup, worker pools, bounded parallelism, fan-in/fan-out, and common race/deadlock pitfalls

Overview

Publisherbobmatnyc
Repositoryclaude-mpm-skills
Skill namegolang-concurrency-patterns
Stars
75
Forks
19
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Golang Concurrency Patterns 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.
  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/bobmatnyc/claude-mpm-skills.git /tmp/claude-mpm-skills
mkdir -p .claude/skills
cp -r /tmp/claude-mpm-skills/toolchains/golang/golang-concurrency-patterns .claude/skills/golang-concurrency-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Golang Concurrency Patterns 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 Golang Concurrency Patterns 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 Golang Concurrency Patterns 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.

Go Concurrency Patterns (Production)

Overview

Go concurrency scales when goroutine lifetimes are explicit, cancellation is propagated with context.Context, and shared state is protected (channels or locks). Apply these patterns to build reliable services and avoid common failure modes: goroutine leaks, deadlocks, and data races.

Quick Start

Default building blocks

  • Use context to drive cancellation and deadlines.
  • Use errgroup.WithContext for fan-out/fan-in with early abort.
  • Bound concurrency (avoid unbounded goroutines) with a semaphore or worker pool.
  • Prefer immutable data; otherwise protect shared state with a mutex or make a single goroutine the owner.

Avoid

  • Fire-and-forget goroutines in request handlers.
  • time.After inside hot loops.
  • Closing channels from the receiver side.
  • Sharing mutable variables across goroutines without synchronization.

Core Concepts

Goroutine lifecycle

Treat goroutines as resources with a clear owner and shutdown condition.

Correct: stop goroutines via context

go
ctx, cancel := context.WithCancel(context.Background())
defer cancel()

go func() {
    ticker := time.NewTicker(250 * time.Millisecond)
    defer ticker.Stop()

    for {
        select {
        case <-ctx.Done():
            return
        case <-ticker.C:
            // do work
        }
    }
}()

Wrong: goroutine without a stop condition

go
go func() {
    for {
        doWork() // leaks forever
    }
}()

Channels vs mutexes (choose intentionally)

  • Use channels to model ownership/serialization of state or to pipeline work.
  • Use mutexes to protect shared in-memory state with simple read/write patterns.

Correct: one goroutine owns the map

go
type req struct {
    key   string
    reply chan<- int
}

func mapOwner(ctx context.Context, in <-chan req) {
    m := map[string]int{}
    for {
        select {
        case <-ctx.Done():
            return
        case r := <-in:
            r.reply <- m[r.key]
        }
    }
}

Correct: mutex protects shared map

go
type SafeMap struct {
    mu sync.RWMutex
    m  map[string]int
}

func (s *SafeMap) Get(k string) (int, bool) {
    s.mu.RLock()
    defer s.mu.RUnlock()
    v, ok := s.m[k]
    return v, ok
}

Patterns

1) Fan-out/fan-in with cancellation (errgroup)

Use errgroup.WithContext to run concurrent tasks, cancel siblings on error, and wait for completion.

Correct: cancel on first error

go
g, ctx := errgroup.WithContext(ctx)

for _, id := range ids {
    id := id // capture
    g.Go(func() error {
        return process(ctx, id)
    })
}

if err := g.Wait(); err != nil {
    return err
}

Wrong: WaitGroup loses the first error and does not propagate cancellation

go
var wg sync.WaitGroup
for _, id := range ids {
    wg.Add(1)
    go func() {
        defer wg.Done()
        _ = process(context.Background(), id) // ignores caller ctx + captures id
    }()
}
wg.Wait()

2) Bounded concurrency (semaphore pattern)

Bound parallelism to prevent CPU/memory exhaustion and downstream overload.

Correct: bounded fan-out

go
limit := make(chan struct{}, 8) // max 8 concurrent
g, ctx := errgroup.WithContext(ctx)

for _, id := range ids {
    id := id
    g.Go(func() error {
        select {
        case <-ctx.Done():
            return ctx.Err()
        case limit <- struct{}{}:
        }
        defer func() { <-limit }()

        return process(ctx, id)
    })
}

return g.Wait()

3) Worker pool (durable throughput)

Use a fixed number of workers for stable throughput and predictable resource usage.

Correct: worker pool with context stop

go
type Job struct{ ID string }

func runPool(ctx context.Context, jobs <-chan Job, workers int) error {
    g, ctx := errgroup.WithContext(ctx)

    for i := 0; i < workers; i++ {
        g.Go(func() error {
            for {
                select {
                case <-ctx.Done():
                    return ctx.Err()
                case j, ok := <-jobs:
                    if !ok {
                        return nil
                    }
                    if err := handleJob(ctx, j); err != nil {
                        return err
                    }
                }
            }
        })
    }

    return g.Wait()
}

4) Pipeline stages (fan-out between stages)

Prefer one-directional channels and close only from the sending side.

Correct: sender closes

go
func stageA(ctx context.Context, out chan<- int) {
    defer close(out)
    for i := 0; i < 10; i++ {
        select {
        case <-ctx.Done():
            return
        case out <- i:
        }
    }
}

Wrong: receiver closes

go
func stageB(in <-chan int) {
    close(in) // compile error in<-chan; also wrong ownership model
}

5) Periodic work without leaks (time.Ticker vs time.After)

Use time.NewTicker for loops; avoid time.After allocations in hot paths.

Correct: ticker

go
t := time.NewTicker(1 * time.Second)
defer t.Stop()

for {
    select {
    case <-ctx.Done():
        return
    case <-t.C:
        poll()
    }
}

Wrong: time.After in loop

go
for {
    select {
    case <-ctx.Done():
        return
    case <-time.After(1 * time.Second):
        poll()
    }
}

Decision Trees

Channel vs Mutex

  • Need ownership/serialization (single writer, message passing) → use channel + owner goroutine
  • Need shared cache/map with many readers and simple updates → use RWMutex
  • Need simple counter with low contention → use atomic

WaitGroup vs errgroup

  • Need error propagation + sibling cancellation → use errgroup.WithContext
  • Need only wait and errors are handled elsewhere → use sync.WaitGroup

Buffered vs unbuffered channel

  • Need backpressure and synchronous handoff → use unbuffered
  • Need burst absorption up to a known size → use buffered (size with intent)
  • Unsure → start unbuffered and measure; add buffer only to remove known bottleneck

Testing & Verification

Race detector and flake control

Run targeted tests with the race detector and disable caching during debugging:

bash
go test -race ./...
go test -run TestName -race -count=1 ./...

Timeouts to prevent hanging tests

Correct: test-level timeout via context

go
func TestSomething(t *testing.T) {
    ctx, cancel := context.WithTimeout(context.Background(), 2*time.Second)
    defer cancel()

    if err := doThing(ctx); err != nil {
        t.Fatal(err)
    }
}

Troubleshooting

Symptom: deadlock (test hangs, goroutines blocked)

Actions:

  • Add timeouts (context.WithTimeout) around blocking operations.
  • Verify channel ownership: only the sender closes; receivers stop on ok == false.
  • Check for missing <-limit release in semaphore patterns.

Symptom: data race (go test -race reports)

Actions:

  • Identify shared variables mutated by multiple goroutines.
  • Add a mutex or convert to ownership model (single goroutine owns state).
  • Avoid writing to captured loop variables.

Symptom: goroutine leak (memory growth, slow shutdown)

Actions:

  • Ensure every goroutine selects on ctx.Done().
  • Ensure time.Ticker is stopped and channels are closed by senders.
  • Avoid context.Background() inside request paths; propagate caller context.

Resources

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 Golang Concurrency Patterns AI skill do?

Go concurrency patterns for production services: context cancellation, errgroup, worker pools, bounded parallelism, fan-in/fan-out, and common race/deadlock pitfalls

Why use Golang Concurrency Patterns on TypingMind?

Because you install it once and use it with any model. Golang Concurrency Patterns 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 Golang Concurrency Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/bobmatnyc/claude-mpm-skills/tree/main/toolchains/golang/golang-concurrency-patterns. 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 Golang Concurrency Patterns?

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 Golang Concurrency Patterns?

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

Is the Golang Concurrency Patterns AI skill free?

Yes. It is published on GitHub by bobmatnyc 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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