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M07 Concurrency

OrganizationPopular
actionbook
m07-concurrency

CRITICAL: Use for concurrency/async. Triggers: E0277 Send Sync, cannot be sent between threads, thread, spawn, channel, mpsc, Mutex, RwLock, Atomic, async, await, Future, tokio, deadlock, race condition, 并发, 线程, 异步, 死锁

Overview

Publisheractionbook
Repositoryrust-skills
Skill namem07-concurrency
Stars
1.5K
Forks
114
Bundled files
4
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 actionbook on GitHub. Read the source before you install it.

Installation

Install the M07 Concurrency 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/actionbook/rust-skills/tree/main/skills/m07-concurrency
  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/actionbook/rust-skills.git /tmp/rust-skills
mkdir -p .claude/skills
cp -r /tmp/rust-skills/skills/m07-concurrency .claude/skills/m07-concurrency
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable M07 Concurrency 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 M07 Concurrency 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 M07 Concurrency 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.

Concurrency

Layer 1: Language Mechanics

Core Question

Is this CPU-bound or I/O-bound, and what's the sharing model?

Before choosing concurrency primitives:

  • What's the workload type?
  • What data needs to be shared?
  • What's the thread safety requirement?

Error → Design Question

ErrorDon't Just SayAsk Instead
E0277 Send"Add Send bound"Should this type cross threads?
E0277 Sync"Wrap in Mutex"Is shared access really needed?
Future not Send"Use spawn_local"Is async the right choice?
Deadlock"Reorder locks"Is the locking design correct?

Thinking Prompt

Before adding concurrency:

  1. What's the workload?

    • CPU-bound → threads (std::thread, rayon)
    • I/O-bound → async (tokio, async-std)
    • Mixed → hybrid approach
  2. What's the sharing model?

    • No sharing → message passing (channels)
    • Immutable sharing → Arc
    • Mutable sharing → Arc<Mutex> or Arc<RwLock>
  3. What are the Send/Sync requirements?

    • Cross-thread ownership → Send
    • Cross-thread references → Sync
    • Single-thread async → spawn_local

Trace Up ↑ (MANDATORY)

CRITICAL: Don't just fix the error. Trace UP to find domain constraints.

Domain Detection Table

Context KeywordsLoad Domain SkillKey Constraint
Web API, HTTP, axum, actix, handlerdomain-webHandlers run on any thread
交易, 支付, trading, paymentdomain-fintechAudit + thread safety
gRPC, kubernetes, microservicedomain-cloud-nativeDistributed tracing
CLI, terminal, clapdomain-cliUsually single-thread OK

Example: Web API + Rc Error

"Rc cannot be sent between threads" in Web API context
    ↑ DETECT: "Web API" → Load domain-web
    ↑ FIND: domain-web says "Shared state must be thread-safe"
    ↑ FIND: domain-web says "Rc in state" is Common Mistake
    ↓ DESIGN: Use Arc<T> with State extractor
    ↓ IMPL: axum::extract::State<Arc<AppConfig>>

Generic Trace

"Send not satisfied for my type"
    ↑ Ask: What domain is this? Load domain-* skill
    ↑ Ask: Does this type need to cross thread boundaries?
    ↑ Check: m09-domain (is the data model correct?)
SituationTrace ToQuestion
Send/Sync in Webdomain-webWhat's the state management pattern?
Send/Sync in CLIdomain-cliIs multi-thread really needed?
Mutex vs channelsm09-domainShared state or message passing?
Async vs threadsm10-performanceWhat's the workload profile?

Trace Down ↓

From design to implementation:

"Need parallelism for CPU work"
    ↓ Use: std::thread or rayon

"Need concurrency for I/O"
    ↓ Use: async/await with tokio

"Need to share immutable data across threads"
    ↓ Use: Arc<T>

"Need to share mutable data across threads"
    ↓ Use: Arc<Mutex<T>> or Arc<RwLock<T>>
    ↓ Or: channels for message passing

"Need simple atomic operations"
    ↓ Use: AtomicBool, AtomicUsize, etc.

Send/Sync Markers

MarkerMeaningExample
SendCan transfer ownership between threadsMost types
SyncCan share references between threadsArc<T>
!SendMust stay on one threadRc<T>
!SyncNo shared refs across threadsRefCell<T>

Quick Reference

PatternThread-SafeBlockingUse When
std::threadYesYesCPU-bound parallelism
async/awaitYesNoI/O-bound concurrency
Mutex<T>YesYesShared mutable state
RwLock<T>YesYesRead-heavy shared state
mpsc::channelYesOptionalMessage passing
Arc<Mutex<T>>YesYesShared mutable across threads

Decision Flowchart

What type of work?
├─ CPU-bound → std::thread or rayon
├─ I/O-bound → async/await
└─ Mixed → hybrid (spawn_blocking)

Need to share data?
├─ No → message passing (channels)
├─ Immutable → Arc<T>
└─ Mutable →
   ├─ Read-heavy → Arc<RwLock<T>>
   └─ Write-heavy → Arc<Mutex<T>>
   └─ Simple counter → AtomicUsize

Async context?
├─ Type is Send → tokio::spawn
├─ Type is !Send → spawn_local
└─ Blocking code → spawn_blocking

Common Errors

ErrorCauseFix
E0277 Send not satisfiedNon-Send in asyncUse Arc or spawn_local
E0277 Sync not satisfiedNon-Sync sharedWrap with Mutex
DeadlockLock orderingConsistent lock order
future is not SendNon-Send across awaitDrop before await
MutexGuard across awaitGuard held during suspendScope guard properly

Anti-Patterns

Anti-PatternWhy BadBetter
Arc<Mutex> everywhereContention, complexityMessage passing
thread::sleep in asyncBlocks executortokio::time::sleep
Holding locks across awaitBlocks other tasksScope locks tightly
Ignoring deadlock riskHard to debugLock ordering, try_lock

Async-Specific Patterns

Avoid MutexGuard Across Await

rust
// Bad: guard held across await
let guard = mutex.lock().await;
do_async().await;  // guard still held!

// Good: scope the lock
{
    let guard = mutex.lock().await;
    // use guard
}  // guard dropped
do_async().await;

Non-Send Types in Async

rust
// Rc is !Send, can't cross await in spawned task
// Option 1: use Arc instead
// Option 2: use spawn_local (single-thread runtime)
// Option 3: ensure Rc is dropped before .await

Related Skills

WhenSee
Smart pointer choicem02-resource
Interior mutabilitym03-mutability
Performance tuningm10-performance
Domain concurrency needsdomain-*

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 M07 Concurrency AI skill do?

CRITICAL: Use for concurrency/async. Triggers: E0277 Send Sync, cannot be sent between threads, thread, spawn, channel, mpsc, Mutex, RwLock, Atomic, async, await, Future, tokio, deadlock, race condition, 并发, 线程, 异步, 死锁

Why use M07 Concurrency on TypingMind?

Because you install it once and use it with any model. M07 Concurrency 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 M07 Concurrency in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/actionbook/rust-skills/tree/main/skills/m07-concurrency. 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 M07 Concurrency?

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 M07 Concurrency?

As many as you like. As long as a model supports skills, you can use M07 Concurrency with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the M07 Concurrency AI skill free?

It is published on GitHub by actionbook. Check the repository for licensing terms. 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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