Distributed Systems Architecture logo

Distributed Systems Architecture

Organization
devcodex-labs
distributed-systems-architecture

分布式系统架构专家 Owner — 当任务涉及消息队列、RPC、事件驱动、跨服务一致性、分布式锁/租约、交付语义、顺序、幂等、重试、补偿、分区、背压或故障注入时使用;要求把网络与重复执行视为常态并冻结可验证的不变量。

Overview

Publisherdevcodex-labs
Repositorydevcodex
Skill namedistributed-systems-architecture
Stars
433
Forks
44
Bundled files
2
LicenseAGPL-3.0
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.

  • 2 bundled files

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

  • Open source

    Published by devcodex-labs on GitHub. Read the source before you install it.

Installation

Install the Distributed Systems Architecture 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/devcodex-labs/devcodex/tree/main/content/skills/distributed-systems-architecture
  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/devcodex-labs/devcodex.git /tmp/devcodex
mkdir -p .claude/skills
cp -r /tmp/devcodex/content/skills/distributed-systems-architecture .claude/skills/distributed-systems-architecture
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Distributed Systems Architecture 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 Distributed Systems Architecture 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 Distributed Systems Architecture 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.

Distributed Systems Architecture

职责

从系统级边界统一设计一致性、交付、顺序、幂等、重试、补偿、分区与背压。backend 负责领域流程,SRE 负责运行恢复,data 负责持久化;本 Skill 负责跨节点/跨服务语义。

DistributedSystemsArchitectureGate

字段要求
consistencyModelstrong/eventual/causal/session 等选择、读写可见性和可接受陈旧窗口
deliverySemanticsat-most-once / at-least-once / effectively-once,禁止无证据承诺 exactly-once
orderingPolicyglobal/partition/key ordering、乱序窗口、重放规则
idempotencyModelkey、scope、TTL、结果缓存、并发重复和副作用边界
retryBudgetretryable 分类、backoff/jitter、总预算、deadline 与 retry storm 防护
compensationPlantransaction/outbox/saga/补偿动作、不可补偿边界和人工恢复
partitionFailureMatrixtimeout、partial failure、lease loss、split brain、clock skew、dependency outage
backpressurePlanproducer/consumer 速率、队列上限、drop/reject/degrade、dead letter 与恢复
durableBatchOrchestration数据源耗尽、持久 cursor/checkpoint、批次节奏、多 Worker 原子聚合、durable completion、Coordinator/callback 崩溃恢复

执行流程

  1. 列出节点、服务、存储、broker 和外部系统的信任/失败边界。
  2. 冻结业务不变量与可接受一致性,而不是先选中间件。
  3. 为每条写路径定义 delivery/order/idempotency/retry/compensation。
  4. 为 lease/lock/single-active 定义丢失资格后的 fail-safe,禁止继续副作用。
  5. 建立正常、重复、乱序、延迟、丢失、分区、恢复与重放矩阵。
  6. 将可观测字段绑定 request/event/idempotency/trace/attempt/partition。
  7. 把验证映射到 unit/integration/failure-injection/load/recovery replay。

DurableBatchOrchestrationProbe

队列、批处理、导入导出或 fan-out/fan-in 流程声称“完整 run”时,必须验证:

  1. 数据源是否分页/流式推进至可证明耗尽,并持久化 cursor/checkpoint。
  2. 批次节奏、并发和背压是否有界,重试后不会重复扩大副作用。
  3. 多 Worker 的 accepted/completed/failed 计数与聚合状态是否原子、幂等且可重放。
  4. BatchRun/Batch/Job/Coordinator/Completion Event(或等价领域对象)是否持久化,进程内 callback 不作为唯一完成事实源。
  5. Coordinator、Worker、callback、broker/storage 崩溃后能从持久状态恢复并保持最终完成不变量。
  6. executable evidence 覆盖 crash、duplicate、out-of-order、dependency outage、rolling drain 和恢复后聚合。

缺少任一适用项只能 partial;一次 addBulk 或内存循环只证明入队动作,不证明 durable batch orchestration。

输出字段

consistencyModel、deliverySemantics、orderingPolicy、idempotencyModel、retryBudget、compensationPlan、partitionFailureMatrix、backpressurePlan、observabilityKeys、verificationRoute、evidenceMatrix。

反模式

  • 用“最终一致”代替陈旧窗口、冲突策略和收敛条件。
  • 宣称 exactly-once,却只做消息去重或 broker ACK。
  • 所有错误无限重试,未设 deadline、jitter 和 retry budget。
  • 分布式锁过期/续租失败后仍继续写副作用。
  • 只测试正常路径,不测重复、乱序、分区和恢复。
  • 把 Redis、Kafka 或数据库选型当作系统语义本身。

验证

至少覆盖 duplicate、out-of-order、late/lost message、consumer crash、broker/storage outage、lease loss、retry storm、poison message、partial success、replay 与恢复后不变量。

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 Distributed Systems Architecture AI skill do?

分布式系统架构专家 Owner — 当任务涉及消息队列、RPC、事件驱动、跨服务一致性、分布式锁/租约、交付语义、顺序、幂等、重试、补偿、分区、背压或故障注入时使用;要求把网络与重复执行视为常态并冻结可验证的不变量。

Why use Distributed Systems Architecture on TypingMind?

Because you install it once and use it with any model. Distributed Systems Architecture 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 Distributed Systems Architecture in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/devcodex-labs/devcodex/tree/main/content/skills/distributed-systems-architecture. 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 Distributed Systems Architecture?

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 Distributed Systems Architecture?

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

Is the Distributed Systems Architecture AI skill free?

Yes. It is published on GitHub by devcodex-labs under the AGPL-3.0 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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