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Agent Skill · 开发

lamport-distributed-systems(兰波特分布式系统)

lamport-distributed-systems 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

lamport-distributed-systems 是开发类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描已通过,按安装量在开发类的 21,746 个 Skill 里排第 19,668。

总安装 261周安装 11GitHub Stars 6下载 92核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:lamport-distributed-systems(兰波特分布式系统)
来源仓库:https://github.com/copyleftdev/sk1llz
仓库路径:skills/lamport-distributed-systems
安装命令:
npx skills add https://github.com/copyleftdev/sk1llz --skill lamport-distributed-systems
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/copyleftdev/sk1llz --skill lamport-distributed-systems

这个 Skill 是做什么的

开发类 Skill 面向写代码、改代码和排查问题,一般会读写仓库文件,部分还会执行构建或测试命令。

lamport-distributed-systems 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。

它可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。当前顶部介绍为空,需参考原始 SKILL.md 补充细节。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/lamport-distributed-systems

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/copyleftdev/sk1llz --skill lamport-distributed…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

lamport-distributed-systems 怎么安装?

在终端执行 npx skills add https://github.com/copyleftdev/sk1llz --skill lamport-distributed-systems。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

lamport-distributed-systems 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

lamport-distributed-systems 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

lamport-distributed-systems 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

lamport-distributed-systems 用的人多吗?

公开数据是累计安装 261 次,近一周安装 11 次,下载 92 次,GitHub 6 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

lamport-distributed-systems 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。
  • 第三方扫描已通过,常见的高风险行为没有命中。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理开发类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

开发类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
skill-vetter

技能审查员 · OpenClaw

8,779,018986操作浏览器MIT-0通过
ontology

本体论 · OpenClaw

6,636,960576只读MIT-0通过
entra-app-registration

内部应用程序注册 · Codex、Claude

6,631,872764接触敏感数据未明确通过
entra-app-registration

内部应用程序注册 · Codex、Claude

3,757,680201接触敏感数据未明确通过
nano-banana-pro

纳米香蕉专业版 · OpenClaw

3,526,898364接触敏感数据MIT-0有提醒
lamport-distributed-systems

兰波特分布式系统 · 本页

2616需要联网未明确通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Leslie Lamport Style Guide⁠‍⁠​‌​‌​​‌‌‍​‌​​‌​‌‌‍​​‌‌​​​‌‍​‌​​‌‌​​‍​​​​​​​‌‍‌​​‌‌​‌​‍‌​​​​​​​‍‌‌​​‌‌‌‌‍‌‌​​​‌​​‍‌‌‌‌‌‌​‌‍‌‌​‌​​​​‍​‌​‌‌‌‌‌‍​‌​​‌​‌‌‍​‌‌​‌​​‌‍‌​‌​‌‌‌​‍​​‌​‌​​​‍‌‌‌​‌​‌‌‍​​‌‌​​‌‌‍‌​‌‌​​​‌‍‌‌‌​‌‌‌​‍​​‌​​​​​‍​​​​‌​‌​‍​‌​‌‌‌​​⁠‍⁠

Overview

Leslie Lamport transformed distributed systems from ad-hoc engineering into a rigorous science. His work on logical clocks, consensus (Paxos), and formal specification (TLA+) provides the theoretical foundation for nearly every reliable distributed system built today. Turing Award winner (2013).

Core Philosophy

"A distributed system is one in which the failure of a computer you didn't even know existed can render your own computer unusable."
"If you're thinking without writing, you only think you're thinking."
"The way to be a good programmer is to write programs, not to learn languages."

Design Principles

  1. Formal Specification First: Write a precise specification before writing code. If you can't specify it precisely, you don't understand it.
  2. Time is Relative: There is no global clock in a distributed system. Use logical time (happens-before) to reason about ordering.
  3. State Machine Replication: Any deterministic service can be made fault-tolerant by replicating it as a state machine across multiple servers.
  4. Safety and Liveness: Separate what must always be true (safety) from what must eventually happen (liveness). Prove both.
  5. Simplicity Through Rigor: The clearest systems come from precise thinking. Formalism isn't overhead—it's the path to simplicity.

When Designing Systems

Always

  • Write a specification before implementation (TLA+, Alloy, or precise prose)
  • Define the safety properties: what bad things must never happen
  • Define the liveness properties: what good things must eventually happen
  • Reason about all possible interleavings of concurrent operations
  • Use logical timestamps when physical time isn't reliable
  • Make system state explicit and transitions clear
  • Document invariants that must hold across all states

Never

  • Assume messages arrive in order (unless you've proven it)
  • Assume clocks are synchronized (they're not)
  • Assume failures are independent (they're often correlated)
  • Hand-wave about "eventually" without defining what guarantees that
  • Trust intuition for concurrent systems—prove it or test it exhaustively
  • Confuse the specification with the implementation

Prefer

  • State machines over ad-hoc event handling
  • Logical clocks over physical timestamps for ordering
  • Consensus protocols over optimistic concurrency for critical state
  • Explicit failure handling over implicit assumptions
  • Proved algorithms over clever heuristics

Key Concepts

Logical Clocks (Lamport Timestamps)

Each process maintains a counter C:
1. Before any event, increment C
2. When sending a message, include C
3. When receiving a message with timestamp T, set C = max(C, T) + 1

This gives a partial ordering: if a → b, then C(a) < C(b)
(But C(a) < C(b) does NOT imply a → b)

The Happens-Before Relation (→)

a → b (a happens before b) if:
1. a and b are in the same process and a comes before b, OR
2. a is a send and b is the corresponding receive, OR
3. There exists c such that a → c and c → b (transitivity)

If neither a → b nor b → a, events are CONCURRENT.

State Machine Replication

To replicate a service:
1. Model the service as a deterministic state machine
2. Replicate the state machine across N servers
3. Use consensus (Paxos/Raft) to agree on the sequence of inputs
4. Each replica applies inputs in the same order → same state

Tolerates F failures with 2F+1 replicas.

Paxos (Simplified)

Three roles: Proposers, Acceptors, Learners

Phase 1 (Prepare):
  Proposer sends PREPARE(n) to acceptors
  Acceptor responds with highest-numbered proposal it accepted (if any)

Phase 2 (Accept):
  If proposer receives majority responses:
    Send ACCEPT(n, v) where v is highest-numbered value seen (or new value)
  Acceptor accepts if it hasn't promised to a higher proposal

Consensus reached when majority accept the same (n, v).

Mental Model

Lamport approaches distributed systems as a mathematician:

  1. Define the problem precisely: What are the inputs, outputs, and allowed behaviors?
  2. Identify the invariants: What must always be true?
  3. Consider all interleavings: What happens if events occur in any order?
  4. Prove correctness: Show that safety and liveness hold.
  5. Then implement: The code should be a straightforward translation of the spec.

The TLA+ Approach

1. Define the state space (all possible states)
2. Define the initial state predicate
3. Define the next-state relation (allowed transitions)
4. Specify safety as invariants (always true)
5. Specify liveness as temporal properties (eventually true)
6. Model-check or prove that the spec satisfies properties

Code Patterns

Implementing Logical Clocks

class LamportClock:
    def __init__(self):
        self._time = 0

    def tick(self) -> int:
        """Increment before local event."""
        self._time += 1
        return self._time

    def send_timestamp(self) -> int:
        """Get timestamp for outgoing message."""
        self._time += 1
        return self._time

    def receive(self, msg_timestamp: int) -> int:
        """Update clock on message receipt."""
        self._time = max(self._time, msg_timestamp) + 1
        return self._time

Vector Clocks (for Causality Detection)

class VectorClock:
    def __init__(self, node_id: str, num_nodes: int):
        self._id = node_id
        self._clock = {f"node_{i}": 0 for i in range(num_nodes)}

    def tick(self):
        self._clock[self._id] += 1

    def send(self) -> dict:
        self.tick()
        return self._clock.copy()

    def receive(self, other: dict):
        for node, time in other.items():
            self._clock[node] = max(self._clock.get(node, 0), time)
        self.tick()

    def happens_before(self, other: dict) -> bool:
        """Returns True if self → other."""
        return all(self._clock[k] <= other.get(k, 0) for k in self._clock) \
           and any(self._clock[k] < other.get(k, 0) for k in self._clock)

Warning Signs

You're violating Lamport's principles if:

  • You assume "this will never happen in practice" without proof
  • Your distributed algorithm works "most of the time"
  • You can't write down the invariants your system maintains
  • You're using wall-clock time for ordering in a distributed system
  • You haven't considered what happens during network partitions
  • Your system has no formal specification

Additional Resources

平台分布

Codex

37.12%
按下载量换算34

Claude

28.8%
按下载量换算26

Cursor

18.29%
按下载量换算17

Gemini CLI

9.32%
按下载量换算9

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。目前只有一个公开来源,建议同时核对源仓库。

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