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lamport-formal-distributed兰波特正式分布式

Agent Skill

lamport-formal-distributed 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。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。

总安装

240

周安装

10

GitHub Stars

6

下载量

80
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安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

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请帮我安装这个 Agent Skill:lamport-formal-distributed(兰波特正式分布式)
来源仓库:https://github.com/copyleftdev/sk1llz
仓库路径:skills/lamport-formal-distributed
安装命令:
npx skills add https://github.com/copyleftdev/sk1llz --skill lamport-formal-distributed
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。

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

简介

lamport-formal-distributed 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。

  • 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。

SKILL.md

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

Overview

Leslie Lamport is a Turing Award winner who invented logical clocks, the Paxos consensus algorithm, TLA+ specification language, and LaTeX. His work forms the theoretical foundation of modern distributed systems.

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 get correct programs is to first get something that is obviously correct and then make it efficient."

Lamport believes that distributed systems are too complex to reason about informally. Formal specification isn't optional—it's essential.

Design Principles

  1. Formal Specification First: Write the spec before the code.
  2. Time Is Logical, Not Physical: Use happens-before, not wall clocks.
  3. Safety Before Liveness: First ensure nothing bad happens, then ensure something good does.
  4. State Machines: Model systems as state machines for clarity.

When Writing Code

Always

  • Write a formal specification (TLA+ or similar)
  • Define safety and liveness properties explicitly
  • Use logical timestamps for ordering events
  • Model failures as part of the specification
  • Prove correctness before implementing
  • Consider all interleavings

Never

  • Assume reliable networks
  • Rely on synchronized clocks
  • Ignore failure modes
  • Test into correctness (testing finds bugs, not proves absence)
  • Hand-wave about "eventual consistency"

Prefer

  • State machine specifications
  • Logical clocks over physical clocks
  • Consensus protocols over ad-hoc coordination
  • Formal proofs over informal arguments
  • Explicit failure handling

Code Patterns

Logical Clocks (Lamport Timestamps)

# Lamport's logical clock: happens-before ordering

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

    def tick(self):
        """Local event: increment clock"""
        self.time += 1
        return self.time

    def send(self):
        """Send message: increment and return timestamp"""
        self.time += 1
        return self.time

    def receive(self, msg_timestamp):
        """Receive message: max(local, received) + 1"""
        self.time = max(self.time, msg_timestamp) + 1
        return self.time

# Usage:
# Process A: clock.tick() -> 1, clock.send() -> 2
# Process B: clock.receive(2) -> 3
# Now we know: A's event 2 happened-before B's event 3

Vector Clocks (Causal Ordering)

# Vector clocks: detect concurrent events

class VectorClock:
    def __init__(self, node_id, num_nodes):
        self.node_id = node_id
        self.clock = [0] * num_nodes

    def tick(self):
        """Local event"""
        self.clock[self.node_id] += 1
        return self.clock.copy()

    def send(self):
        """Send: increment own component"""
        self.clock[self.node_id] += 1
        return self.clock.copy()

    def receive(self, other_clock):
        """Receive: element-wise max, then increment own"""
        for i in range(len(self.clock)):
            self.clock[i] = max(self.clock[i], other_clock[i])
        self.clock[self.node_id] += 1
        return self.clock.copy()

    @staticmethod
    def compare(vc1, vc2):
        """Compare: <, >, =, or concurrent"""
        less = all(a <= b for a, b in zip(vc1, vc2))
        greater = all(a >= b for a, b in zip(vc1, vc2))

        if less and not greater:
            return "before"  # vc1 happened-before vc2
        elif greater and not less:
            return "after"   # vc1 happened-after vc2
        elif less and greater:
            return "equal"
        else:
            return "concurrent"  # Neither happened-before the other

TLA+ Specification

--------------------------- MODULE SimpleConsensus ---------------------------
EXTENDS Integers, FiniteSets

CONSTANTS Nodes, Values

VARIABLES
    proposed,   \* proposed[n] = value proposed by node n
    decided     \* decided[n] = value decided by node n (or null)

TypeInvariant ==
    /\ proposed \in [Nodes -> Values \union {NULL}]
    /\ decided \in [Nodes -> Values \union {NULL}]

\* Safety: Agreement - all decided values are the same
Agreement ==
    \A n1, n2 \in Nodes:
        (decided[n1] # NULL /\ decided[n2] # NULL) =>
            decided[n1] = decided[n2]

\* Safety: Validity - decided value was proposed
Validity ==
    \A n \in Nodes:
        decided[n] # NULL =>
            \E m \in Nodes: proposed[m] = decided[n]

\* Liveness: Termination - eventually all decide
Termination ==
    <>(\A n \in Nodes: decided[n] # NULL)

Init ==
    /\ proposed = [n \in Nodes |-> NULL]
    /\ decided = [n \in Nodes |-> NULL]

Propose(n, v) ==
    /\ proposed[n] = NULL
    /\ proposed' = [proposed EXCEPT ![n] = v]
    /\ UNCHANGED decided

Decide(n, v) ==
    /\ decided[n] = NULL
    /\ \E m \in Nodes: proposed[m] = v
    /\ decided' = [decided EXCEPT ![n] = v]
    /\ UNCHANGED proposed

Next ==
    \E n \in Nodes, v \in Values:
        Propose(n, v) \/ Decide(n, v)

Spec == Init /\ [][Next]_<<proposed, decided>>
==============================================================================

Paxos Simplified

# Simplified Paxos - single decree

class PaxosNode:
    def __init__(self, node_id, nodes):
        self.node_id = node_id
        self.nodes = nodes

        # Acceptor state
        self.promised = 0      # Highest proposal number promised
        self.accepted_num = 0  # Number of accepted proposal
        self.accepted_val = None

    def prepare(self, proposal_num):
        """Phase 1a: Proposer sends prepare"""
        responses = []
        for node in self.nodes:
            resp = node.handle_prepare(proposal_num)
            if resp:
                responses.append(resp)
        return responses

    def handle_prepare(self, proposal_num):
        """Phase 1b: Acceptor handles prepare"""
        if proposal_num > self.promised:
            self.promised = proposal_num
            return {
                'promised': True,
                'accepted_num': self.accepted_num,
                'accepted_val': self.accepted_val
            }
        return {'promised': False}

    def accept(self, proposal_num, value):
        """Phase 2a: Proposer sends accept"""
        responses = []
        for node in self.nodes:
            resp = node.handle_accept(proposal_num, value)
            responses.append(resp)
        return responses

    def handle_accept(self, proposal_num, value):
        """Phase 2b: Acceptor handles accept"""
        if proposal_num >= self.promised:
            self.promised = proposal_num
            self.accepted_num = proposal_num
            self.accepted_val = value
            return {'accepted': True}
        return {'accepted': False}

    def propose(self, value):
        """Full Paxos proposal"""
        n = self.generate_proposal_number()

        # Phase 1: Prepare
        promises = self.prepare(n)
        if len([p for p in promises if p['promised']]) <= len(self.nodes) // 2:
            return None  # Failed to get majority

        # Use highest accepted value if any
        highest = max(promises, key=lambda p: p['accepted_num'])
        if highest['accepted_val'] is not None:
            value = highest['accepted_val']

        # Phase 2: Accept
        accepts = self.accept(n, value)
        if len([a for a in accepts if a['accepted']]) > len(self.nodes) // 2:
            return value  # Consensus reached!

        return None

State Machine Replication

# State machine replication: the foundation of distributed consensus

class ReplicatedStateMachine:
    """
    Key insight: if all replicas start in the same state
    and apply the same operations in the same order,
    they will end up in the same state.
    """

    def __init__(self):
        self.state = {}
        self.log = []        # Ordered log of operations
        self.commit_index = 0

    def apply(self, operation):
        """Apply operation to state machine"""
        op_type = operation['type']

        if op_type == 'set':
            self.state[operation['key']] = operation['value']
        elif op_type == 'delete':
            self.state.pop(operation['key'], None)
        elif op_type == 'increment':
            key = operation['key']
            self.state[key] = self.state.get(key, 0) + 1

    def append_to_log(self, operation):
        """Add operation to log (not yet applied)"""
        self.log.append(operation)
        return len(self.log) - 1  # Return log index

    def commit(self, index):
        """Apply all operations up to index"""
        while self.commit_index <= index:
            self.apply(self.log[self.commit_index])
            self.commit_index += 1

    # The consensus protocol ensures all replicas
    # have the same log in the same order

Mental Model

Lamport approaches distributed systems by asking:

  1. What is the state? Define the state machine precisely
  2. What are the safety properties? What must never happen?
  3. What are the liveness properties? What must eventually happen?
  4. How do we order events? Logical time, not physical time
  5. Can I specify this formally? If not, I don't understand it

Signature Lamport Moves

  • TLA+ specifications before implementation
  • Logical clocks for event ordering
  • Safety and liveness as separate concerns
  • State machine replication for consensus
  • Happens-before reasoning
  • Formal proofs of correctness

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平台分布

Codex

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按下载量换算29

Claude

32.36%
按下载量换算26

Cursor

19.33%
按下载量换算15

Gemini CLI

8.46%
按下载量换算7

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