AGENT SKILL

yjs-crdt-sync

暂无简介

License 许可
Apache-2.0
最近更新
2026-11-12
Stars
★ 13
Forks
2

03 SKILL.md 正文

/yjs-crdt-sync

When to Use

  • Multiple agents write to a shared config/state simultaneously without conflicts
  • Real-time collaborative agent task queues: any agent can push/pop, merges automatically
  • Offline-first: agent works disconnected, sync resumes on reconnect without data loss
  • Shared Y.Text: agents collaboratively edit a document (e.g., shared code review)

Do NOT use for

  • Strong consistency (use [[etcd-distributed-config]])
  • Append-only audit logs (use [[append-only-hypercore]])

Shared Y.Map (config sync)

import * as Y from 'yjs'
import { WebsocketProvider } from 'y-websocket'

const doc      = new Y.Doc()
const config   = doc.getMap('agent-config')

// Connect to sync server
const provider = new WebsocketProvider('ws://localhost:1234', 'yamtam-swarm', doc)

provider.on('status', ({ status }) => console.log('[yjs]', status))

// Write (merges automatically with concurrent writes from other agents)
config.set('model-tier', 'power')
config.set('max-tokens',  8000)

// Read
console.log('[yjs] tier:', config.get('model-tier'))

// Observe changes (from any agent, including remote)
config.observe((event) => {
  event.changes.keys.forEach((change, key) => {
    if (change.action === 'add' || change.action === 'update') {
      console.log(`[yjs] ${key} →`, config.get(key))
    }
  })
})

Shared Y.Array (task queue)

const taskQueue = doc.getArray('task-queue')

// Any agent can push without conflict
taskQueue.push([{ id: 'task-1', tool: 'bash', priority: 2 }])

// Pop from front (claim a task)
doc.transact(() => {
  if (taskQueue.length > 0) {
    const task = taskQueue.get(0)
    taskQueue.delete(0, 1)
    console.log('[yjs] claimed task:', task)
  }
})

// Observe additions from other agents
taskQueue.observe((event) => {
  event.changes.added.forEach(item => {
    console.log('[yjs] new task:', item.content.getContent())
  })
})

Offline sync (encode/decode state vector)

// Agent A: encode current state to share with Agent B
const stateVector  = Y.encodeStateVector(doc)
const stateAsBytes = Y.encodeStateAsUpdate(doc)

// Agent B: apply missing updates
const update = Y.encodeStateAsUpdate(docA, Y.decodeStateVector(docB))
Y.applyUpdate(docB, update)

// Both docs are now identical — order of apply doesn't matter (CRDT)

Awareness (presence: which agents are online)

import { Awareness } from 'y-protocols/awareness'

const awareness = provider.awareness

// Set local agent state
awareness.setLocalState({
  agentId: process.env.YAMTAM_AGENT_ID,
  status:  'running',
  tier:    'power',
})

// Get all online agents
awareness.on('change', () => {
  const states = [...awareness.getStates().values()]
  console.log('[yjs] online agents:', states.map(s => s.agentId))
})

Anti-Fake-Pass Checklist

❌ Not wrapping concurrent mutations in doc.transact() → multiple observers fire per sub-change
❌ Y.Array.delete() outside transaction → partial delete visible to observers mid-operation
❌ Y.Doc without unique clientID per agent → two agents collide on same clientID, merge broken
❌ WebsocketProvider without reconnect handling → disconnects silently, sync stops
❌ Comparing Y.Map values with === → values are shared types; use .toJSON() for comparison
❌ applyUpdate() without try/catch → malformed update (from untrusted peer) throws and crashes

04 同仓库更多 Skills

Horonomos-laps
Horonomos-laps/Yana-AI

openai--sentry--sentry

Use when the user asks to inspect Sentry issues or events, summarize recent production errors, or pull basic Sentry health data via the Sentry API; perform read-only queries with the bundled script and require `SENTRY_AUTH_TOKEN`.

★ 13
Horonomos-laps
Horonomos-laps/Yana-AI

ito-basket-compare

Compare Itô prediction-market baskets against a user's knowledge base, portfolio notes, financial context, watchlist, or research thesis. Use for read-only basket comparison and gap analysis without investment advice or live trading.

★ 13
Horonomos-laps
Horonomos-laps/Yana-AI

implementing-network-traffic-baselining

Build network traffic baselines from NetFlow/IPFIX data using Python pandas for statistical analysis, z-score anomaly detection, and hourly/daily traffic pattern profiling

★ 13
Horonomos-laps
Horonomos-laps/Yana-AI

detecting-evasion-techniques-in-endpoint-logs

Detects defense evasion techniques used by adversaries in endpoint logs including log tampering, timestomping, process injection, and security tool disabling. Use when investigating suspicious endpoint behavior, building detection rules for evasion tactics, or conducting threat hunting for stealthy adversary activity. Activates for requests involving evasion detection, defense evasion analysis, log tampering detection, or MITRE ATT&CK TA0005.

★ 13
Horonomos-laps
Horonomos-laps/Yana-AI

implementing-microsegmentation-with-guardicore

Implementing microsegmentation using Akamai Guardicore Segmentation to map application dependencies, create granular network policies, visualize east-west traffic flows, and enforce least-privilege communication between workloads across data centers and cloud.

★ 13
Horonomos-laps
Horonomos-laps/Yana-AI

ramdisk-tmpfs-patterns

暂无简介

★ 13