From onchain-arb
This skill should be used when the user wants to scan multiple trending tokens for arbitrage opportunities in bulk, or asks things like 'scan for arb opportunities', 'which trending tokens have price spreads', 'batch arbitrage check', 'find tokens with CEX-DEX price differences', '扫描套利机会', '哪些热门币有价差'.
How this skill is triggered — by the user, by Claude, or both
Slash command
/onchain-arb:scanThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Batch scan trending tokens for CEX-DEX and cross-chain arbitrage opportunities.
Batch scan trending tokens for CEX-DEX and cross-chain arbitrage opportunities.
okx-dex-token skill (trending tokens / 热门代币).${CLAUDE_PLUGIN_ROOT}/references/cex-registry.md for supported exchanges.okx-dex-market for the token's on-chain price on Ethereum (and other chains if the token is multi-chain).onchain-arb 套利扫描 (基于 onchainOS 热门代币)
扫描时间: YYYY-MM-DD HH:MM UTC
已检测交易所: OKX, Binance | 默认链: Ethereum
┌──────────┬──────────────┬──────────────┬────────┬──────────────────────┐
│ 代币 │ 最高价 │ 最低价 │ 价差% │ 价差来源 │
├──────────┼──────────────┼──────────────┼────────┼──────────────────────┤
│ TOKEN_A │ $xx.xx (OKX) │ $xx.xx (ETH) │ 2.35% │ CEX↔DEX │
│ TOKEN_B │ $xx.xx (BSC) │ $xx.xx (BIN) │ 1.80% │ 跨链 │
│ TOKEN_C │ $xx.xx (OKX) │ $xx.xx (ARB) │ 1.12% │ CEX↔DEX │
│ ... │ │ │ │ │
└──────────┴──────────────┴──────────────┴────────┴──────────────────────┘
发现 X 个代币存在 >0.5% 价差
/onchain-arb <token> for individual queries.okx-dex-token skill is unavailable, inform the user: "⚠ 无法获取热门代币列表(okx-dex-token 未安装),请使用 /onchain-arb 查询单个代币" and abort.npx claudepluginhub mastersamasama/master-plugin-repository --plugin onchain-arbSearches MemPalace before answering questions about past work, people, projects, or prior decisions. Returns verbatim stored content instead of guessing from model memory.
Guides Payload CMS config (payload.config.ts), collections, fields, hooks, access control, APIs. Debugs validation errors, security, relationships, queries, transactions, hook behavior.
Implements vector databases with Pinecone, Weaviate, Qdrant, Milvus, pgvector for semantic search, RAG, recommendations, and similarity systems. Optimizes embeddings, indexing, and hybrid search.