Agents can sign plugins, rotate credentials without losing identity, and publicly attest to behavior.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
End-to-end MegaETH development playbook (Feb 2026). Covers wallet operations, token swaps (Kyber Network), eth_sendRawTransactionSync (EIP-7966) for instant receipts, JSON-RPC batching, real-time mini-block subscriptions, storage-aware contract patterns (Solady RedBlackTreeLib), MegaEVM gas model, WebSocket keepalive, bridging from Ethereum, and debugging with mega-evme. Use when building on MegaETH, managing wallets, sending transactions, or deploying contracts.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Use this Skill when the user asks for:
| Network | Chain ID | RPC | Explorer |
|---|---|---|---|
| Mainnet | 4326 | https://mainnet.megaeth.com/rpc | https://mega.etherscan.io |
| Testnet | 6343 | https://carrot.megaeth.com/rpc | https://megaeth-testnet-v2.blockscout.com |
eth_sendRawTransactionSync (EIP-7966) — returns receipt in <10mseth_getTransactionReceiptaggregate3) for batching multiple eth_call requestseth_call is 2-10x faster; Multicall amortizes per-RPC overheadeth_getLogs) with fast ones in same requestNote: Earlier guidance recommended JSON-RPC batching over Multicall for caching benefits. With v2.0.14's performance improvements, Multicall is now preferred.
eth_chainId every 30 secondsminiBlocks subscription for real-time dataeth_maxPriorityFeePerGas (returns 0)eth_estimateGas (MegaEVM costs differ from standard EVM)eth_sendRawTransactionSync → done--skip-simulationAlways be explicit about:
When implementing changes, provide:
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Agents can sign plugins, rotate credentials without losing identity, and publicly attest to behavior.
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Query historical crypto market data from 0xArchive across Hyperliquid, Lighter.xyz, and HIP-3. Covers orderbooks, trades, candles, funding rates, open interest, liquidations, and data quality. Use when the user asks about crypto market data, orderbooks, trades, funding rates, or historical prices on Hyperliquid, Lighter.xyz, or HIP-3.
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Calculate ROI for AI-as-a-Service (managed AI agents). Estimates cost savings, efficiency gains, and payback period for deploying AI agents across business operations. Use when evaluating whether managed AI agents make financial sense for a company.
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Design, build, deploy, and operate production AI agent systems — single agents, multi-agent teams, and autonomous swarms. Complete methodology from agent architecture through orchestration, memory systems, safety guardrails, and operational excellence.
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Design, build, test, document, and secure production-grade APIs. Covers the full lifecycle from schema design through deployment, monitoring, and versioning. Use when designing new APIs, reviewing existing ones, generating OpenAPI specs, building test suites, or debugging production issues.
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Turn your AI agent into a business automation architect. Design, document, implement, and monitor automated workflows across sales, ops, finance, HR, and support — no n8n or Zapier required.
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