「chain」の検索結果

Conducts digital forensics investigations following a personal data breach, covering evidence preservation, chain of custody documentation, log analysis, scope determination, and root cause analysis. References industry-standard tools including Splunk, ELK Stack, and Wireshark. Provides forensic workflow from initial evidence collection through final investigation report. Keywords: digital forensics, breach investigation, evidence preservation, chain of custody, root cause analysis, Splunk, ELK, Wireshark.

日本語の概要は準備中です。原文の説明を表示しています。

Detect known malicious npm package versions and install-time supply-chain indicators in repositories, lockfiles, and node_modules. Use when a user mentions an npm compromise, Shai-Hulud, the Keyv/cacheable incident, suspicious preinstall scripts, credential-stealing packages, or asks whether a JavaScript project was exposed to a package supply-chain attack. Do not use as a general CVE or license audit.

日本語の概要は準備中です。原文の説明を表示しています。

Supply chain mapping, supplier dependency analysis, customer concentration, geographic concentration, bottleneck identification, supply chain risk, logistics network, sourcing strategy, inventory management, vertical integration analysis

日本語の概要は準備中です。原文の説明を表示しています。

Plan, implement, or review langchain architecture work in an existing codebase with compatibility, security, and verification controls. Use when the user explicitly requests langchain architecture work.

日本語の概要は準備中です。原文の説明を表示しています。

IOTA blockchain MCP server providing wallet management with human-in-the-loop signing, Move smart contract build/test/publish, and on-chain queries via JSON-RPC and GraphQL. Use when interacting with IOTA network from AI coding agents, managing IOTA wallets, building or publishing Move packages, querying on-chain objects or transactions, or checking epoch and network statistics.

日本語の概要は準備中です。原文の説明を表示しています。

Interact with Ethereum and EVM blockchains using ethers.js. Use when a user asks to connect to Ethereum, read blockchain data, send transactions, interact with smart contracts, or build a dApp frontend.

日本語の概要は準備中です。原文の説明を表示しています。

Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when "langgraph, langchain agent, stateful agent, agent graph, react agent, agent workflow, multi-step agent, langgraph, langchain, agents, state-machine, workflow, graph, ai-agents, orchestration" mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Comprehensive expertise in decentralized autonomous organization governance systems, including Snapshot off-chain voting, OpenZeppelin Governor on-chain execution, treasury multi-sigs, proposal lifecycles, delegation, and governance attack prevention. Use when "DAO governance, on-chain voting, Snapshot, Governor contract, governance proposal, treasury management, multi-sig, token voting, delegation, quorum, timelock, governance attack, " mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Expert in on-chain privacy technologies - ZK-SNARKs, ZK-STARKs, mixers, stealth addresses, ring signatures, and confidential transactions for building privacy-preserving blockchain applicationsUse when "privacy, zero knowledge, zk-snark, zk-stark, mixer, stealth address, ring signature, confidential transaction, tornado, private transaction, anonymous, unlinkability, privacy, zero-knowledge, zk-snarks, zk-starks, mixers, stealth-addresses, tornado-cash, zcash, confidential" mentioned.

日本語の概要は準備中です。原文の説明を表示しています。

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

日本語の概要は準備中です。原文の説明を表示しています。

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

日本語の概要は準備中です。原文の説明を表示しています。

Apply @aleabitoreddit ("Serenity")'s distilled stock-analysis method to ANY ticker, sector, or situation — critical-chokepoint / supply-chain-OSINT idea discovery, first-principles value-chain decomposition, a Buffett-style quality gate (moat / profitability / customer-replacement risk, all default unverified), and narrative-vs-fundamentals hygiene. Produces a beginner-friendly Chinese analysis (她的观点 / 小白解释 / 第一性原理 / Buffett 直接判断 / 当前结论) that classifies an idea as 研究地图 vs 可投资结论. Trigger on "analyze like Serenity / 用 aleabito 的方法分析 / 用 Serenity 框架 / critical chokepoint 分析 / 第一性原理 + Buffett 判断这只股 / supply-chain bottleneck thesis". Never emits buy/sell calls.

日本語の概要は準備中です。原文の説明を表示しています。

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

日本語の概要は準備中です。原文の説明を表示しています。

Add a new chain to an existing warp route owned by a customer. Reads a Linear ticket, adds the new chain to the deploy.yaml, builds a customer-specific strategy file for existing chains, runs warp apply, and outputs transaction files for the customer to sign via their multisig.

日本語の概要は準備中です。原文の説明を表示しています。

Foundation skill for warp-route updates. Reads the current on-chain config of a warp route via `hyperlane warp read`, enumerates every artifact (router, ProxyAdmin, ISM, hook, fee contracts) per chain, classifies each artifact's owner (auto-detects Safe / ICA / EOA; asks the user for everything else — Turnkey, Privy, MPC, custom multisig, timelock, etc.), and persists a per-artifact owner table that downstream update skills consume to know which signing path each batch needs. Read-only — no signing, no on-chain mutations.

日本語の概要は準備中です。原文の説明を表示しています。

The low-level on-chain probes for identifying what an owner address is — cast code → EOA / EIP-7702-delegated EOA / contract, VERSION() → Gnosis Safe, read-only get-owner-ica.ts derive → ICA derivation, getAccountInfo → Sealevel account existence — with how to interpret each result. The primitive layer beneath validate-owners / resolve-artifacts / update-owners, which each apply their own per-chain or per-artifact policy on top.

日本語の概要は準備中です。原文の説明を表示しています。

Expert guide for Web3 and blockchain dApp integration — viem, wagmi v2, ethers.js v6, RainbowKit, smart contract interactions, and EVM wallet state / Panduan ahli integrasi Web3 dan blockchain.

日本語の概要は準備中です。原文の説明を表示しています。

IOTA blockchain MCP server providing wallet management with human-in-the-loop signing, Move smart contract build/test/publish, and on-chain queries via JSON-RPC and GraphQL. Use when interacting with IOTA network from AI coding agents, managing IOTA wallets, building or publishing Move packages, querying on-chain objects or transactions, or checking epoch and network statistics.

日本語の概要は準備中です。原文の説明を表示しています。

Comprehensive AI/ML development guide for LangChain, LangGraph, and ML model integration in FastAPI. Use when building LLM applications, agents, RAG systems, sentiment analysis, aspect-based analysis, chain orchestration, prompt engineering, vector stores, embeddings, or integrating ML models with FastAPI endpoints. Covers LangChain patterns, LangGraph state machines, model deployment, API integration, streaming, error handling, and best practices.

日本語の概要は準備中です。原文の説明を表示しています。

This skill should be used when the user asks to "buy token", "sell token", "swap token", "trade crypto", "check trade status", "query transaction", "scan tokens", "feed", "monitor chain", "query token", "token details", "check token safety", "list wallets", "show wallets", "my wallets", "AI scan", "AI扫链", "auto scan", "smart scan", "tweet scan", "推文扫链", "twitter scan", "onboarding", "get started", "check IP", "get IP", "IP whitelist", "查IP", "IP白名单", "launch token", "create token", "发币", "创建代币", "auto sell", "stop loss", "take profit", "trailing stop", "止盈止损", "移动止损", "holders", "top holders", "kol holders", "insider", "持仓", "持仓列表", or mentions trading on Solana/ETH/BSC/Base chains via XXYY. Enables on-chain token trading and data queries through the XXYY Open API.

日本語の概要は準備中です。原文の説明を表示しています。

Build and analyze discrete or continuous Markov chains including transition matrix construction, state classification, stationary distribution computation, and mean first passage times. Use when modeling a memoryless system with observed transition counts or rates, computing long-run steady-state probabilities, determining expected hitting times or absorption probabilities, classifying states as transient or recurrent, or building a foundation for hidden Markov models or reinforcement learning MDPs.

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user runs /toolchain-health and wants a trustworthy green/yellow/red verdict on the installed toolchain with ranked repairs. Not for tasks that require source or remote-system changes.

日本語の概要は準備中です。原文の説明を表示しています。

Build AI prompt chains and multi-step workflows for complex tasks. TRIGGERS - Use when user needs help with ai-chain-builder related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Use when you need to know where a piece of reporting actually came from — the user gives an article URL and asks "where does this come from?", "who is the original source?", "is this just a rewrite?", or another skill needs the provenance chain before grading or citing a report. Follows an article's links and named attributions back to the originating vendor, government, victim or actor document and returns the chain, the primary's stated access and confidence language, publication dates, and every hop it could not resolve. Deterministic — a script or the Liberty91 platform resolves the chain; the model does not reconstruct it from memory. Invoked by /quality-of-information-check, /threat-actor-profiling, /campaign-tracking and /intelligence-writing. Works on any URL with no API key.

日本語の概要は準備中です。原文の説明を表示しています。