「integration」の検索結果

Use when user asks to "build a Solana dapp", "write an Anchor program", "create a token", "debug Solana errors", "set up wallet connection", "test my Solana program", "deploy to devnet", or "explain Solana concepts" (rent, accounts, PDAs, CPIs, etc.). Also use for quick on-chain lookups via public RPC + curl — "what's the balance of <wallet>", "look up transaction <sig>", "token balance for <account>", "check this address on mainnet/devnet". End-to-end Solana development playbook covering wallet connection, Anchor/Pinocchio programs, Codama client generation, Surfpool/LiteSVM/Mollusk testing, security checklists, and JSON-RPC curl lookups against public clusters. Prefers @solana/kit v7 plugin clients (createClient + .use()), wallet connection via @solana/kit-plugin-wallet + @solana/react, web3.js v3 (Kit internals, RC) as the migration target for legacy codebases, and Surfpool for local networks and integration testing.

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。

Reference for building Starknet applications using starknet.js v9.x SDK, including contract interaction, account management, transaction handling, fee estimation, wallet integration, and paymaster flows.

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 guide for building AI agents that interact with Solana blockchain using SendAI's Solana Agent Kit. Covers 60+ actions, LangChain/Vercel AI integration, MCP server setup, and autonomous agent patterns.

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。

Complete Birdeye API integration for real-time DeFi data across Solana and 15 other chains. Use for token prices, OHLCV charts, market discovery, on-chain trader intelligence, holder analysis, wallet portfolio & P&L, and WebSocket streams for live prices and whale alerts.

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。

Cross-chain token swap integration using NEAR Intents 1Click API. Use when building swap widgets, bridge interfaces, or multi-chain transfers across EVM, Solana, NEAR, TON, Stellar, and Tron.

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。

Complete Switchboard Oracle Protocol SDK for Solana - the permissionless oracle solution for price feeds, on-demand data, VRF randomness, and real-time streaming via Surge. Covers TypeScript SDK, Rust integration, Oracle Quotes, and all Switchboard tools.

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。

Complete guide for Pyth Network - decentralized oracle providing real-time price feeds for DeFi. Covers price feed integration, confidence intervals, EMA prices, on-chain CPI, off-chain fetching, and streaming updates for Solana applications.

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。

Migration guide from Jupiter Metis (v1) or Ultra to Swap API v2. Use when migrating existing Jupiter swap integrations, updating base URLs, or transitioning from quote+swap-instructions to the unified build endpoint.

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。

0G Compute Network guide for decentralized AI inference, fine-tuning, and GPU services. Covers chatbots, image generation, speech-to-text, SDK integration (0g-serving-broker), processResponse API, broker.inference methods, CLI commands (0g-compute-cli), and account management. Use this skill for any 0G compute, 0G AI, or decentralized GPU question.

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 Solana wallet-connected apps with Phantom Connect SDKs and DFlow spot trading. Use when user asks to connect a Phantom wallet, integrate Phantom in React, React Native, or vanilla JS, sign messages or transactions, build token-gated pages, mint NFTs, accept crypto payments, or swap/stream tokens with DFlow. Covers @phantom/react-sdk, @phantom/react-native-sdk, @phantom/browser-sdk, and DFlow spot trading and market-data streaming. Do NOT use for Ethereum or EVM wallet integrations, or non-DFlow DEX routing.

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。

Design, implement, and debug MagicBlock applications on Solana. Covers Ephemeral Rollups with delegated state; ER/PER architecture and settlement; private payments and token flows; oracles and randomness; scheduling and temporary authority; security and local validation. Use for MagicBlock product selection, integration, cross-product design, or production troubleshooting.

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 a production 1:1 stablecoin swap with LI.FI Intents — an interface or backend where the amount the user sends equals the amount they receive (no visible gas or solver fees), same-chain or cross-chain, filled by LI.FI's solver network and verifiable on-chain. USE THIS SKILL WHEN USER WANTS TO: - Build a 1:1 stablecoin swap interface ("send 100 USDC, receive 100 USDC"), same- or cross-chain - Add stablecoin transfers with a guaranteed output amount to an app, backend, or bot - Build enterprise/fintech-grade transfers with no visible gas or solver fees for the end user - Implement the LI.FI Intents quote -> approve -> escrow open -> status loop for stablecoins - Move USDC / USDT 1:1 across chains under an onboarded LI.FI integrator - Understand how the LI.FI integrator ID / Partner Portal enables 1:1 quoting - Use the @lifi/intent TypeScript SDK to quote, build, open, and track an intent order This skill is self-contained and covers the integration end to end via the `@lifi/intent` SDK (the primary path), with the raw `order.li.fi` REST endpoints as a language-agnostic option. For the wider LI.FI product surface (classic swaps/bridges, Composer, Earn), use the `lifi` skill.

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 guidance for integrating Jupiter APIs (Swap, Lend, Perps, Trigger, Recurring, Tokens, Price, Portfolio, Prediction Markets, Send, Studio, Lock, Routing). Use for endpoint selection, integration flows, error handling, and production hardening.

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。

Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, batch processing, or scheduled tasks. Always consult this skill when the user asks to create, build, or design an n8n workflow, automate a process, or connect services — even if they don't explicitly mention 'patterns'. Covers webhook, API, database, AI, batch processing, and scheduled automation architectures. Also use when optimizing a slow workflow or speeding up large-item-count processing (node count, batchSize, all-items vs per-item).

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。

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

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。

Template for skills that integrate with an MCP server. Demonstrates the reference file pattern: Claude reads a domain-specific MCP cheatsheet before making any tool calls, reducing query failures caused by server-specific gotchas. Fork this skill and replace the Sentry example with your target MCP.

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 wants to "set up tracing", "monitor my agent", "configure logging", "add observability", "debug production traffic", or needs guidance on monitoring deployed agents, including ADK (Agent Development Kit) agents. Covers Cloud Trace, prompt-response logging, BigQuery Agent Analytics, third-party integrations (AgentOps, Phoenix, MLflow, etc.), and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment setup (use google-agents-cli-deploy) or API code patterns (use google-agents-cli-adk-code).

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。

Manage Git worktrees in project-level ../.zcf/project-name/ directory with smart defaults, IDE integration and content migration

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。

Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.

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 admin asks to configure an MCP tool server (e.g., GitHub, weather API), rotate credentials, grant/revoke worker access to MCP tools, or add a custom API integration via YAML.

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。

Set up or recover MTP hot reload for a long-lived console-host edit/re-run loop in a Microsoft Testing Platform project. Use for explicit MTP console requests such as "enable hot reload", "dotnet run to rerun on edit", or unsupported/rude edits. Covers setup, run/watch, restarts, filters, and the VSTest no-mutation fallback. For one-time runs, exact commands, filter errors, TRX/dumps, or merely a failing test, use run-tests. Do not use when Test Explorer or an IDE should rerun tests without an MTP console host. Excludes editor integration, CI, and writing/debugging tests.

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。

Provides file paths to language-specific reference files for the test ANALYSIS skills (assertion-quality, test-anti-patterns, test-gap-analysis, test-smell-detection, test-tagging). Read this catalog to discover available extension files (e.g., dotnet.md for .NET/MSTest/xUnit/NUnit/TUnit, python.md for pytest/unittest, typescript.md for Jest/Vitest/Mocha, java.md for JUnit/TestNG, etc.). Do not invoke this reference-only skill — its bundled files are read by the quality agent and analysis skills needing framework-specific lookup tables (test markers, assertion APIs, skip annotations, sleep patterns, mystery guest indicators, integration markers, setup/teardown, tag-support capability).

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。

Run file-based C# apps with the .NET CLI when the user explicitly wants C#/.NET code without creating a project. Use for C# language/API experiments, one-file C# apps, small multi-file C# apps composed with `#:include`/`#:exclude`, or C# file-based apps linked with `#:ref`. Do not use for language-agnostic throwaway scripts, generic computations, Python/PowerShell-style automation, full projects, or existing app integration.

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。

Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).

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。