4,718 件 ・ 関連度順
概要と使いどころ
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。
Integrate with Octav API for cryptocurrency portfolio tracking, transaction history, and DeFi analytics across 50+ blockchain networks. Use when building applications that need to: (1) Track wallet balances and net worth across multiple chains, (2) Query transaction history with filtering and search, (3) Monitor DeFi protocol positions (Aave, Uniswap, etc.), (4) Access historical portfolio snapshots, (5) Analyze token distribution and holdings, (6) Pay per request as an autonomous agent via x402. Triggers on: "Octav API", "crypto portfolio API", "blockchain portfolio tracking", "DeFi analytics API", "wallet balance API", "transaction history API", "multi-chain portfolio", "Octav x402".
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。
TypeScript library for NEAR Protocol blockchain interaction. Use this skill when writing code that interacts with NEAR Protocol, including viewing contract data, calling contract methods, sending NEAR tokens, building transactions, creating type-safe contract wrappers, integrating wallets (Wallet Selector, HOT Connect), React hooks and providers (@near-kit/react), managing keys, testing with sandbox, meta-transactions (NEP-366), and message signing (NEP-413).
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 building wallet infrastructure, authentication systems, or financial applications. Reach for Privy when you need to create embedded wallets, manage user authentication, control wallet permissions with policies, execute transactions across blockchains, or build treasury/agent wallets with programmable controls.
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。
Multi-agent workflow examples to work together on the OpenServ Platform. Covers agent discovery, multi-agent workspaces, task dependencies, and workflow orchestration using the Platform Client. Read reference.md for the full API reference. Read openserv-agent-sdk and openserv-client for building and running agents.
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 developing with near-api-js v7 - the JavaScript/TypeScript library for NEAR blockchain interaction. Use when: (1) building apps that interact with NEAR blockchain, (2) creating/signing transactions, (3) calling smart contracts, (4) managing accounts and keys, (5) working with NEAR RPC API, (6) handling FT/NFT tokens on NEAR, (7) using NEAR cryptographic operations (KeyPair, signing), (8) converting between NEAR units (yocto, gas), (9) gasless/meta transactions with relayers, (10) NEP-413 message signing for authentication, (11) storage deposit management for FT contracts. Triggers on any NEAR blockchain development 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。
Build with the ChainGPT Web3 AI developer platform. Full API/SDK reference and project scaffolding for: Web3 AI Chatbot & LLM, AI NFT Generator, Smart Contract Generator, Smart Contract Auditor, AI Crypto News, AgenticOS Twitter agents, and Solidity LLM. Use when building blockchain apps, Web3 chatbots, NFT tools, smart contract tools, crypto news feeds, AI agents, or integrating any ChainGPT API. Triggers: chaingpt, web3 ai, nft generator, smart contract audit, crypto news api, agenticos, solidity llm, cgpt, blockchain ai, token analytics.
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。
Deploy a production self-hosted n8n end-to-end to a fresh Linux VM over SSH, using Docker Compose behind a Caddy reverse proxy with automatic HTTPS. Use whenever the user wants to self-host, install, provision, or deploy n8n on their own server/VPS (Hetzner, DigitalOcean, AWS EC2, bare metal) — single/regular mode or queue mode with workers — or to update, back up, restore, or harden such an instance, or make Python Code nodes run on it (task runners). For SELF-HOSTED n8n (Docker), not n8n Cloud and not building workflows. The skill makes the agent ask single-vs-queue first, collect domain/SSH/timezone inputs, generate fresh secrets on the box, and bring the stack up with TLS. Trigger on "deploy n8n", "self-host n8n", "n8n docker compose", "n8n queue mode / workers", "n8n reverse proxy / SSL", "back up / update my n8n", "Python runner unavailable" / "n8nio/runners sidecar", or "we don't want to give every user the OAuth client secret" / "enable Sign in with Google" (credential overwrites).
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。
Wire n8n error handling so failures are loud, structured, and recoverable. Use when building any webhook/API workflow, a scheduled or unattended workflow, or any path where a silent failure would drop user-visible work — and whenever the user mentions error handling, onError, continueErrorOutput, error branches/outputs, retries, retryOnFail, Respond to Webhook status codes, 4xx/5xx, Error Trigger, or "my workflow fails silently". Covers per-node error outputs and wiring, retry/self-healing, error-trigger workflows, and 4xx/5xx response shapes.
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。
Use when building, editing, validating, testing, or debugging an n8n workflow through the n8n-mcp MCP server — designing a flow, configuring a node, writing an expression or Code node, wiring credentials, or fixing one that misbehaves. The entry-point skill for the n8n-mcp-skills pack: it routes you to the right specialist skill, gives working knowledge of every n8n-mcp tool from turn one, and states the rules that keep workflows from breaking in production. Always consult it first on any n8n, workflow, node, or automation task — even a quick one-off, and even when the user names no skill — because n8n's surface drifts between versions and the specialist skills prevent silent failures.
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 reusable, composable n8n sub-workflows. Use when extracting shared logic, building anything multi-step or reused across workflows, or any workflow over ~10 nodes — and whenever the user mentions sub-workflows, Execute Workflow, reuse, shared/common logic, modular workflows, "Define Below" inputs, waitForSubWorkflow, mode each vs all, or exposing a workflow as an agent tool. Covers typed sub-workflow inputs, all-vs-each execution, verb-first naming for discovery, stateless vs stateful design, and splitting by input shape.
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。
Write JavaScript or Python for the n8n Custom Code Tool (@n8n/n8n-nodes-langchain.toolCode) — the AI-agent-callable tool, NOT the workflow Code node. Use when building a Code Tool attached to an AI Agent, writing code that an LLM will invoke, parsing the `query` input, returning a string result, defining an input schema for structured arguments (specifyInputSchema, jsonSchemaExample, DynamicStructuredTool), or troubleshooting errors like "Wrong output type returned", "No execution data available", "The response property should be a string, but it is an object", "Cannot assign to read only property 'name'", or an AI agent that refuses to call the tool. Covers the critical differences between Code node and Code Tool: return format (string vs `[{json:{...}}]`), unavailability of `$fromAI`/`$input`/`$helpers` in the Code Tool sandbox, naming rules for AI invocation, and when to use `toolWorkflow`/HTTP Request Tool instead.
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 n8n AI agents the right way. Use when building or editing any @n8n/n8n-nodes-langchain.* AI node — an AI Agent, LLM chain, Text Classifier, or Information Extractor — and whenever the user mentions AI agents, LLM with tools, tool calling, $fromAI, system prompts, agent memory, sessionId, structured/JSON output, output parser, RAG, vector store, a chat assistant/bot, or human-in-the-loop review. Covers Agent-vs-chain-vs-classifier choice, the model/memory/tools/outputParser slots, tool names/descriptions as prompt, structured output with autoFix, memory, RAG, human review, and chat topologies.
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 high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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。
UI and visual skill for antislop. Use when building or editing any interface: color, layout, components, motion. Load with the core.
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。
Produces revision sheets with quick navigation by act, a master concept-to-URL table, Q&A cheat-sheet with 6-10 anticipated questions, glossary, and external resources list. Use when preparing for a talk with Q&A, creating shareable reference material for attendees, or building a safety-net glossary for live delivery.
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。
Performs git archaeology, changelog analysis, and builds a verified factual timeline by cross-referencing git history with source material. REX mode only (skipped automatically in Concept mode). Use when building a REX talk and you need verified commit metrics, release timelines, and contributor data from a git repository.
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。
Generate complete, deploy-ready landing pages from any repository. Use when creating a homepage for an open-source project, building a project website, converting a README into a marketing page, or standardizing landing pages across multiple repos.
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 helps users extract YouTube influencer profiles including social links, subscriber counts, and channel stats via the BrowserAct API. Agent should proactively apply this skill when users express needs like finding YouTube creators for specific keywords, discovering influencers for a marketing campaign, extracting YouTube channel contact emails, scraping YouTube influencer social media links, gathering subscriber counts for YouTube creators, researching top YouTube channels in a specific niche, compiling a list of YouTube content creators with recent uploads, collecting YouTube creator profiles for outreach, extracting total views and video counts for specific YouTube influencers, building a database of YouTube partners for market research, finding YouTube influencers who uploaded videos this month, or monitoring competitor influencer activities on YouTube.
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 helps users extract structured data from Reddit posts and comments via BrowserAct API. Agent should proactively apply this skill when users express needs like analyzing competitor mentions on Reddit, tracking brand sentiment in Reddit comments, extracting Reddit discussions for market research, finding popular Reddit posts by keywords, monitoring community feedback on specific topics, gathering user reviews from Reddit threads, searching for Reddit posts within a specific date range, sorting Reddit discussions by relevance or hotness, compiling nested Reddit comments for deep analysis, building a structured dataset of Reddit conversations, discovering trending topics in specific subreddits, or monitoring social media activity for specific brands on Reddit.
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 helps users automatically extract structured image data from Google Images via BrowserAct API. Agent should proactively apply this skill when users express needs like finding images for specific keywords, gathering product style images for competitors, building visual datasets at scale, scanning visual search results for market research, tracking localized image trends by country, compiling related image thumbnails and links, extracting image titles and source logos, fetching click through URLs from image results, monitoring competitor visual assets, sourcing creative content for specific topics, looking up product pictures in different regions, collecting structured image metadata without opening detail pages.
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 helps users automatically extract YouTube video transcripts and metadata via the BrowserAct API. The Agent should proactively apply this skill when users express needs like extracting full transcript from a specific YouTube video, getting subtitles and metadata for video content analysis, gathering video titles and likes counts, summarizing YouTube videos without watching them, collecting channel details from a video URL, tracking transcript automation for specific videos, scraping YouTube subtitles for internal knowledge bases, fetching full video content for AI summarization pipelines, downloading structured transcripts from YouTube links, analyzing video text content for media research, monitoring video publisher information and channel links, or building datasets from YouTube video transcripts.
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 helps users automatically extract structured channel data from YouTube search results via BrowserAct API. Agent should proactively apply this skill when users express needs like finding YouTube channels about specific topics, collecting data on YouTube content creators, tracking YouTube influencers in specific industries, getting YouTube channel information for competitor analysis, searching for YouTube channels related to keywords, monitoring YouTube channel updates for specific keywords, finding YouTube channels that recently published videos, extracting YouTube channel subscriber counts, discovering YouTube vloggers in specific niches, building a YouTube channel database for market research, batch extracting YouTube channel links and descriptions, or monitoring competitor channel growth.
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。