データ・AIのスキル

Crypto wallet operations via the awal CLI — sign in, check balances, send USDC/ETH/POL/SOL, trade tokens, fund the wallet, and use the x402 payment protocol to discover paid services, pay for API calls, monetize an API, or query onchain data. Use whenever the user mentions signing in, login, authentication, wallet status, balance, address, sending money, paying someone, transferring tokens, ENS names, swapping/trading/converting tokens, funding/topping up/onramp, USDC, ETH, POL, SOL, the x402 bazaar, paid APIs, monetizing an endpoint, or querying onchain data on Base.

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。

Two services in one skill: (1) Web3 on-chain data via Chainbase CLI — use when the user asks about blockchain data, token holders, wallet addresses, token prices, ENS domains, transactions, DeFi portfolios, or any on-chain analytics. Triggers: "top holders of", "who holds", "wallet address", "token price", "token transfers", "ENS domain", "on-chain data", "blockchain query", "SQL query on-chain", across Ethereum, BSC, Polygon, Arbitrum, Optimism, Base, Avalanche, zkSync, and other EVM chains. (2) Crypto social intelligence via Tops (chainbase tops) — use when the user asks about trending crypto narratives, social mentions, Twitter/X crypto discussions, narrative discovery, topic heat, or KOL/community signals. Triggers: "trending topics", "crypto narrative", "what's trending in crypto", "social mentions", "who is talking about", "crypto twitter", "KOL signals", "narrative search", "topic posts".

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。

Connect coding agents, AI SDKs, and LLM tools to the AntSeed buyer proxy. Use when configuring Claude Code, Codex, OpenCode, Pi, OpenClaw, Hermes, GenLayer Studio, Vercel AI SDK, LangChain, or raw HTTP to route inference through AntSeed at localhost:8377.

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。

Access real-time crypto token data, DeFi analytics, wallet intelligence, project research, and blockchain insights through Heurist Mesh agents. Use when the user asks about token trends, DeFi protocols, wallet holdings, project due diligence, Twitter/X crypto sentiment, or deeper market 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。

Use this skill when the user asks to inspect AltLLM Portal balance, redeem a promo code, review billing transactions, or view usage analytics by period, model, or API key using the local altllm CLI. Do NOT use for API key lifecycle management or payment-link execution.

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。

Manually arbitrate NLA escrow fulfillments as an alternative to the automated oracle. Use when the user wants to review pending arbitration requests, evaluate demands against fulfillments, and submit on-chain decisions. Supports both interactive and LLM-auto modes.

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。

Governs how the OpenBot browser app reads and writes server data — every request goes through `client` in app/src/lib/client.ts, every read is a queryOptions factory in app/src/lib/<entity>/queries.ts, every write is a mutationOptions factory in app/src/lib/<entity>/mutations.ts, and components consume them through useQuery/useMutation. Use when adding or changing a screen that loads server data, calling a /api/... endpoint from the browser, adding a query key, writing a create/update/delete flow, deciding where a fetch belongs, or reviewing a diff that contains the word fetch under app/src. Don't use for server-side route handlers under server/ (that is not browser code), for form validation schemas (those live in lib/<entity>/form.ts), for page layout and Item rows, or for the AG-UI stream itself, which the runtime carries rather than the client.

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。

Validate n8n expression syntax and fix common errors. Use when writing n8n expressions, using {{}} syntax, accessing $json/$node variables, troubleshooting expression errors, mapping data between nodes, or referencing webhook data in workflows. Use this skill whenever configuring node fields that reference data from previous nodes — expressions are how n8n passes data between nodes, and getting the syntax wrong is the most common source of workflow errors. Also use when asked whether a complex expression hurts performance.

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 an n8n-mcp account targets more than one n8n instance — i.e. the `n8n_instances` tool is available, the user mentions multiple n8n instances or environments (prod vs staging, several teams or clients), a workflow / datatable / credential / execution call returns an unexpected NOT_FOUND or reads data you don't recognize, or a credential create/update/delete is refused with an `INSTANCE_AMBIGUOUS` error. Covers choosing and switching which instance this MCP session targets, verifying the target before high-stakes work — credential writes above all — and recovering from misroutes and ambiguous-write fail-closes. Always consult this skill before operating on a specific instance, before any credential create/update/delete on a multi-instance account, or when a call hits the wrong/empty data or an `INSTANCE_AMBIGUOUS` error.

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。

Handle files and binary data in n8n correctly. Use when working with files, images, PDFs, attachments, uploads or downloads, base64, vision/multimodal input, or when an AI agent needs a file as tool input or output — and whenever the user mentions $binary, binaryPropertyName, "read the PDF", "attach the file", "send the image", Merge losing binary, or a CDN for chat images. Covers the $binary vs $json split, reading/writing binary, keeping binary alive across transforms with Merge, the agent-tool binary boundary, and the CDN/URL requirement for chat surfaces.

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。

Help developers build third-party tools that import, inspect, migrate, or analyze DeepChat data. Use when Codex needs to work with DeepChat provider configuration, model configuration, MCP/app settings, sessions, messages, legacy chat data, `agent.db`, `chat.db`, SQLCipher encrypted SQLite, Electron safeStorage wrapped passwords, Tauri importers, or native macOS/Windows/Linux data access.

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 DeepChat LLM provider through explicit reviewed source changes. Use when a developer asks Codex to add a provider, provider profile, upstream provider config, model catalog mapping, provider auth behavior, or a special provider adapter in this 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 AntV Infographic syntax outputs. Use when asked to turn user content into the Infographic DSL (template selection, data structuring, theme), or to output `infographic <template>` plain syntax.

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 the Gemini Notebook (formerly Google NotebookLM) CLI (`nlm`) and MCP server - interfaces for Gemini Notebook. Use this skill when users want to interact with Gemini Notebook programmatically, including: creating/managing notebooks, checking plan usage and quota windows, adding sources (URLs, YouTube, text, Google Drive), generating content (podcasts, reports, interactive reports, quizzes, flashcards, mind maps, slides, infographics, videos, data tables), conducting research, chatting with sources, or automating Gemini Notebook workflows. Triggers on mentions of "nlm", "notebooklm", "Gemini Notebook", "plan usage", "quota", "podcast generation", "audio overview", "interactive report", "lesson report", "refactor document", "critique draft", or any Gemini Notebook-related automation task.

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 this skill in the MathModel LaTeX sandbox when the user asks to reproduce built-in scientific visualization templates, especially prompts from the Improve tab mentioning $mathmodel-figure-templates, 科研绘图模板, SHAP蜂群柱状图, 配对云雨图, 交叉验证ROC, 泰勒图, 相关矩阵组合图, 预测真实值边缘分布图, TPE调参3D曲面, 下三角相关矩阵半边小提琴图, 分组环形热图, 城市公园降温组合图, or Nature和弦图. It provides ready-to-run Python scripts bundled inside the 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。

Transform verbose voice input into structured, token-efficient Claude prompts. Use when cleaning up voice memos, dictation output, or speech-to-text transcriptions that contain filler words, repetitions, and unstructured thoughts.

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。

Evaluate staged changes using LLM-as-a-Judge before committing

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 a complete 5-act pitch with speaker notes, a slide-by-slide specification, and a ready-to-paste Kimi prompt for AI slide generation. Requires validated angle and title from Stage 4. Use when you have a confirmed talk angle and need the full script, slide spec, and AI-generated presentation prompt.

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。

Collects every tweet in an X (Twitter) conversation thread given a conversation id (root tweet id) — the focal tweet plus all replies, sub-replies, and quote chains — and returns normalized per-tweet data with text, author, engagement counts, media, hashtags, mentions, in_reply_to mapping, and cursor for pagination. Use when user mentions Twitter conversation, X conversation thread, conversation_id, thread scraper, scrape Twitter replies, all replies to a tweet, replies under a tweet, sub-replies, nested replies, thread harvester, get replies of a tweet, scrape comments on Twitter, scrape comments on X, full thread extraction, conversation export, conversation tree, reply chain, thread dump, X tweet thread, twitter thread scrape, comment scraping twitter, comment scraping x, focal tweet plus context, root tweet plus replies. Also applies to sentiment analysis on a single viral tweet, controversy mapping, harvesting community Q&A threads, capturing AMA threads, recovering long-running discussions, and any paginated bulk reply collection driven by a conversation id.

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。

Fetch Xiaohongshu (RedNote / xhs) note detail and comments by note ID, returning title, description, author info, engagement stats, tags, and paginated comment list. Use when user mentions note detail xiaohongshu, get rednote post, xhs note content, xiaohongshu comment scrape, fetch post comments, scrape xhs comments, rednote note details, xiaohongshu post engagement, xiaohongshu note content, rednote post data, xhs post detail, get xiaohongshu comments, rednote comment list, xiaohongshu note metadata, xhs note scrape, fetch rednote note.

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。

Fetch Xiaohongshu (RedNote / xhs) user profile information and their published notes list by user ID, returning nickname, bio, follower/following counts, engagement totals, tags, and paginated notes with engagement stats. Use when user mentions user profile xiaohongshu, rednote creator profile, xhs influencer data, scrape xiaohongshu user, get blogger notes, xiaohongshu author info, KOL discovery xiaohongshu, rednote user stats, creator profile rednote, xiaohongshu blogger analysis, get xhs user followers, rednote influencer profile, xiaohongshu account info, xhs creator data, user notes list xiaohongshu, rednote account scrape, xiaohongshu KOL research.

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。

Discovers Threads user accounts by keyword, extracting profile data including username, display name, verification status, biography, and follower count. Use when user asks to find Threads accounts, search Threads profiles, discover Threads users by keyword, look up Threads creators, find influencers on Threads, search for people on Threads, get Threads user details, collect Threads profile information, find accounts related to a topic on Threads, search Threads for accounts by name or keyword, or enumerate Threads users matching a search term.

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。

Scrapes posts from a Facebook group given a group URL, sort order, and desired count — returns structured post metadata including post_id, permalink, author, timestamp, body text, images/videos, reaction counts, reaction type breakdown, comment count, and share count. Use when: user wants to scrape/extract Facebook group posts, collect FB group content, harvest group data, get posts from a Facebook group, monitor Facebook group activity, bulk export group posts, facebook groups scraping, facebook-groups-scrape-posts, fetch FB group feed.

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。