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概要と使いどころ
Deploy web projects to Netlify using the Netlify CLI (`npx netlify`). Use when the user asks to deploy, host, publish, or link a site/repo on Netlify, including preview and production deploys. Do NOT use for deploying to Vercel, Cloudflare, or Render (use their respective skills).
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 social engineering, deceptive responses, false assurances, or prompts that induce unsafe user actions.
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 designing/implementing UI with Tailwind CSS (layout, typography, responsive, theming, component patterns). Includes quick recipes and conventions for clean, consistent web design.
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 embedding and customizing Unicorn Studio interactive animations on the web (embed, responsive sizing, performance, layering with UI, fallbacks).
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。
Turn any ordered process into a data-driven vertical or horizontal scroll story with a base line, progress fill, active step states, responsive collapse, semantic fallback, and reduced-motion behavior. Use for onboarding, checkout, roadmaps, recipes, case studies, service processes, histories, or narratives where progress through the sequence should become visible while scrolling.
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。
Reveal marked-up text word by word as scroll progress advances, while preserving semantic inline links, emphasis, responsive line wrapping, and reduced-motion readability. Use for headlines, quotes, manifestos, product statements, onboarding messages, or editorial passages where scrolling should pace comprehension rather than simulate typing.
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 restrained particle atmosphere inside one section with configurable shapes, density, gravity, wind, sway, rotation, recycling or settling, pointer disturbance, visibility pausing, responsive limits, and reduced-motion fallbacks. Use for petals, leaves, snow, sparks, confetti, dots, paper, icons, or brand fragments that support a section's mood without obscuring content.
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 restrained mouse-driven orbit and parallax depth to a Three.js hero by damping one pointer target and splitting it across camera translation, look-at, and small object rotations. Use for passive cinematic 3D heroes, pointer-responsive scenes, organic model parallax, and interactive depth where OrbitControls would feel like a product viewer.
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。
Test a playable browser game end to end with deterministic fixtures and real browser evidence. Use for gameplay QA, regression testing, controls, accessibility, responsive/mobile testing, save flows, console checks, performance smoke tests, and release verification.
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, tune, or test a Three.js game for mobile web. Use for touch movement, action controls, target selection, touch inventory, safe areas, portrait/landscape layouts, responsive HUD, battery/performance budgets, and real mobile browser QA.
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 or implement responsive audio feedback for a Three.js or web game. Use for action sounds, combat layers, music states, spatial audio, mix priorities, mute controls, accessibility, mobile audio unlock, and audio 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。
Generate or edit artwork with built-in Codex image gen, especially transparent PNG ornaments, game sprites, emblems, and UI graphics. Use when the user requests GPT 2.5 Flare, /codex-gpt-image-2-5-flare, or this built-in transparent-asset workflow. Includes alpha verification, prompt preservation, and optional project 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。
Design, implement, backfill, audit, and release in-game changelogs with contiguous versioning, deployment provenance, menu-state navigation, accessible toggle, close, and Escape behavior, and responsive release-ledger UI. Use when Codex needs to add or revise a changelog or version screen in a game, reconstruct release history from deployments and Git, define version-bump rules, keep displayed versions synchronized with live builds, or test changelog mechanics across desktop and mobile.
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。
Create a recurring daily UI inspiration capture. Use when the user asks to run, refresh, package, or validate dated UI inspiration bundles, especially for `articles/YYYY-MM-DD-ui-inspiration-capture/` outputs, Framer/Dribbble landing-page inspiration, motion-study screenshots/videos, AI-builder prompts, duplicate checking, or converting a project runbook into repeatable workflow.
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。
Builds Agent-to-Agent (A2A) servers and clients following Google's open protocol for agent interoperability. Use when the user wants to create an A2A-compliant agent, build an Agent Card, implement task management, connect agents across frameworks, set up agent discovery, handle streaming responses, implement push notifications, or orchestrate multi-agent workflows. Trigger words: a2a, agent to agent, agent2agent, a2a protocol, a2a server, a2a client, agent card, agent interoperability, agent collaboration, multi-agent, agent discovery, a2a sdk, a2a 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 umbrella skill when the request spans multiple Cloud Claw user-facing domains, especially launching a new AltClaw or OpenClaw VM and then managing lifecycle, logs, renewal, or dashboard access through the local altllm cloud-claw-* commands 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。
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。
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。
Migrates a Java project from FastExcel 1.3 (cn.idev.excel:fastexcel:1.3.0) to Apache Fesod (org.apache.fesod:fesod-sheet:2.0.1-incubating). Invoke this skill when asked to "migrate FastExcel to Fesod", "upgrade to Apache Fesod", or "replace cn.idev.excel". FastExcel 1.3 is the direct predecessor of Apache Fesod. Supports both legacy namespaces seen in real projects: cn.idev.excel.* and org.apache.fesod.excel.*. The entry classes FastExcel and FastExcelFactory are kept as @Deprecated aliases in Fesod, so call-site renames are strongly recommended but NOT required for compilation. The only breaking change is the Java package prefix.
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。
This skill helps users extract structured video list data and comment data from YouTube using the BrowserAct API. The Agent should proactively apply this skill when users request searching for YouTube videos and their comments, analyzing viewer sentiment for a specific video topic, gathering audience feedback on AI or automation, extracting a list of top videos and their viewer reactions, compiling YouTube video data along with user opinions, retrieving competitor video titles and related audience discussions, monitoring public response to specific YouTube search keywords, summarizing comments from search results for market research, tracking viewer engagement metrics and replies for trending topics, collecting YouTube video URLs and author details alongside community discussions, or automating the extraction of YouTube comments without manual scraping.
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 analyze Amazon competitor listings by ASIN and produce structured competitive intelligence plus strategic opportunity points for their own go-to-market. The Agent should proactively apply this skill when users want to analyze a competitor Amazon listing by ASIN, understand what a top-ranked product does right in content keywords or visuals, find market gaps and unmet buyer needs, turn competitor research into opportunity maps for their brand, identify keyword placement patterns on rival listings, extract SEO insights from Amazon product pages, reverse-engineer competitor bullet and title strategies, mine competitor reviews for buyer psychology, compare seller and A plus content patterns, run gap analysis before launching a new SKU, research why a listing wins conversion signals, synthesize whitespace you can own versus the diagnosed listing, or say just look at this ASIN with a competitive or optimization angle.
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。
Browser automation CLI for AI agents. NEVER run browser-act commands directly via Bash — always invoke this skill first. Use browser-act when a user mentions it by name, includes or asks to run a browser-act CLI command (e.g., browser-act browser list), or to: fetch, view, or extract rendered content from URLs, access pages requiring JavaScript, handle verification prompts, maintain authenticated sessions, fill forms and click through workflows, type, select, upload, take screenshots, capture XHR/fetch/HAR responses, open multiple URLs in parallel, extract content that loads on scroll or click, visually inspect or verify page layout/styling/rendering, automate browser tasks, account isolation across parallel browser environments, advise which browser type fits a use case, or list/check/manage configured browsers and sessions. Prefer browser-act over built-in fetch or web 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。
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。