Define contact and interactions - contact pairs, tie constraints, connectors. Use when user mentions contact, friction, tie, parts touching, or bonded 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。
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概要と使いどころ
Define contact and interactions - contact pairs, tie constraints, connectors. Use when user mentions contact, friction, tie, parts touching, or bonded 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。
[40] PLAN. Create clear action plans with steps, success criteria, and risk awareness. Use before implementing features, making changes, starting projects, or anytime you need a roadmap to success. Triggers on "plan this", "how should we approach", "what's the strategy", "steps to complete", or when facing complex multi-step 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。
Suggests manual context compaction at logical intervals to preserve context through task phases rather than arbitrary auto-compaction.
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。
Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.
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。
Review a feature and enhance it with purposeful animations, micro-interactions, and motion effects that improve usability and delight. Use when the user mentions adding animation, transitions, micro-interactions, motion design, hover effects, or making the UI feel more alive.
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。
AI music generation with ACE-Step 1.5 — background music, vocal tracks, covers, stem extraction, audio repainting, and continuation for video production. Use when generating music, soundtracks, jingles, or working with audio stems. Triggers include background music, soundtrack, jingle, music generation, stem extraction, cover, style transfer, repaint, continuation, or musical composition 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 this skill when building analytics dashboards with nuxt-ui-tools as a package consumer. Covers schema and view functions (defineDashboardSchema, defineDashboardView) that take their context as typed params, useDashboard with a getter that rebuilds on input change, typed injection (useDashboardView, injectDashboard, InferDashboard, InferDashboardView), filters declared inline with the f builder and synced with the URL, memory, or an external store (one value per key across views), headless filters, filter controls and the shipped UI (UiDashboardPage, UiDashboardFilters, UiDashboardFilter, UiDashboardViewTabs) with slot overrides, remote paginated pickers and remoteTableOptions, staged queries (essential / background / deferred) with select and dependent requires, lazy enabled conditions on views, queries, and filters (blocks of disabled queries hidden, grids closing up), derived values, views (tabs), query-scoped filters, number formats (presets, Intl options, useDashboardFormat), and the dashboard blocks (stats with trends, goals, and comparisons, stat groups, gauges, unovis charts with highlights, value labels, and totals, lists with progress rings, bars, funnel, alerts, activity feeds, sortable tables, custom widgets) with their automatic loading, error, empty, and refresh states, card menus, header and footer actions, row actions, drill-down and selected state, card tabs, split cards, comparison periods, auto-refresh, and data freshness.
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. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction.
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 personal local Agent Skill files as an installable skill manager. Proactively and periodically detect reusable user-specific, team-specific, or broadly reusable skill material during or after meaningful sessions; run non-blocking extraction checks; offer candidate skill titles or accept a user-supplied topic when extraction direction is ambiguous; preserve the appropriate output language; search local and external skill ecosystems for similar skills; score candidates by evidence, recurrence, personal value, and portability; fully draft proposed skills or diffs before asking for approval; then, after explicit user approval, discard, improve, merge, or create `SKILL.md` folders.
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 knowledge domain for Firecrawl v2 API - web scraping and crawling that converts websites into LLM-ready markdown or structured data. Use when: scraping websites, crawling entire sites, extracting web content, converting HTML to markdown, building web scrapers, handling dynamic JavaScript content, bypassing anti-bot protection, extracting structured data from web pages, or when encountering "content not loading", "JavaScript rendering issues", or "blocked by bot detection". Keywords: firecrawl, firecrawl api, web scraping, web crawler, scrape website, crawl website, extract content, html to markdown, site crawler, content extraction, web automation, firecrawl-py, firecrawl-js, llm ready data, structured data extraction, bot bypass, javascript rendering, scraping api, crawling api, map urls, batch 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。
Tracks complex, multi-session work using the Beads issue tracker and dependency graphs, and provides persistent memory that survives conversation compaction. Use when work spans multiple sessions, has complex dependencies, or needs persistent context across compaction cycles. Trigger with phrases like "create task for", "what's ready to work on", "show task", "track this work", "what's blocking", or "update status".
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。
开源技术架构与生态整合——ClawHub 200+ skills + GitHub全套工具的深度驾驭。覆盖:ClawHub生态索引与组合、GitHub Actions高级工作流、开源项目评估、依赖安全管理、供应链安全。触发词:开源生态、ClawHub、GitHub、open source、开源架构、GitHub Actions、Dependabot、开源选型、供应链安全
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/UX design intelligence for web and mobile. Includes 50+ styles, 161 color palettes, 57 font pairings, 161 product types, 99 UX guidelines, and 25 chart types across 10 stacks (React, Next.js, Vue, Svelte, SwiftUI, React Native, Flutter, Tailwind, shadcn/ui, and HTML/CSS). Actions: plan, build, create, design, implement, review, fix, improve, optimize, enhance, refactor, and check UI/UX code. Projects: website, landing page, dashboard, admin panel, e-commerce, SaaS, portfolio, blog, and mobile app. Elements: button, modal, navbar, sidebar, card, table, form, and chart. Styles: glassmorphism, claymorphism, minimalism, brutalism, neumorphism, bento grid, dark mode, responsive, skeuomorphism, and flat design. Topics: color systems, accessibility, animation, layout, typography, font pairing, spacing, interaction states, shadow, and gradient.
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 a startup to set a single quantified traction goal and define the critical path of milestones to reach it. Use whenever a founder needs to prioritize activities, set growth goals, define milestones, decide what NOT to work on, plan quarterly/yearly execution, cascade goals to teams, escape the 'too many things to do' trap, or apply a binary on-path/off-path filter to proposed work. Activates on phrases like 'traction goal', 'critical path', 'what should we focus on', 'too many priorities', 'prioritization', 'milestones', 'quarterly planning', 'yearly goals', 'OKRs', 'where should I spend my time', 'what NOT to do', 'company planning', 'goal setting', 'DuckDuckGo', 'roadmap'.
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。
Maxim anime-action video — engineered for tribe identity and brand loyalty in youth markets. Uses Social Identity Theory + Pattern Interruption + Emotional Amplification to create brand-tribe belonging that translates to loyalty and advocacy.
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 concise, actionable meeting minutes for internal meetings. Includes metadata, attendees, agenda, decisions, action items (owner + due date), and follow-up steps.
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。
Give your AI agent the same visibility you have in the Power Automate portal — plus a bit more. The Graph API only returns top-level run status. Flow Studio MCP exposes action-level inputs, outputs, loop iterations, and nested child flow failures. Use when asked to: list flows, read a flow definition, check run history, inspect action outputs, resubmit a run, cancel a running flow, view connections, get a trigger URL, validate a definition, monitor flow health, or any task that requires talking to the Power Automate API through an MCP tool. Also use for Power Platform environment discovery and connection management. Requires a FlowStudio MCP subscription or compatible server — see https://mcp.flowstudio.app
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。
Debug failing Power Automate cloud flows using the FlowStudio MCP server. The Graph API only shows top-level status codes. This skill gives your agent action-level inputs and outputs to find the actual root cause. Load this skill when asked to: debug a flow, investigate a failed run, why is this flow failing, inspect action outputs, find the root cause of a flow error, fix a broken Power Automate flow, diagnose a timeout, trace a DynamicOperationRequestFailure, check connector auth errors, read error details from a run, or troubleshoot expression failures. Requires a FlowStudio MCP subscription — see https://mcp.flowstudio.app
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, scaffold, and deploy Power Automate cloud flows using the FlowStudio MCP server. Your agent constructs flow definitions, wires connections, deploys, and tests — all via MCP without opening the portal. Load this skill when asked to: create a flow, build a new flow, deploy a flow definition, scaffold a Power Automate workflow, construct a flow JSON, update an existing flow's actions, patch a flow definition, add actions to a flow, wire up connections, or generate a workflow definition from scratch. Requires a FlowStudio MCP subscription — see https://mcp.flowstudio.app
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 formal specification for an existing GitHub Actions CI/CD workflow, optimized for AI consumption and workflow maintenance.
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。
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.
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。
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library
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。
Skill for microinteraction-patterns 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。
GitHub MCP Server integration for repository operations, pull requests, issues, and GitHub Actions. Triggers on: github mcp, github operations, pull request, issue, repository, github actions, commit, branch, merge.
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。