「action」の検索結果

Use between coding-agent runs (Codex, Claude Code, Cursor, Aider, etc.) to stop the project from drifting, overbuilding, or accumulating dead weight. Forces a read-only audit that classifies every active item as KEEP / DELAY / REMOVE and ends with a single, unambiguous next action. Trigger on "audit before continuing", "what should I do next", "this project got too big", "between runs", "trim scope", "stop adding stuff", or any signal that the project feels noisy and the next step is unclear. Do NOT load for trivial one-line fixes, when the next action is already obvious, or when you're mid-run and just need to keep going.

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。

Web search, content extraction, crawling, and deep research via the Tavily CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, extract content from a URL, grab text from a webpage, crawl documentation, download a site's pages, discover URLs on a domain, or conduct in-depth research with citations. Also use when they say "fetch this page", "pull the content from", "get the page at https://", "find me articles about", or reference extracting data from external websites. This provides LLM-optimized web search, content extraction, site crawling, URL discovery, and AI-powered deep research — capabilities beyond what agents can do natively. Do NOT trigger for local file operations, git commands, deployments, or code editing 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。

Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks 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。

Clinical Decision Support System (CDSS) development patterns. Drug interaction checking, dose validation, clinical scoring (NEWS2, qSOFA), alert severity classification, and integration into EMR workflows. Use when building clinical decision support — drug interaction checks, dose validation, clinical scoring, or alert severity.

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. Integrations: shadcn/ui MCP for component search and examples. Use when designing, building, or reviewing UI for web or mobile apps.

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。

Ranks any list of impediments and their countermeasures using a value-stream scoring model (ROI, Cost to Implement, Ease of Deployment, Risk Factor) and a fixed prioritization formula. Use when someone asks to prioritize, rank, sequence, or triage impediments, countermeasures, remediation items, risks, findings, gaps, action items, or backlog entries; or mentions value-stream prioritization, A3 / lean countermeasure ranking, ROI vs. effort scoring, or building a remediation / improvement backlog. Works with GHQR findings, audit results, retrospective action items, risk registers, architecture review gaps, or any free-form `{impediment, countermeasure}` list.

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 asks to "share memory between agents", "KV cache compaction for multi-agent", "orchestrator worker context", "latent briefing", "reduce worker tokens", "cross-agent memory without summarization", or discusses Attention Matching compaction, recursive language models with workers, or token explosion in hierarchical 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。

Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web 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。

SOP for the automated refinement loop (/refine) workflow. Trigger when: - Exhaustively auditing and optimizing pre-existing codebase artifacts. - Applying iterative contraction mapping to reduce code entropy. - Prompt contains: /refine, refinement loop, contraction mapping, fixed-point sweep.

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, modify, or debug business events and their follow-up reactions, including order cancellation events, queued listeners, transaction timing, and rollback behavior.

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, modify, or refactor application use cases into Laravel-native DDD Actions. Use for business operations such as placing or cancelling orders, extracting controller orchestration, and fixing use-case behavior.

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-powered autonomous data extraction that navigates complex sites and returns structured JSON. Use this skill when the user wants structured data from websites, needs to extract pricing tiers, product listings, directory entries, or any data as JSON with a schema. Triggers on "extract structured data", "get all the products", "pull pricing info", "extract as JSON", or when the user provides a JSON schema for website data. More powerful than simple scraping for multi-page structured extraction.

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。

日本語Web UIの素材集。既定配色は暫定標準カラー(ブルー基調・ライトのみ・色は役割トークンだけを参照し、主操作は action-primary-*、状態は status-* に固定)。明示指定時だけMode B Pop。タイポと数値表記・レスポンシブ4段・部品HTML/CSS・モーションとa11y・情報設計の工程と表示形式の導出・動く参照実装(assets/reference)・コントラスト検査/トークン生成/旧配色の自動移行スクリプトを持つ。設計判断と体験の規律は含めない(Skill ux-design から参照される後工程)。UIの実装・見た目のレビュー・配色の導入や移行で必ず参照する。

GitHub Actionsの自動チェック、Cloudflareのチーム用Account設定、非エンジニア向け資格情報登録、自動公開を安全に導入する。Codexで「CI/CDを設定」「Cloudflareを設定」「自動デプロイ」「$setup-cicd」と依頼されたときに使用する。

Deploy and use the Exasol Text AI Extension (TXAIE) with notebook-connector. Covers `deploy_license`, `initialize_text_ai_extension`, the `PYTHON3_TXAIE` language container, the Extraction API classes `StandardExtractor`, `NamedEntityExtractor`, `TopicClassifierExtractor`, `PipelineExtractor`, and `BranchExtractor`, named-entity, zero-shot classification, and feature extraction runs, default-model installation, and result-table querying such as `TXAIE_AUDIT_LOG`.

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。

Verify Rash Studio scenes, ActionScript subset behavior, resource links, portable embeds, exports, and desktop or mobile interaction. Use for Rash acceptance checks, compatibility review, security review, or release validation.

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。

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。

Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. Triggers: "azure-ai-contentunderstanding", "ContentUnderstandingClient", "multimodal analysis", "document extraction", "video analysis", "audio transcription".

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 model stealing, model inversion, and membership inference performed through inference-API abuse by monitoring query patterns, applying output perturbation, and red-teaming your own model's extractability.

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。

Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.

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。

Solana development patterns used in the Pump SDK — Anchor IDL-based program interaction, SPL Token and Token-2022 management, transaction construction with instruction composition, RPC batching, and cross-program coordination.

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。

Nix + flake-parts 開発環境の構築パターン: root flake.nix（成果物）+ dev/flake.nix（devShell + CI シェル）を direnv で読み込む構成、および GitHub Actions CI テンプレートを提供する。このスキルは /nix-devenv と明示的に呼び出されたときのみ使用する。キーワードマッチによる自動起動はしない。

システムログを分析してエラーの根本原因を特定し、段階的に深堀りしていくデバッグ専門家スキル。 アプリケーションログ、システムログ、クラウドサービスログなど様々な形式に対応。 5 Whys、タイムライン分析、Fishbone分析などのRCA（根本原因分析）手法を用いて 問題の本質を突き止め、再発防止策まで提案する。 Use when analyzing system logs to find error root causes, debugging application issues, or performing technical post-mortem analysis with log data. For organizational incident management processes (post-incident review, corrective action plans, incident reports without log data), use incident-rca-specialist instead. Triggers: "analyze this log", "find the root cause", "debug this error", "why is this failing", "log analysis", "what caused this crash", "troubleshoot this issue"