7,923 件 ・ 関連度順
概要と使いどころ
Improve or implement purposeful motion systems, micro-interactions, gestures, and transition behavior for production-grade UI. Use when the user mentions animation, motion, transitions, micro-interactions, hover states, drawers, toasts, gestures, 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。
Improve layout composition, spacing systems, grouping, and visual rhythm. Use when the user mentions weak layout structure, arbitrary spacing, weak grouping, crowded composition, or layout monotony rather than priority/emphasis problems.
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 or refine UI color systems, warmth, semantic color, and color-based hierarchy in designs that are too gray, monochromatic, or missing color meaning. Use when the user mentions dull colors, gray UI, missing warmth, weak semantic color, or a need for stronger color hierarchy.
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 distinctive, production-grade frontend interfaces with strong hierarchy, thoughtful systems, and polished implementation that avoid generic AI aesthetics. Use when the user wants to build or redesign web pages, flows, components, or full app surfaces, or when another better-web-ui skill needs shared project design context before other better-web-ui 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。
Extract reusable components, product patterns, and design tokens into a clearer design system with shared APIs, documented defaults, and better reuse. Use when the user wants to refactor repeated UI, consolidate buttons/cards/forms/sections, build a component library, or turn one-off values into reusable tokens.
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 or refine depth through elevation systems, raised and inset surfaces, layered overlap, and meaningful shadow behavior so interfaces feel structured instead of flat. Use when the user mentions depth, elevation, shadows, layering, flat cards, pressed states, drag feedback, or inset 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。
MVVM architecture standards for React/TypeScript frontends with TanStack ecosystem, TailwindCSS, and Shadcn UI.
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。
Routes /sdd-run commands to fullstack-typescript skills or system CLI.
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 EZ_math_model coder or packaging fails repeatedly with the same error and needs root-cause analysis instead of another blind retry.
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。
Phase 2 of 5 — collaborative system design plus change-footprint mapping; outputs an ARCH doc for generate-tasks. Use only when the user asks to run Phase 2, plan the architecture, or design the solution — never trigger automatically from a design 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。
Systematic code review checklist and pre-commit quality gates for PRs. Use when: reviewing pull requests, checking code quality, before committing code. Keywords: review, PR, pull request, checklist, quality, commit, 審查, 檢查, 簽入.
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。
Four-phase debugging framework that ensures root cause investigation before attempting fixes. Never jump to solutions.
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。
Fork, clone to ~/.clank, run installer, edit CLAUDE.md
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。
Systematically trace bugs backward through call stack to find original trigger
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 intentional non-action — deliberate stopping without clearing, assessment, or rebalancing. Recognition that sometimes the most productive response is no response. Every other self-care skill produces output; rest produces silence. Use when all tending skills feel like more activity rather than less, when the system is functioning well but at high utilization, after sustained intensive work, or when the impulse to optimize is itself the problem.
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。
Contribute to OpenClaw ecosystem projects (OpenClaw, NemoClaw, NanoClaw) through a structured 9-step workflow: target verification, codebase exploration, parallel audit, finding cross-reference, and pull request creation. Emphasizes false positive prevention and project convention adherence.
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。
Prepare an organisation for regulatory inspection by assessing readiness against agency-specific focus areas (FDA, EMA, MHRA). Covers warning letter and 483 theme analysis, mock inspection protocols, document bundle preparation, inspection logistics, and response template creation. Use when a regulatory inspection has been announced or is anticipated, when a periodic self-assessment is due, when new systems have been implemented since the last inspection, or after a significant audit finding that may attract regulatory attention.
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。
Qualify a vendor or supplier of GxP-relevant software or services. Covers vendor risk classification, assessment questionnaire design, desk and on-site audit approaches, quality agreement evaluation, SLA review, and ongoing monitoring cadence definition. Use when selecting a new vendor for a GxP-critical system, onboarding a cloud provider for regulated data, performing a periodic re-qualification, when a vendor audit finding requires re-assessment, or when EU Annex 11 or ICH Q10 requires supplier qualification.
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 and execute chaos engineering experiments using Litmus or Chaos Mesh. Test system resilience through controlled fault injection, validate hypothesis-driven tests, and improve failure recovery. Use before major product launches, after architecture changes to validate resilience, during GameDays or disaster recovery drills, to validate assumptions about failure modes, or as part of an SRE maturity program.
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。
Assess and improve garden soil through testing, amendment, composting, and biodynamic preparations. Covers jar test, spade test, earthworm count, amendment by soil type (clay, sandy, depleted, compacted), composting methods (hot, cold, vermicomposting), no-till practices, cover cropping, and biodynamic preparations 500-508. Use when starting a new garden bed, when plants underperform despite adequate water and light, when transitioning to organic or biodynamic practice, when soil has become compacted or depleted, or when building a composting system.
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。
Navigate the Kabbalistic Tree of Life (Etz Chaim) — explore the ten sephirot, twenty-two paths, four worlds, and three pillars. Covers correspondences, contemplative exercises, and structural analysis. Use when exploring a sephira's attributes and divine names, studying structural relationships between sephirot, learning the four worlds (Atzilut through Assiah), tracing a path's Hebrew letter and tarot attributions, or using the Tree as a symbolic map for understanding a concept or system.
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 intuitive exploration for approaching unknown codebases, problems, or systems without preconceptions. Adapts the Coordinate Remote Viewing protocol to AI investigation: cooldown (clear assumptions), staged data gathering (raw signals → dimensional → analytical), AOL management (separating observations from premature labels), and structured review. Use when investigating an unfamiliar codebase with unknown architecture, debugging a problem where premature hypotheses could mislead, exploring a domain with limited context, or when previous attempts have been led astray by assumptions and "beginner's mind" would be more productive.
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。
Monitor, cap, and recover from context accumulation in agentic systems. Covers per-cycle cost tracking, context window auditing, budget caps with enforcement policies, emergency pruning when approaching limits, and progressive disclosure integration to minimize token spend on routing. Use when running long-lived agent loops (heartbeats, polling, autonomous workflows), when context windows are growing unpredictably between cycles, when API costs spike beyond expected baselines, when designing new agentic workflows that need cost guardrails from the start, or when post-mortem analysis reveals a cost incident caused by context accumulation.
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。
Implement comprehensive model drift monitoring using Evidently AI, statistical tests (PSI, KS), and custom metrics to detect data drift and concept drift in production ML systems. Set up automated alerting and reporting workflows to catch degradation before it impacts business metrics. Use when production models show unexplained performance degradation, when new data distributions differ from training data, when seasonal shifts affect input features, or when regulatory requirements mandate model monitoring.
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。