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概要と使いどころ
Implement Kubernetes security policies including NetworkPolicy, PodSecurityPolicy, and RBAC for production-grade security. Use when securing Kubernetes clusters, implementing network isolation, or enforcing pod security standards.
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 web interfaces with intentional aesthetic direction. Avoids generic AI aesthetics (centered layouts, Inter/Arial, purple gradients, uniform corners). Use for UI components, dashboards, landing pages, React/HTML/CSS layouts where design quality matters.
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。
Rules for Drizzle ORM schema design, query patterns, migration workflows, and relational query usage. Ensures type-safe, production-ready database interactions.
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 and ML expert covering PyTorch, TensorFlow, Hugging Face, scikit-learn, LLM integration, RAG pipelines, MLOps, and production ML systems
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 production-grade HackerOne reports — title formula, impact-led writing, CVSS 3.1 scoring, complete PoC formatting, attached files, and submission etiquette. Use AFTER triage-validation passes and you're ready to submit on HackerOne.
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。
Never test mock behavior. Never add test-only methods to production classes. Understand dependencies before mocking.
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。
Generate 5 distinct, production-grade UI variations for a requested new or existing section, component, page, flow, or shell, then help the user preview and apply one. Use when the user asks to add or redesign UI like a hero, pricing, navbar, auth flow, dashboard shell, ecommerce surface, or data component.
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 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。
Convert Figma designs into production-ready code using MCP server tools. Use this skill when users provide Figma URLs, request design-to-code conversion, ask to implement Figma mockups, or need to extract design tokens and system values from Figma files. Works with frames, components, and entire design files to generate HTML, CSS, React, or other frontend 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。
Deploy and manage Netlify projects using Next.js with serverless functions, environment variables, and continuous deployment. Use this skill when working with Netlify deployments, configuring netlify.toml, managing Netlify Functions, debugging webhooks, setting environment variables, or troubleshooting deployment issues for Next.js applications on Netlify. Essential for Twilio-Aldea and similar serverless SMS/telephony projects. v2.0 includes official Netlify documentation, production patterns from Twilio-Aldea (webhook timeout solutions, background functions), and TypeScript code examples.
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 cinematic case study pages for films, documentaries, photography portfolios, and creative projects — film-frame hero images, structured crew credit grids, dark footage galleries, festival award sections, scroll-choreographed reveals via data-attribute animation orchestration. Warm cream palette with analog typography. Single-file HTML, zero build step. Triggers on film case study, documentary showcase, filmmaker portfolio, production company website, film credits page, creative project case study, director portfolio, photography case study.
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。
Research UI component patterns across the component.gallery corpus — 60 canonical component types, ~95 production design systems, 2,600+ examples — plus an AI-era overlay (prompt inputs, tool-call cards, streaming status, citations) and composite patterns (command palette, app shell, agent console). Compare implementations, find alternative component names, ground frontend decisions in real-world precedent. Pairs with minoan-frontend-design for research-then-build workflows. Triggers on component patterns, how do others implement X, design system research, component comparison, AI chat components, agent UI patterns.
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 distinctive, production-grade frontend interfaces — websites, landing pages, dashboards, React components, HTML/CSS layouts, editorial heroes with depth-parallax (Astryx pattern), pointer-reactive fluid effects, pixel-glitch canvas heroes and terminal-style tool-panel dioramas (Composio pattern). Avoids generic AI aesthetics. Triggers on web UI, styling, beautify, frontend design, dashboard, landing page, React component, editorial hero, glitch hero, dev-tool landing page.
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 production-ready React/Next.js code from Figma designs. Instructs proper Figma MCP tool usage, component reuse strategies, and variant mapping. **CRITICAL: ALWAYS use this skill when user provides a Figma link or mentions implementing/redesigning from Figma design.** Never attempt manual implementation without invoking this skill first.
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。
Queries the NCATS Translator ARAX production API for bounded, typed, provenance-rich one-hop and endpoint-pinned two-hop biomedical knowledge-graph relationships. Use for Biolink-constrained RTX-KG2 lookup, explicit selected-provider ARAX federation, separate entity normalization, qualifier-aware graph traversal, and inspection of TRAPI edge bindings, publications, and knowledge-source provenance. Do not use for inference, ranking, open-ended pathfinding, clinical guidance, or sensitive queries.
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。
Full AI-assisted workflow for long-form SEO content (2,000+ words). Covers JTBD strategy, E-E-A-T quality standards, and 5 production phases: research, outline, draft, editing, and SEO/publishing. Built for B2B marketing agency context. Use when: user wants to write or optimize an SEO article, mentions "SEO content", "blog post", "GTM article", "long-form content", or asks to create content with keyword strategy.
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 high design quality. Use this skill when the user asks to build web components, pages, or applications.
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 data analysis for quality, correctness, and reproducibility. Covers data quality assessment, assumption checking, model validation, data leakage detection, and reproducibility verification. Use when reviewing a colleague's analysis before publication, validating an ML pipeline before production deployment, auditing a report for regulatory or business decision-making, or performing a second-analyst review in a regulated environment.
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 A/B tests for ML models in production using traffic splitting, statistical significance testing, and canary/shadow deployment strategies. Measure performance differences and make data-driven decisions about model rollout. Use when validating a new model version before full rollout, comparing candidate models trained with different algorithms, measuring business metric impact of model changes, or when regulatory requirements mandate gradual rollout.
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。
Profile and optimize Shiny application performance using profvis, bindCache, memoise, async/promises, debounce/throttle, and ExtendedTask for long-running computations. Use when the app feels slow or unresponsive during user interaction, when server resources are exhausted under concurrent load, when specific operations create bottlenecks, or when preparing an app for production deployment with many concurrent users.
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。
Orchestrate end-to-end machine learning pipelines using Prefect or Airflow with DAG construction, task dependencies, retry logic, scheduling, monitoring, and integration with MLflow, DVC, and feature stores for production ML workflows. Use when automating multi-step ML workflows from data ingestion to deployment, scheduling periodic model retraining, coordinating distributed training tasks, or managing retry logic and failure recovery across pipeline stages.
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 a generative diffusion model (DDPM or score-based) with noise scheduling, U-Net architecture, training loop, and sampling procedures including DDIM acceleration. Use when building a generative model for image, audio, or molecular synthesis; implementing DDPM from a research paper; adding a custom noise schedule or conditioning mechanism; replacing a GAN-based generator with a diffusion alternative; or prototyping before scaling with production frameworks like diffusers.
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 GitOps continuous delivery using Argo CD or Flux with app-of-apps pattern, automated sync policies, drift detection, and multi-environment promotion. Manage Kubernetes deployments declaratively from Git with automated reconciliation. Use when implementing declarative infrastructure management, migrating from imperative kubectl commands to Git-driven deployments, setting up multi-environment promotion workflows, enforcing code review gates for production, or meeting audit and compliance requirements.
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。