「ai-design」の検索結果

プロジェクトのデザインシステムを SSOT として作成・監査・更新する。トークン、デザインシステムに関わるアクセシビリティ、Typography、Motion、aesthetic direction の作業に使う。デザインシステムに紐づかない単発UI実装や個別デザインレビューには使わない。ユーザーが /ai-design-system と入力したら使う。

Audit websites, apps, screenshots, mockups, and design code for harmful AI-design clichés, generic generated defaults, and established UI defects. Use when the user wants evidence-backed design feedback, an anti-slop review, or a removal-first cleanup plan without a speculative redesign.

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。

Audit and rewrite frontend UI to remove generic AI design patterns ("AI slop"), both the first-order defaults (purple gradients, Inter, a centered hero over three cards, untouched shadcn) and the second-order "tasteful AI" ones (cream with terracotta, near-black with acid green, all-caps mono chrome, one accented headline word, decorative 01/02/03). Use when asked to "de-slop a UI", "make a design look less AI-generated", "audit a page or component for AI design tells", "fix Claude/Codex-generated frontend that looks generic", or keep a multi-page site on one design system. Covers HTML/CSS and React/Tailwind/shadcn. Has a detection-only mode and a zero-dependency scanner (scripts/detect.mjs) for the code-certain tells.

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。

Prevent and remove generic AI-generated design defaults, incoherent visual choices, and established UI defects while creating, revising, or reviewing websites, apps, screenshots, mockups, and design code. Use as a passive quality gate during UI work or for an explicit anti-slop cleanup; preserve the chosen art direction instead of forcing a neutral redesign.

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 GitHub Actions workflows with proper syntax, reusable workflows, composite actions, matrix builds, caching, and security best practices. Use when creating CI/CD workflows for GitHub-hosted projects or automating GitHub repository 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。

Strategic guidance for choosing and implementing testing approaches across the test pyramid. Use when building comprehensive test suites that balance unit, integration, E2E, and contract testing for optimal speed and confidence. Covers multi-language patterns (TypeScript, Python, Go, Rust) and modern best practices including property-based testing, test data management, and CI/CD 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。

Build event streaming and real-time data pipelines with Kafka, Pulsar, Redpanda, Flink, and Spark. Covers producer/consumer patterns, stream processing, event sourcing, and CDC across TypeScript, Python, Go, and Java. When building real-time systems, microservices communication, or data integration pipelines.

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。

When validating system performance under load, identifying bottlenecks through profiling, or optimizing application responsiveness. Covers load testing (k6, Locust), profiling (CPU, memory, I/O), and optimization strategies (caching, query optimization, Core Web Vitals). Use for capacity planning, regression detection, and establishing performance SLOs.

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。

Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing 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。

Relational database implementation across Python, Rust, Go, and TypeScript. Use when building CRUD applications, transactional systems, or structured data storage. Covers PostgreSQL (primary), MySQL, SQLite, ORMs (SQLAlchemy, Prisma, SeaORM, GORM), query builders (Drizzle, sqlc, SQLx), migrations, connection pooling, and serverless databases (Neon, PlanetScale, Turso).

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。

Reduces attack surface across OS, container, cloud, network, and database layers using CIS Benchmarks and zero-trust principles. Use when hardening production infrastructure, meeting compliance requirements, or implementing defense-in-depth security.

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。

Time-series database implementation for metrics, IoT, financial data, and observability backends. Use when building dashboards, monitoring systems, IoT platforms, or financial applications. Covers TimescaleDB (PostgreSQL), InfluxDB, ClickHouse, QuestDB, continuous aggregates, downsampling (LTTB), and retention policies.

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。

Authentication, authorization, and API security implementation. Use when building user systems, protecting APIs, or implementing access control. Covers OAuth 2.1/OIDC, JWT patterns, sessions, Passkeys/WebAuthn, RBAC/ABAC/ReBAC, policy engines (OPA, Casbin, SpiceDB), managed auth (Clerk, Auth0), self-hosted (Keycloak, Ory), and API security best practices.

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。

Provides design token system and theming framework for consistent, customizable UI styling across all components. Covers complete token taxonomy (color, typography, spacing, shadows, borders, motion, z-index), theme switching (CSS custom properties, theme providers), RTL/i18n support (CSS logical properties), and accessibility (WCAG contrast, high contrast themes, reduced motion). This is the foundational styling layer referenced by ALL component skills. Use when theming components, implementing light/dark mode, creating brand styles, customizing visual design, ensuring design consistency, or supporting RTL languages.

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 implement Internal Developer Platforms (IDPs) with self-service capabilities, golden paths, and developer experience optimization. Covers platform strategy, IDP architecture (Backstage, Port), infrastructure orchestration (Crossplane), GitOps (Argo CD), and adoption patterns. Use when building developer platforms, improving DevEx, or establishing platform teams.

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 dashboards, reports, and data-driven interfaces requiring charts, graphs, or visual analytics. Provides systematic framework for selecting appropriate visualizations based on data characteristics and analytical purpose. Includes 24+ visualization types organized by purpose (trends, comparisons, distributions, relationships, flows, hierarchies, geospatial), accessibility patterns (WCAG 2.1 AA compliance), colorblind-safe palettes, and performance optimization strategies. Use when creating visualizations, choosing chart types, displaying data graphically, or designing data interfaces.

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。

Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.

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。

Managing cloud infrastructure using declarative and imperative IaC tools. Use when provisioning cloud resources (Terraform/OpenTofu for multi-cloud, Pulumi for developer-centric workflows, AWS CDK for AWS-native infrastructure), designing reusable modules, implementing state management patterns, or establishing infrastructure deployment workflows.

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。

Apply and enforce cloud resource tagging strategies across AWS, Azure, GCP, and Kubernetes for cost allocation, ownership tracking, compliance, and automation. Use when implementing cloud governance, optimizing costs, or automating infrastructure management.

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。

Writing optimized, secure, multi-stage Dockerfiles with language-specific patterns (Python, Node.js, Go, Rust), BuildKit features, and distroless images. Use when containerizing applications, optimizing existing Dockerfiles, or reducing image sizes.

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。

Optimize SQL query performance through EXPLAIN analysis, indexing strategies, and query rewriting for PostgreSQL, MySQL, and SQL Server. Use when debugging slow queries, analyzing execution plans, or improving database 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。

Document database implementation for flexible schema applications. Use when building content management, user profiles, catalogs, or event logging. Covers MongoDB (primary), DynamoDB, Firestore, schema design patterns, indexing strategies, and aggregation pipelines.

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。

Vector database implementation for AI/ML applications, semantic search, and RAG systems. Use when building chatbots, search engines, recommendation systems, or similarity-based retrieval. Covers Qdrant (primary), Pinecone, Milvus, pgvector, Chroma, embedding generation (OpenAI, Voyage, Cohere), chunking strategies, and hybrid search 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。

Transform raw data into analytical assets using ETL/ELT patterns, SQL (dbt), Python (pandas/polars/PySpark), and orchestration (Airflow). Use when building data pipelines, implementing incremental models, migrating from pandas to polars, or orchestrating multi-step transformations with testing and quality checks.

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。