「database」の検索結果

Azure Cosmos DB SDK for Java. NoSQL database operations with global distribution, multi-model support, and reactive 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。

Deploy web apps with backend APIs, database, and file storage. Use when the user asks to deploy or publish a website or web app and wants a public URL. Uses HTTP API via curl.

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。

n8n workflow automation knowledge base. Provides n8n node information, node functionality details, workflow patterns, and configuration examples. Covers triggers, data transformation, data input/output, AI integration, covering 10 nodes. Keywords: n8n, workflow, automation, node, trigger, webhook, http request, database, ai agent.

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。

Save, load, update, delete, merge, and navigate entity objects using TMS Aurelius ORM (TObjectManager). Use when the user asks how to persist or retrieve data with Aurelius, manage object lifetime, work with associations or collections at runtime, handle transactions, use cached updates, or control concurrency. Does NOT cover query DSL or criteria/projections (those are in a separate skill). Triggers on requests like "save an entity with Aurelius", "load a customer from the database", "add items to a collection", "how do I update an object", "flush changes", "delete a record with Aurelius", "merge a transient object", "how do transactions work in Aurelius", "batch updates Aurelius".

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。

Map Delphi classes to a relational database using TMS Aurelius ORM attributes. Use when the user asks to create entity classes, add Aurelius mapping to existing classes, fix or review mapping attributes, explain how a class is mapped, or work with associations, inheritance, automapping, nullable fields, blobs, or composite identifiers. Triggers on requests like "create Aurelius entities for...", "map this class to Aurelius", "add ORM mapping", "fix the mapping on this class", "how do I map a one-to-many in Aurelius".

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 Wasp's built-in features to your app: auth, email, jobs, and more. These are full-stack, batteries-included features that Wasp handles for you. Use when the user wants to add meta tags, authentication (email, social auth providers), email sending, database setup, styling (tailwind, shadcn), or other Wasp-powered functionality.

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 modify backend APIs, services, jobs, persistence, and integrations while preserving contracts, authorization, data integrity, and failure behavior. Use for server-side features, database changes, background processing, backend defects, and service refactors; do not use for frontend-only or infrastructure-only 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。

Notion API via curl: pages, databases, blocks, search.

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 database migration files for naming conventions and dangerous operations. Auto-activates for migration files.

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。

Bun runtime, package manager, bundler, and test runner. Use when running scripts with bun, managing packages, serving HTTP with Bun.serve, querying databases with Bun.sql/bun:sqlite/Bun.redis, shell scripting with $, using S3/file I/O, writing tests with bun:test, bundling or compiling to executable, or using any Bun-specific API (spawn, glob, semver, FFI, workers, plugins, HTMLRewriter).

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。

Scalingo is a European (French) Platform-as-a-Service for deploying and operating web applications, background workers, and managed databases. Use this skill whenever the user mentions Scalingo, wants to deploy an app to Scalingo, work with the scalingo CLI, provision addons (PostgreSQL, MySQL, MongoDB, Redis, OpenSearch, InfluxDB), configure review apps, manage the scalingo.json manifest or a Procfile, scale containers, set up log drains, migrate from Heroku, use the Scalingo Terraform provider, or target sovereign regions (osc-fr1, osc-secnum-fr1 SecNumCloud). Also trigger for "deploy a French PaaS", "HDS-compliant hosting", "SecNumCloud deployment", or questions about Scalingo's Heroku-compatible buildpack model.

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。

Plan, write, and maintain automated tests in any language, with worked examples in Python and JavaScript/TypeScript. Use this skill whenever the user wants to write or plan tests, apply TDD (red-green-refactor), structure unit tests with arrange-act-assert and given-when-then naming, choose between fakes, stubs, and mocks, set up dependency injection for testability, build integration tests with testcontainers, database seeding, or transaction rollback, write Playwright or Cypress end-to-end tests, use property-based testing with Hypothesis or fast-check, create pytest fixtures or factory_boy factories, review coverage or mutation testing, or triage flaky tests in CI. Also trigger for "test pyramid", "test trophy", "test isolation", "mocking", "snapshot tests", "golden files", "parametrized tests", "make the tests pass".

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 find and fix performance problems in applications, frontend and backend, by measuring first. Use this skill whenever the user reports slow endpoints, high latency, poor throughput, memory leaks, large bundles, or slow page loads; wants to profile with cProfile, py-spy, Chrome DevTools, Node --cpu-prof, or perf; run load tests with locust or k6; set performance budgets or SLOs; optimize database queries (EXPLAIN, indexes, N+1); add caching (Redis); improve frontend performance (code splitting, lazy loading, image optimization, memoization, virtualization); or tune the network (CDNs, gzip/br, HTTP/2/3). Also trigger for p50/p95/p99 latency, throughput, error rate, connection pooling, async vs threads, GIL, and memory leak investigations.

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, extend, validate, and publish a ChatGPT Sites application whose durable database and backend functions run on Convex. Use when a user invokes $codex-sites-convex, selects this skill from the skill picker, asks for a ChatGPT Site backed by Convex, or asks to add Convex data, realtime queries, mutations, authentication, files, schedules, or actions to an existing ChatGPT Sites project.

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Continuously detect recurring UI/UX concepts during frontend discussions, track conversation-wide recurrence, and register repeated concepts as structured knowledge assets in a Notion database via MCP. Activates when UI/UX principles, heuristics, laws, or design patterns recur during frontend work, or on manual @ux commands (@ux save, @ux skip, @ux link).

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。

Get started with your Open SaaS project — fetches docs, checks Wasp installation, and helps you start your database and 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。

Use when sending notifications via mail, Slack, database, or custom channels — with queuing, on-demand recipients, and notification preferences.

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。

Projete esquemas de banco de dados robustos e escaláveis para SQL e NoSQL. Fornece diretrizes de normalização, estratégias de indexação, padrões de migração, design de restrições e otimização de desempenho. Garante integridade de dados, desempenho de consultas e modelos de dados mantíveis.

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。

Princípios de design de banco de dados e tomada de decisão. Design de schema, estratégia de indexação, seleção de ORM, bancos de dados serverless.

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。

Otimizador de banco de dados especializado em tuning de desempenho moderno, otimização de queries e arquiteturas escaláveis.

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Domine migrações de schema e dados em ORMs (Sequelize, TypeORM, Prisma), incluindo estratégias de rollback e deployments sem downtime.

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。

Arquiteto de banco de dados especializado em design de camada de dados do zero, seleção de tecnologia, modelagem de schemas e arquiteturas de bancos de dados escaláveis.

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Banco de dados de embeddings de código aberto para aplicações de IA. Armazene embeddings e metadados, realize buscas vetoriais e full-text, filtre por metadados. API simples com 4 funções. Escala de notebooks para clusters de produção. Use para busca semântica, aplicações RAG ou recuperação de documentos. Melhor para desenvolvimento local e projetos de código aberto.

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。

Banco de dados vetorial gerenciado para aplicações de IA em produção. Totalmente gerenciado, com auto-scaling, busca híbrida (vetores densos + esparsos), filtragem de metadados e namespaces. Latência baixa (<100ms p95). Use para RAG em produção, sistemas de recomendação ou busca semântica em escala. Ideal para infraestrutura serverless e gerenciada.

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。