「databases」の検索結果

Use this skill for ANY work involving React, Next.js, TypeScript, or Tailwind in the browser layer. This includes building components and pages, but equally covers debugging and fixing frontend issues: CSS/Tailwind classes not applying, form validation behavior, hydration mismatches between server and client renders, styling bugs, layout shifts, and rendering problems. Also use for refactoring components (e.g., splitting Server vs Client Components), data fetching patterns, state management, bundle optimization, and frontend tooling. If the problem involves what users see or interact with in a web browser — whether building, fixing, or refactoring — use this skill. Not for backend APIs, databases, infrastructure, or DevOps.

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Use when the user wants to know if something works — the answer requires running code, not analyzing it. Output is a verdict backed by evidence: passed, failed, or broken. Primary triggers: 'run the tests', 'does X still work after my change?', 'did the merge break anything?', 'verify the fix worked', 'check if the endpoint returns X', 'confirm nothing regressed', 'run tests/unit/test_foo.py', 'let me know the results', 'make sure my changes didn't break anything'. Hard stops — do NOT use for: reviewing test code for quality/coverage gaps, debugging why test infrastructure/databases/seed scripts are misbehaving, writing or fixing tests, diagnosing root causes of unexpected behavior. The deciding question: is the user asking for the result of executing something, or asking for help understanding/analyzing/improving something? If it's the latter, use diagnose or review-code instead.

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Recommend technology stacks based on project requirements, team expertise, and constraints. Use when selecting frameworks, languages, databases, and infrastructure for new projects.

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 with searchable style, palette, typography, and chart databases. Use when designing UI components, choosing colors/fonts, reviewing code for UX issues, building landing pages, or implementing responsive layouts.

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。

Master Coolify operator for self-hosted deployment platform. Use when the user mentions 'coolify', 'deploy on coolify', 'list/restart/redeploy applications', 'view coolify logs', 'coolify API/CLI', 'manage coolify servers/databases/apps', or 'coolify context'. Automates deployments and management via REST API or official CLI.

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Transform raw data from CSVs, Google Sheets, or databases into executive-ready reports with visualizations, key metrics, trend analysis, and actionable recommendations. Creates data-driven narratives for leadership. Use when users need to turn spreadsheets into executive summaries or board reports.

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Configures and hardens Claude Code and Codex MCP servers. Use when connecting databases, APIs, SaaS, building servers, or serving a clearance-filtered knowledge base.

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。

Orchestrating migrations, upgrades, and modernization across frameworks, libraries, APIs, databases, and dependencies. Generates codemods, applies Strangler Fig, verifies equivalence, plans rollback.

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。

Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.

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Detect graphic trademarks in product images by comparing against registered trademark databases across 15 major trademark offices using YOLO-based object detection and visual similarity. Triggered when users mention graphic trademark detection, logo infringement check, trademark similarity analysis, image trademark risk assessment, product image trademark screening, or logo detection. Even if the user does not explicitly say "trademark detection," this skill should be triggered whenever the need involves comparing a product image against registered graphic trademarks or assessing trademark infringement risk.

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Craft Cloud — Pixel & Tonic's serverless hosting platform for Craft CMS. Covers craft-cloud.yaml configuration, the Build → Migrate → Release deploy pipeline, the craftcms/cloud extension package, edge image transforms via Cloudflare, edge static caching with cache.rules + ESI, Cloud-managed S3 filesystem, MySQL 8 / Postgres 15 databases (no MariaDB, no tablePrefix), Console-based command runner and scheduled cron (once-per-hour minimum), auto-handled queue jobs, custom domains and SSL, preview environments per branch, Cloud limitations (ephemeral filesystem, no SSH, no .htaccess, no built-in mail), plugin development requirements for Cloud compatibility, and self-hosted → Cloud migration.

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Servd (servd.host) — Craft-specialised managed hosting for Craft CMS. Covers git push-to-deploy with the optional servd.yaml build config, local → staging → production environments with uni-directional Project Config sync, the servd/craft-asset-storage plugin (S3-backed Flysystem volumes on the svdcdn.com CDN, off-server image transforms, Imager-X/ImageOptimize integrations), Servd's static caching (full vs tag-based purge, {% dynamicInclude %}, CSRF injection, cache-busting) and running Blitz alongside it in reverse-proxy mode, MariaDB/MySQL databases over an SSH tunnel, automatic + manual backups, the Dedicated Queue Runner, environment variables and secrets, the ephemeral load-balanced filesystem (Redis + remote volumes for runtime files), plugin/feature constraints, and Servd-vs-Craft-Cloud differences.

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 installing or operating Univer CLI for .univer files, spreadsheets, documents, slides, Base databases, Board canvases, cross-Unit embedding, worktrees, Facade authoring, inspection, verification, import/export, screenshots, or viewer handoff.

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。

Data engineer — databases, migrations, pipelines, schema design, and query optimization. Use when asked to "design a schema", "write a migration", "build a data pipeline", or "optimize this query".

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Expert guide for high-performance Vector Databases, Deep RAG architectures, pgvector 0.8+ HNSW, Reciprocal Rank Fusion (RRF), Cross-Encoder Re-ranking, and Late Chunking / Panduan ahli Vector DB, arsitektur Deep RAG, pgvector HNSW, RRF, dan Re-ranking.

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Supabase security expert to audit RLS (Row Level Security), RBAC, relational databases, prevent data leakage, and utilize Supabase Linter / Ahli keamanan Supabase untuk audit RLS (Row Level Security), RBAC, database relasional, pencegahan kebocoran data, dan pemanfaatan Supabase Linter.

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。

Expert guide for integrating Large Language Models (LLMs), Model Context Protocol (MCP v1.x), hybrid reasoning models, RAG architecture, vector databases, and AI agents / Panduan ahli untuk integrasi LLM, Model Context Protocol (MCP), model hybrid reasoning, arsitektur RAG, vector database, dan agen AI.

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。

Run CodeQL static analysis for security vulnerability detection, taint tracking, and data flow analysis. Use when asked to analyze code with CodeQL, create CodeQL databases, write custom QL queries, perform security audits, or set up CodeQL in CI/CD 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。

Compositional algorithm and data analysis via algebraic databases

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Bridge layer connecting AlgebraicJulia/Catlab.jl to plurigrid/asi. Wires ACSets (attributed C-sets), wiring diagrams, decorated cospans, and the AlgebraicJulia ecosystem (AlgebraicDynamics, AlgebraicPetri, AlgebraicRewriting, Decapodes) into the ASI skill graph. ACSets generalize relational databases with categorical semantics; every diagram, network, and model in the ecosystem is an ACSet.

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Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases.

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。

Attributed C-Sets as algebraic databases. Category-theoretic data structures generalizing graphs and dataframes with Gay.jl color 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。

Manage Alibaba Cloud RDS using the @alicloud/rds20140815 TypeScript SDK. Use when working with relational database instances (MySQL, PostgreSQL, SQL Server, MariaDB), accounts, databases, backups, security, monitoring, parameters, read-only instances, database proxy, migration, cross-region DR, PostgreSQL extensions, RDS Custom instances, and resource tagging. Covers all 398 APIs of the RDS 20140815 version.

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 integration tests using TestContainers for .NET with xUnit. Covers infrastructure testing with real databases, message queues, and caches in Docker containers instead of mocks.

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。