「rds」の検索結果

Expert knowledge for Azure DevOps development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when managing org/projects, repos, pipelines, work items, dashboards/analytics, or Azure DevOps Server deployments, and other Azure DevOps related development tasks. Not for Azure Boards (use azure-boards), Azure Pipelines (use azure-pipelines), Azure Repos (use azure-repos), Azure Test Plans (use azure-test-plans).

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 knowledge for Azure Boards development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, and integrations & coding patterns. Use when managing work items, Agile/Scrum processes, WIQL queries, Excel/Office integrations, or GitHub links, and other Azure Boards related development tasks. Not for Azure DevOps (use azure-devops), Azure Pipelines (use azure-pipelines), Azure Repos (use azure-repos), Azure Test Plans (use azure-test-plans).

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。

Amazon listing builder and optimizer for sellers. Two modes: (A) Create — build keyword-optimized listings from scratch using keyword lists + product characteristics + AI copywriting, (B) Optimize — audit existing listings, find keyword gaps, score across 8 dimensions, and rewrite with missing keywords. Integrates with amazon-keyword-research for keyword input. Works on 12 Amazon marketplaces. No API key required. Use when: (1) creating a new Amazon listing from keywords, (2) auditing an existing listing for SEO and conversion, (3) checking keyword coverage in title/bullets/description, (4) generating listing copy with target keywords and tone, (5) comparing listings against competitors, (6) preparing a listing for launch or relaunch.

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 EVERY Google Search Console property at once — rank all sites by clicks and impressions with period-over-period deltas, then diff keywords per site to surface what is newly ranking, rising, dropping, lost, or ranking well without earning clicks. Built for agencies and multi-site owners. Use when asked to compare all sites, rank properties by traffic, find new keywords across a portfolio, or spot which site is down. Trigger phrases: "audit GSC", "all my sites", "rank my properties", "portfolio search performance", "which sites are down", "what new keywords are we ranking for", "client site performance", "GSC report across accounts". For ONE site's queries, pages, or index coverage, use the search-console skill instead.

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 50-200+ keywords into topical clusters for SEO domination. Build content roadmaps for topical authority. Triggers on: "keyword research", "keyword clustering", "topical authority", "keyword map", "keyword strategy", "content roadmap for SEO", "keyword grouping", "topic clusters", "SEO keyword plan", "map my keywords", "keyword cluster", "hub and spoke content", "build topical authority", "SEO content plan", "keyword universe".

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 HTML dashboards with KPI metric cards, bar/pie/line charts, progress indicators, and data visualizations. Use when users request dashboards, metrics displays, KPI visualizations, data charts, or monitoring 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。

根据用户指定的国家、稀有度和单位类型，生成符合Kards游戏风格的单位卡牌，包含名称、特殊规则及数值属性。

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。

Creates comprehensive dashboard and analytics interfaces that combine data visualization, KPI cards, real-time updates, and interactive layouts. Use this skill when building business intelligence dashboards, monitoring systems, executive reports, or any interface that requires multiple coordinated data displays with filters, metrics, and visualizations working together.

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。

使用火山引擎 RDS SQL Server，当用户需要执行 RDS SQL Server 相关的实例管理、数据库操作、账号管理和运维任务时使用该skills，可直接调用 uv run ./scripts/call_rds_mssql.py 脚本获取实时结果。

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 you need to use Spring Data JDBC with Java records — including entity design with records, repository pattern, immutable updates, aggregate relationships, custom queries, transaction management, and avoiding N+1 problems. This should trigger for requests such as Review Java code for Spring Data JDBC; Apply best practices for Spring Data JDBC in Java code; Model Spring Data JDBC aggregates with Java records; Review Spring Data JDBC repositories and aggregate boundaries; Improve optimistic locking or domain events in Spring Data JDBC. Part of Plinth Toolkit

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Query 2021-2025 CUMCM school awards and 2026 forecasts, or assess a student's preparation distance from provincial and national awards using school history and prior modeling experience.

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Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability 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。

Provides AWS CloudFormation patterns for Amazon RDS databases. Use when creating RDS instances (MySQL, PostgreSQL, Aurora), DB clusters, multi-AZ deployments, parameter groups, subnet groups, and implementing template structure with Parameters, Outputs, Mappings, Conditions, and cross-stack references.

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 AWS RDS (Relational Database Service) management patterns using AWS SDK for Java 2.x. Use when creating, modifying, monitoring, or managing Amazon RDS database instances, snapshots, parameter groups, and configurations.

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 patterns to configure AWS RDS (Aurora, MySQL, PostgreSQL) with Spring Boot applications. Configures HikariCP connection pools, implements read/write splitting, sets up IAM database authentication, enables SSL connections, and integrates with AWS Secrets Manager. Use when setting up RDS connections in Spring Boot, configuring connection pooling, or managing database credentials securely.

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。

Check data compliance with construction standards. Validate data against ISO 19650, IFC, COBie, UniFormat 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 Databricks AI/BI dashboards. Must use when creating, updating, or deploying Lakeview dashboards as Databricks Dashboard have a unique json structure. CRITICAL: You MUST test ALL SQL queries via CLI BEFORE deploying. Follow guidelines strictly.

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。

Applies stewardship virtues (Care, Curiosity, Humility, Diligence) to plugin work. Use when authoring plugins or reviewing code quality.

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。

Guides implementation of technical, contractual, and organisational supplementary measures for international data transfers per EDPB Recommendations 01/2020. Covers encryption, pseudonymisation, split processing, audit rights, transparency obligations, and internal policies. Keywords: supplementary measures, encryption, pseudonymisation, EDPB recommendations, transfer safeguards.

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。

Creates GDPR Article 30(2) Records of Processing Activities for data processors with all four mandatory fields: processor and controller names and contact details, categories of processing, third country transfers, and security measures description. Activate for processor RoPA, Art. 30(2), processor records, sub-processor documentation.

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。

Guides systematic review of processing documentation for completeness against GDPR Articles 5, 13-14, 24, 28, and 30. Activate when auditing documentation or preparing for inspections. Keywords: documentation review, processing records, completeness, privacy notices, RoPA.

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。

Structures risk mitigation planning and residual risk tracking for Data Protection Impact Assessments under GDPR Article 35(7)(d). Covers mitigation measure identification, implementation tracking, residual risk acceptance, and Art. 36 prior consultation triggers. Keywords: DPIA mitigation, risk treatment, residual risk, Art. 35(7)(d), safeguards, mitigation tracking, prior consultation.

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。

Creates GDPR Article 30(1) Records of Processing Activities (RoPA) for data controllers with all seven mandatory fields: controller identity and contact details, processing purposes, data subject categories, personal data categories, recipient categories, third country transfers, and retention periods. Includes Python generator for automated RoPA creation. Activate for controller RoPA, Art. 30(1), processing records, data mapping.

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Guides privacy audit evidence collection processes including evidence planning, sampling strategies, documentation standards, chain of custody, interview techniques, system walkthrough procedures, and evidence evaluation. Covers ISO 19011 evidence categories (records, statements of fact, observations) and ISACA audit evidence requirements for privacy compliance assessments. Keywords: audit evidence, evidence collection, sampling, chain of custody, audit documentation, interview techniques.

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。