997 件 ・ 関連度順
概要と使いどころ
Provides AWS CloudFormation patterns for EC2 instances, Security Groups, IAM roles, and load balancers. Use when creating EC2 instances, SPOT instances, Security Groups, IAM roles for EC2, Application Load Balancers (ALB), Target 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 CloudFormation patterns for Amazon S3. Use when creating S3 buckets, policies, versioning, lifecycle rules, 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 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 CloudFormation patterns for CloudWatch monitoring, metrics, alarms, dashboards, logs, and observability. Use when creating CloudWatch metrics, alarms, dashboards, log groups, log subscriptions, anomaly detection, synthesized canaries, Application Signals, and implementing template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and CloudWatch best practices for monitoring production infrastructure.
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 for unit testing Spring Cache annotations (@Cacheable, @CachePut, @CacheEvict). Generates test code that mocks cache managers, verifies cache hit/miss behavior, tests cache key generation with SpEL expressions, validates eviction strategies, and checks conditional caching scenarios. Triggers: caching tests, test Spring cache, mock cache, Spring Boot caching, cache hit/miss verification, @Cacheable testing.
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 Auto Scaling including EC2, ECS, and Lambda. Use when creating Auto Scaling groups, launch configurations, launch templates, scaling policies, lifecycle hooks, and predictive scaling. Covers template structure with Parameters, Outputs, Mappings, Conditions, cross-stack references, and best practices for high availability and cost optimization.
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 and configurations for running WireMock as a standalone Docker container. Generates mock HTTP endpoints, creates stub mappings for testing, validates integration scenarios, and simulates error conditions. Use when you need to mock APIs, create a mock server, stub external services, simulate third-party APIs, or fake API responses for integration testing.
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 for testing utility classes, static methods, and helper functions. Validates pure functions, null handling, edge cases, and boundary conditions. Generates AssertJ assertions and @ParameterizedTest for string utils, math utils, validators, and collection helpers. Use when testing utils, test helpers, helper functions, static methods, or verifying utility code correctness.
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。
Track construction retainage/retention amounts. Monitor held amounts by subcontractor, track release conditions, and manage retainage billing.
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 overhead, profit, and markup to CWICR estimates. Calculate indirect costs, general conditions, and contractor margins.
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 geographic location factors to CWICR estimates. Adjust costs for regional labor rates, material prices, and market conditions.
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。
IoT-based material tracking for construction sites. Monitor material delivery, storage conditions, usage, and inventory with sensors, RFID, GPS, and real-time dashboards.
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 automated daily progress reports from site data. Track work completed, labor hours, equipment usage, and weather conditions.
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。
Monitor environmental conditions on construction sites. Track air quality, noise levels, vibration, dust, and weather to ensure compliance and worker safety.
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。
Manages parent/child agent relationships with task delegation and result aggregation. Supports sequential chains, parallel fans, conditional routing, retry logic, timeout handling, and YAML-based visual workflow definition.
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。
Takes a manual business workflow description and designs the automated version. Maps current steps, handoffs, decision points, and bottlenecks. Designs automated flow with triggers, conditions, actions, and error handling. Outputs workflow-automation.md with before/after Mermaid diagrams, tool recommendations, implementation steps, and time savings estimate.
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。
CHAIN multiple existing skills into custom multi-step workflows. Programmable skill combinations with automatic handoffs. Create composite skills from building blocks with conditional logic.
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。
Implements hub-and-spoke lazy loading to minimize token usage in large skills. Use when building multi-module skills that need conditional on-demand loading.
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。
Pick which precise covariance estimator to use for a given dataset. Use when you have data X and are unsure which estimator fits its dimension, conditioning, or tail behavior. Wraps precise.suggest() and covariance_features().
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 DreamCraft3D for image-conditioned 3D generation, staged optimization, texture boosting, export, and CUDA troubleshooting.
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 denoising-diffusion-pytorch for PyTorch DDPM/DDIM image diffusion, 1D sequence diffusion, conditioning and guidance, and advanced diffusion variants.
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。
Operate ComfyUI-LTXVideo custom nodes for LTX-2 video, audio, IC-LoRA, prompt-conditioning, and advanced ComfyUI 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。
CPU-only operating guidance for the BEHAVIOR-1K BDDL 3.7.0 package: symbolic activity definitions, condition parsing/evaluation, object taxonomy, and generated-data-backed knowledge-base inspection.
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 audio-diffusion-pytorch for PyTorch waveform diffusion generators, text-conditioned audio generation, inpainting, upsampling, vocoding, and diffusion autoencoding.
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。