「infrastructure」の検索結果

Guide for authoring Pulumi ComponentResource classes. Use when creating reusable infrastructure components, designing component interfaces, setting up multi-language support, or distributing component packages.

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。

Best practices for using Pulumi Automation API to programmatically orchestrate infrastructure operations. Covers multi-stack orchestration, embedding Pulumi in applications, architecture choices, and common 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。

Migrate Terraform projects to Pulumi. Use when users need to move infrastructure from Terraform to Pulumi, translate HCL configurations, or convert Terraform modules to Pulumi components.

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。

Systematic technical debt analysis across architecture, testing, documentation, and infrastructure. Investigates the codebase, scores findings by impact and effort, and generates a prioritized TECH_DEBT.md remediation plan. Delegates to specialized skills for code quality (scout) and linting (lint-fix). Use when assessing overall project health, planning cleanup sprints, or onboarding to an unfamiliar codebase.

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。

Capacity planning — forecasting demand, sizing infrastructure, and avoiding both outages and waste — use when scaling systems.

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 Terraform infrastructure-as-code for AWS, GCP, or Azure. Creates modular VPC, compute, database, cache, CDN, and monitoring configs with per-environment sizing, remote state, cost estimates, and 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。

Generates production-ready Terraform files for AWS infrastructure. Writes complete .tf files for any cloud architecture — no deployment, just file generation.

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。

Ship an app from code to production-ready infrastructure: containerize with Docker and multi-stage builds, set up GitHub Actions CI/CD with automated testing and deployment, add monitoring with health checks and alerting, then run preflight verification..

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 game analytics and telemetry implementation -- event tracking completeness, FTUE and monetization funnel coverage, retention metric infrastructure, A/B testing framework, heatmap data collection, churn prediction signals, and LTV modeling support..

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 research data management infrastructure -- score FAIR principles compliance (Findable, Accessible, Interoperable, Reusable) across all sub-principles, evaluate data governance frameworks and lifecycle management, assess metadata schema quality (Dublin Core, DataCite, DDI.

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 a codebase for product-market fit readiness -- evaluate startup PMF signals, core value loop tightness, feature scatter vs focus ratio, user activation funnel friction, retention hook infrastructure, pricing flexibility, analytics event coverage, CI/CD iteration speed, and growth-stage maturity for pre-seed through Series A products.

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 ML experiment tracking infrastructure for reproducibility gaps, parameter logging completeness, metric capture, artifact management, and pipeline orchestration. Covers MLflow, Weights and Biases, DVC, Sacred, Neptune, Hydra configs, model registries, and produces a reproducibility scorecard (0-30) with actionable fixes for data science 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。

Analyze fleet fuel optimization systems including MPG consumption analytics, eco-driving behavior scoring, route fuel cost modeling, idling detection and anti-idle programs, IFTA tax compliance reporting, alternative fuel transition planning, EV charging infrastructure readiness, and EPA SmartWay partnership metrics for commercial vehicle fleets.

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 any product from a Customer Success Manager perspective -- evaluate onboarding flow completeness, self-service help infrastructure, customer health signal tracking, support channel accessibility, expansion and retention triggers, and customer communication systems.

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。

Analyzes infrastructure costs at 1K-100K user scales by reading the actual codebase, auto-detecting cloud providers, modeling per-action costs, and projecting total monthly spend with optimization recommendations.

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 polished dark-themed architecture diagrams as self-contained HTML+SVG files. Use when the user asks for system, infrastructure, cloud, security, or network topology diagrams.

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。

Discovers requirements and designs a borderless open data lakehouse using Lakehouse for Apache Iceberg and BigQuery data agents. Use when architecting multi-cloud storage infrastructure (Cloud Storage, AWS S3, Azure Blob), establishing ingestion and AI serving subsystems, configuring Cross-Cloud Interconnect, or deploying Gemini Enterprise Agent Platform and BigQuery data agents. Don't use for single-cloud data warehouses, or when the focus is on Knowledge Catalog metadata governance and Spark-driven IDE analytics workflows (use google-cloud-solution-agentic-analytics-spark-knowledge-catalog 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。

Discovers requirements and designs an end-to-end governed agentic analytics solution using Knowledge Catalog and Managed Service for Apache Spark (Lightning Engine). Use when designing data science and analytics workflows across structured and unstructured distributed data (including in S3, Azure Blob, AlloyDB, and Iceberg), establishing metadata governance with Knowledge Catalog aspect types, or grounding agentic IDEs (VS Code, Antigravity) by using the Google Cloud Data Agent Kit. Don't use for provisioning borderless data lakehouse infrastructure (use google-cloud-solution-agentic-ai-borderless-data-lakehouse 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。

Generates and updates secure, production-ready Kubernetes YAML manifests optimized for GKE Autopilot and GKE Standard clusters. Use when creating or modifying GKE deployment manifests, configuring container security contexts, setting CPU/memory resource limits, defining readiness/liveness/startup probes, mounting secrets and volumes, configuring GKE Gateway API routes, targeting Spot VMs, or deploying AI model inference workloads (vLLM, TGI, Gemma). Don't use for live cluster operations, pod troubleshooting (use gke-workload-troubleshooting), or cluster infrastructure provisioning (use gke-cluster-creation).

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。

Diagnoses GKE workload failures (CrashLoopBackOff, OOMKilled, ImagePullBackOff, Pending, etc.) via logs and events. Use when pods fail to start or crash repeatedly. Don't use for GKE cluster infrastructure provisioning, node pool creation, or non-Kubernetes Google Cloud services.

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 agents through a 6-step discovery process to design and deploy Google Cloud global external Application Load Balancers with Cloud CDN, Cloud Armor, and Service Extensions, mapping workload requirements to best-practice configurations. Use when: - Designing, configuring, or deploying a Google Cloud global external Application Load Balancer, Cloud CDN, Cloud Armor WAF, or Service Extensions. - Discovering existing Google Cloud resources (Cloud Storage, MIGs, GKE, Cloud Run) to use as backends. - Generating production-grade Terraform HCL or gcloud CLI scripts for global external Application Load Balancers. - Actuating deployments via Infrastructure Manager or bash scripts, including IAM pre-checks. - Detecting, analyzing, or reconciling configuration drift on deployed global external Application Load Balancers. Don't use for: - Non-Google Cloud load balancing or security configurations. - Purely regional or internal load balancing setups (unless part of a hybrid/failover global design).

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 safety-critical validation, guardrails, and data reduction for gcloud CLI operations across Google Cloud Platform (GCP) services and infrastructure. Use when planning, generating, constructing, proposing, describing, or executing any gcloud CLI commands - including when answering questions about gcloud syntax, or formatting flags. Don't use when writing Google Cloud client library code or raw REST/gRPC API requests.

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。

Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use `agent-platform-deploy`), or managing serving endpoints (use `agent-platform-endpoint-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。

Configures best-practice alerting policies for AI agents using OpenTelemetry (OTel) metrics, generating output as Terraform (.tf) configuration files. Use when analyzing, writing, or deploying alerting policies to monitor agent latency, error rates, token usage, and quality metrics. Don't use for standard infrastructure monitoring unrelated to AI agents, or when the agent is not instrumented with OpenTelemetry (for Reliability, Cost, Safety, Security alerts). NOTE: Reliability, Cost, Safety, and Security alerts use generic OTel metrics and work across runtimes (such as Cloud Run, Vertex AI). Quality alerts rely on Vertex AI Online Monitors and are strictly bound to Vertex AI deployments.

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。