「cost analysis」の検索結果

Use when working with Azure Functions Deep — deep Azure Functions analysis covering function app inventory, trigger types, execution metrics, scaling behavior, slot deployments, application insights integration, and consumption plan cost tracking. Provides optimization guidance for cold starts and memory usage.

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Use when working with Aws Efs — aWS EFS file system management and performance analysis. Covers file system inventory, mount targets, access points, throughput modes, lifecycle policies, replication configurations, and storage metrics. Use when inspecting EFS file systems, debugging mount issues, reviewing performance settings, or optimizing storage costs.

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 working with Aws Gamelift — aWS GameLift fleet management including game server deployments, fleet scaling, matchmaking configuration, game session monitoring, and player session tracking. Covers fleet health, capacity utilization, matchmaking metrics, and cost analysis.

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Use when working with Aws Lambda Deep — deep AWS Lambda analysis covering function inventory, cold start profiling, memory optimization, concurrency patterns, layer dependency auditing, dead letter queue health, event source mappings, and cost estimation. Goes beyond basic metrics to identify optimization opportunities.

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Use when working with Aws Secrets Manager — aWS Secrets Manager secret rotation status, access analysis, cost tracking, and lifecycle management. Covers secret inventory, rotation configuration audit, last access tracking, resource policy review, and version management.

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Use when working with Aws Cloudwatch Logs — aWS CloudWatch Logs group management, Logs Insights query execution, metric filter analysis, retention policy review, and subscription filter management. Covers log group inventory, storage cost optimization, query patterns, and cross-account log aggregation.

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Use when working with Dynamodb — amazon DynamoDB table analysis, capacity mode evaluation, GSI/LSI usage, item access patterns, and cost optimization.

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Build comprehensive 3-5 year financial models with revenue projections, cost structures, cash flow analysis, and scenario planning for early-stage startups.

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Use when a carrier owner, CFO, or controller asks how to build an annual budget, monthly P&L model, cash flow forecast, scenario plan, or break-even analysis for a trucking operation. Covers revenue modeling, cost structures, capital planning, and the discipline of tracking actuals vs budget.

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The science of learning through controlled experimentation. A/B testing isn't about picking winners—it's about building a culture of validated learning and reducing the cost of being wrong. This skill covers experiment design, statistical rigor, feature flagging, analysis, and building experimentation into product development. The best experimenters know that every test, positive or negative, teaches something valuable. Use when "a/b test, experiment, hypothesis, statistical significance, sample size, feature flag, variant, control, treatment, p-value, conversion rate, test winner, split test, experimentation, testing, statistics, feature-flags, hypothesis, growth, optimization, learning, validation" mentioned.

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Codified expertise for electricity and gas procurement, tariff optimization, demand charge management, renewable PPA evaluation, and multi-facility energy cost management. Informed by energy procurement managers with 15+ years experience at large commercial and industrial consumers. Includes market structure analysis, hedging strategies, load profiling, and sustainability reporting frameworks. Use when procuring energy, optimizing tariffs, managing demand charges, evaluating PPAs, or developing energy strategies.

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Design Azure infrastructure using natural language, or analyze existing Azure resources to auto-generate architecture diagrams, refine them through conversation, and deploy with Bicep. When to use this skill: - "Create X on Azure", "Set up a RAG architecture" (new design) - "Analyze my current Azure infrastructure", "Draw a diagram for rg-xxx" (existing analysis) - "Foundry is slow", "I want to reduce costs", "Strengthen security" (natural language modification) - Azure resource deployment, Bicep template generation, IaC code generation - Microsoft Foundry, AI Search, OpenAI, Fabric, ADLS Gen2, Databricks, and all Azure services

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Estimates infrastructure needs based on traffic forecasts, workload analysis, and performance requirements with sizing recommendations and cost trade-offs. Use for "capacity planning", "infrastructure sizing", "resource estimation", or "scalability planning".

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。