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Profiles macOS and iOS apps with Instruments and xctrace: choosing templates such as Time Profiler, launching binaries with arguments, recording traces, and exporting results. Use when a Mac or iOS app needs CPU, memory, or responsiveness profiling.

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Extracts what the user actually wants instead of what they think they should want. Achieves this through one-question-at-a-time interview until ~95% confidence about the underlying intent. Use when an ask is underspecified ("build me X" without "for whom" or "why now"), when the user explicitly invokes ("interview me", "grill me", "are we sure?", "stress-test my thinking"), or when you catch yourself silently filling in ambiguous requirements before any plan, spec, or code exists.

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。

Behavioral guidelines to reduce common LLM coding mistakes. Use when writing, reviewing, or refactoring code to avoid overcomplication, make surgical changes, surface assumptions, and define verifiable success criteria.

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 writing, reviewing, or refactoring production code where reliability, portability, maintainability, explicit contracts, resource safety, and cross-language coding standards matter across Python, TS/JS, C, C++, Rust, or Go.

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。

Profiles native macOS and iOS apps from the command line only: xctrace recording, Time Profiler samples, hotspot ranking, and symbolication with atos and vmmap. Use for headless or SSH-based performance analysis without opening the Instruments GUI.

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。

Instruments code so production behavior is visible and diagnosable. Use when adding logging, metrics, tracing, or alerting. Use when shipping any feature that runs in production and you need evidence it works. Use when production issues are reported but you can't tell what happened from the available data.

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 performance budgets or SLAs exist, users or monitoring report slowness, Core Web Vitals fall below thresholds, a change may have caused a regression, or a feature handles large datasets or high traffic. Measure-first workflow: profile, fix the proven bottleneck, re-measure. Not for speculative 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。

Use when a task feels too large or vague to start, a spec needs breaking into implementable units, work must be parallelized across agents or sessions, or implementation order is unclear. Produces ordered tasks with acceptance criteria. Skip when the spec already contains well-defined tasks.

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。

Process media files (video, audio, images, documents) using Transloadit. Use when asked to encode video to HLS/MP4, generate thumbnails, resize or watermark images, extract audio, concatenate clips, add subtitles, OCR documents, or run any media processing pipeline. Covers 86+ processing robots for file transformation at scale.

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 Azure resource groups and generate detailed Mermaid architecture diagrams showing the relationships between individual resources. Use when someone needs to understand or document how resources in an Azure resource group fit 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。

Analyze Azure resource health, diagnose issues from logs and telemetry, and create a remediation plan for identified problems. Use when an Azure resource is unhealthy, slow, or failing and the cause is unknown.

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。

Fetches real-time Azure retail pricing using the Azure Retail Prices API (prices.azure.com) and estimates Copilot Studio agent credit consumption. Use when asked what an Azure service costs, to compare regions or SKUs, to estimate a workload, or about reserved, savings plan, or spot pricing.

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 Azure DevOps resources via CLI including projects, repos, pipelines, builds, pull requests, work items, artifacts, and service endpoints. Use when automating or scripting Azure DevOps from the command line with az devops.

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。

Validates Azure deployments before they run: Bicep syntax, what-if previews, and permission checks. Use before azd up, azd provision, or az deployment, when preparing or reviewing Bicep files, or to preview what a deployment will change.

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。

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

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 Azure resources used in the app (IaC files and/or resources in a target rg) and optimize costs - creating GitHub issues for identified optimizations. Use when an Azure bill needs reducing or a resource group needs a cost review.

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。

Design AWS architectures for startups using serverless patterns and IaC templates. Use when asked to design serverless architecture, create CloudFormation templates, optimize AWS costs, set up CI/CD pipelines, or migrate to AWS. Covers Lambda, API Gateway, DynamoDB, ECS, Aurora, 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。

Setup and initialization guide for developing AWS CDK (Cloud Development Kit) applications in Python. This skill enables users to configure environment prerequisites, create new CDK projects, manage dependencies, and deploy to AWS. Use when starting or setting up an AWS CDK project in Python.

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。

Aspire skill covering the Aspire CLI, AppHost orchestration, service discovery, integrations, MCP server, VS Code extension, Dev Containers, GitHub Codespaces, templates, dashboard, and deployment. Use when building, running, or deploying a distributed application with Aspire, or setting up its AppHost, integrations, or dashboard.

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。

Design and implement Arduino integration with Azure IoT Hub and IoT Edge, including secure provisioning, resilient telemetry, command handling, and production guardrails. Use when connecting Arduino sensors to Azure, sending MQTT telemetry to IoT Hub, adding an edge gateway, or sending cloud-to-device commands.

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。

Instruments a web app to send telemetry to Azure Application Insights: SDK setup, configuration, and telemetry patterns. Use when the user wants to enable monitoring, request tracking, or logging to App Insights for a web application.

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。

Cascades strategy from boardroom to individual contributor. Detects and fixes misalignment between company goals and team execution. Covers strategy articulation, cascade mapping, orphan goal detection, silo identification, communication gap analysis, and realignment protocols. Use when teams are pulling in different directions, OKRs don't connect, departments optimize locally at company expense, or when user mentions alignment, strategy cascade, silo, conflicting OKRs, or strategy communication.

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。

Cross-functional what-if modeling for cascading multi-variable scenarios. Unlike single-assumption stress testing, this models compound adversity across all business functions simultaneously. Use when planning for compound downside scenarios (for example losing a key customer while a funding round slips) rather than testing one assumption.

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 raw SaaS numbers need turning into a health verdict — MRR, ARR, churn, CAC, LTV, payback, burn multiple, NRR. Calculates the metrics, benchmarks them against industry standards, and returns prioritized, plain-English advice as a senior SaaS CFO would.

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。