「recommendation」の検索結果

Use when working with Cloudhealth — cloudHealth by VMware multi-cloud cost management and governance. Covers cost reporting, rightsizing recommendations, governance policies, budget tracking, reserved instance management, and compliance auditing. Use when analyzing cloud spend across providers, enforcing tagging policies, or tracking budgets.

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 Bloomreach — bloomreach commerce search and merchandising platform management including catalog feeds, search configuration, autosuggest, category pages, SEO, A/B testing, pixel tracking, and recommendation widgets. Covers search relevance, catalog health, widget performance, and content personalization metrics.

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 Azure Cost Management — azure Cost Management cost analysis, budget tracking, Advisor cost recommendations, export management, and spending trend analysis via Azure CLI.

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 Snyk — snyk security scanning and vulnerability analysis. Covers dependency vulnerability scanning, container image scanning, IaC security scanning, code analysis, license compliance, and fix recommendations. Use when scanning for vulnerabilities, analyzing dependency risks, reviewing container security, or auditing IaC 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。

Use when working with Aws Cost Optimization — aWS native cost optimization using Compute Optimizer, Trusted Advisor cost checks, Savings Plan analysis, Cost Explorer, and resource rightsizing. Covers EC2 rightsizing, idle resource detection, Savings Plan and Reserved Instance recommendations, and cost anomaly detection. Use when optimizing AWS infrastructure costs with native AWS tools.

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 Mongodb — mongoDB database analysis, performance tuning, query optimization, and health monitoring. Covers collection analysis, index recommendations, aggregation pipelines, replica set health, Atlas cluster management, and slow query investigation.

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。

Structured decision-making framework for technical proposals and trade-off analysis. When the user faces a choice between multiple approaches (architecture decisions, tool selection, refactoring strategies, migration paths), this skill produces a 1-3-1 format: one clear problem statement, three distinct options with pros/cons, and one concrete recommendation with definition of done and implementation plan. Use when the user asks for a "1-3-1", says "give me options", or needs help choosing between competing approaches.

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 for Vercel cost and performance optimization on deployed projects, especially Next.js, SvelteKit, Nuxt, and limited Astro apps. Collect Vercel metrics, usage, project config, and code scan results first; investigate only metric-backed candidates; produce ranked recommendations grounded in verified files and version-aware Vercel/framework docs. Trigger for Vercel bill reduction, slow or expensive routes, caching opportunities, Function Invocations, Build Minutes, Fast Data Transfer, Core Web Vitals, Bot Management, Fluid compute, or cost breakdown 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。

Stop and check this skill before finishing any reply to a question about how to use Claude or a Claude product — it recommends matching courses, tutorials, and use cases from Claude Academy (academy.claude.com), Anthropic's learning hub. Trigger on: "how do I", "how can I", "getting started with", "what can Claude do", "teach me", "learn to use"; questions about artifacts, projects, skills, plugins, connectors, MCP; requests about rolling Claude out to a team, class, or organization; and any ask for training materials, onboarding content, or learning resources. Use it when the user is learning how to use a feature or product — not when they are mid-task and just want the task done. This skill composes with other skills: after consulting product documentation to answer how a Claude feature works, also check here for a matching course or tutorial — a docs-grounded answer and an Academy recommendation belong together. Only recommend on a strong match; never invent Academy content.

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。

Build and publish a Gradio demo on Hugging Face Spaces for a user-provided LoRA. Use when someone asks to create, generate, ship, or publish a Space, demo, Gradio app, or playground for a LoRA — including LoRAs for Qwen-Image, Qwen-Image-Edit, LTX-Video, Wan, FLUX, SDXL, or other diffusion base models. Also triggers when someone describes a LoRA they trained or hosts on the Hub and wants to share it. Covers picking the right base pipeline and `diffusers` inference recipe, designing a UI tailored to the LoRA's task and inputs (Union/multi-task control, edit, video, image, etc.), respecting model-card recommendations (trigger words, steps, guidance, LoRA scale, example inputs), and shipping to ZeroGPU hardware as a private Space by default.

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 the user asks about finding the best, top, or recommended model for a task, wants to know what AI model to use, or wants to compare models by benchmark scores. Triggers on: "best model for X", "what model should I use for", "top models for [task]", "which model runs on my laptop/machine/device", "recommend a model for", "what LLM should I use for", "compare models for", "what's state of the art for", or any question about choosing an AI model for a specific use case. Always use this skill when the user wants model recommendations or comparisons, even if they don't explicitly mention HuggingFace or benchmarks.

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 en audit interne (risk assessment, controls, compliance, recommendations, DZ context)

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 complete solutions for specific Dataverse SDK use cases with architecture 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。

Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar

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 managing Apple Ads with asc, including OAuth profiles, ad-account discovery, Platform API v1 campaigns and targeting, reports, assets, recommendations, guarded mutations, raw requests, and Campaign Management API v5 migration.

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 optimized Apple App Store metadata recommendations with ASO best practices. Use this skill when analyzing app listings, optimizing metadata (title, subtitle, description, keywords), performing competitive analysis, or validating App Store listing requirements. Triggers on queries about App Store optimization, metadata review, or screenshot strategy.

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 a project's past Codex sessions, memory files, and existing local skills to recommend the highest-value skills to create or update. Use when a user asks what skills a project needs, wants skill ideas grounded in real project history, wants an audit of current project-local skills, or wants recommendations for updating stale or incomplete skills instead of creating duplicates.

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 websites for SEO, performance, security, technical, content, and 15 other issue cateories with 230+ rules using the squirrelscan CLI. Returns LLM-optimized reports with health scores, broken links, meta tag analysis, and actionable recommendations. Use to discover and asses website or webapp issues and health.

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。

Master modern business analysis with AI-powered analytics, real-time dashboards, and data-driven insights. Build comprehensive KPI frameworks, predictive models, and strategic 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。

Comprehensive AWS cost analysis and optimization recommendations using AWS CLI and Cost Explorer

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。

bu skill covers hardening container images by minimizing attack surface, removing unnecessary packages, implementing multi-stage builds, configuring non-root users, and applying CIS Docker Benchmark recommendations to produce secure production-ready images.

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。

Implementing Microsoft Defender for Cloud to enable cloud security posture management, workload protection across VMs, containers, databases, and storage, configure security recommendations, and Kur: adaptive security controls with automated remediation.

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。

bu skill instructs security practitioners on Dağıt:ing Microsoft Defender for Cloud as a cloud-native application protection platform for Azure, multi-cloud, and hybrid environments. It covers enabling Defender plans for servers, containers, storage, and databases, configuring security recommendations, managing Secure Score, and integrating with the unified Defender portal for centralized threat 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。

Facilitate structured post-incident reviews to identify root causes, document what worked and failed, and produce actionable recommendations to improve future incident response.

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。