Results for “recommend”

Building AI-powered personalization systems: recommendation engines, collaborative filtering, content-based filtering, user preference learning, cold-start solutions, and LLM-enhanced personalized experiences. Use when "recommendation system, personalization, collaborative filtering, content-based filtering, user preferences, recommend, suggestions, for you, similar items, you might like, " mentioned.

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Initiate natural language Q&A through Amazon's storefront Alexa shopping assistant to get shopping guidance answers, recommended product groups, ASIN lists, and follow-up questions. Each call supports only 1 prompt; for follow-ups, the agent must summarize context and concatenate a new question for a new request. A url can be used to supplement Amazon page context. Trigger when the user mentions Amazon Alexa, Alexa shopping assistant, Amazon smart assistant, AI shopping guide, conversational product selection, natural language shopping, Amazon chat Q&A, Amazon Alexa shopping, conversational shopping, AI shopping assistant, follow-up questions, product recommendation conversation, context follow-ups. Even if the user does not explicitly mention "Alexa", if their need is to "ask for product recommendations on Amazon using natural language", this skill should also be triggered.

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Use when the user asks for journal recommendation, 选刊, 投稿期刊筛选, 期刊匹配, 适合投稿的期刊, 投哪个期刊, or wants 3-5 suitable journals for a manuscript based on an abstract, discipline, CAS/JCR ranking, impact factor, Aims & Scope, review speed, OA/APC, and publication-risk checks.

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"Implement collaborative filtering for recommendations based on user behavior patterns. Use this skill when the user needs to build a recommendation engine from user-item interaction data, find similar users or items, or predict ratings — even if they say 'users who bought this also bought', 'similar users', or 'recommend based on behavior'.".

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Integrate AI-powered Amazon affiliate product recommendations into websites using affiliatematic.com. Use when you need to: (1) Add Amazon affiliate widgets to a website, (2) Set up automated product recommendations based on page content, (3) Optimize affiliate revenue with AI-powered product matching, (4) Configure affiliate marketing automation. Triggers: amazon affiliate, affiliate marketing, product recommendations, affiliate widget, amazon associates integration, monetize website with amazon.

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Recommend a conservative shadow-only review reference architecture from valid offline evidence. Use only when the user explicitly asks for a reference-architecture recommendation or uses the bundled synthetic fixture.

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Use for deep Vue 3 best-practices review, recommendations, and the full ordered authoring workflow — built-in components (Teleport, Suspense, KeepAlive, Transition, TransitionGroup), animation techniques, slots, fallthrough attrs, custom directives, async components, render functions, plugins, and the post-functionality performance pass. Complements mk:vue (the everyday Composition API quick-reference). Invoke for "review my Vue", "Vue best practices", "recommend improvements", or non-trivial feature work needing the ordered workflow.

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Analyze a codebase and recommend Claude Code automations (hooks, subagents, skills, plugins, MCP servers). Use when user asks for automation recommendations, wants to optimize their Claude Code setup, mentions improving Claude Code workflows, asks how to first set up Claude Code for a project, or wants to know what Claude Code features they should use.

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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.

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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.

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Browse available specialist agents by domain, select up to five, and run them independently on the same task in parallel. Receive a combined report showing each agent’s findings, shared conclusions, conflicting recommendations, and suggested next steps.

• Browsing available specialist agents
• Selecting agents from different domains
• Reviewing a site for security and SEO before launch

Audit repository settings, connected tools, and the agent's runtime setup to show what is usable now. Identify missing workflows and integrations, then recommend prioritized ECC skills, automation hooks, agents, or tool connections.

• Reviewing the current setup before configuring Claude Code
• Listing connected tools and available capabilities
• Checking whether more plugins are needed

Research current facts, compare options, or investigate people and companies using public sources and supplied context. This skill selects an appropriate research workflow and separates evidence, inference, and recommendations, including dates where freshness matters.

• Check current facts against existing documents
• Compare options before making a decision
• Research potential contacts and companies

Design recommendation and ranking workflows that select the best items for a user or situation. Organize candidate retrieval, enrichment, filtering, scoring, selection, and background recording into six stages, then generate a runnable code scaffold.

• Designing personalized content feeds
• Reranking search or RAG results
• Building task prioritization workflows

Turn scored competitor profiles into a report for strategic decisions. Organize market maps, comparison tables, unoccupied positions, threats, and prioritized recommendations so leaders can decide whom to compete with and how to differentiate.

• Preparing a competitive report for leadership or a board
• Combining completed competitor profiles
• Assessing room for differentiation and competitive threats

Turn a decision into a concise brief with one problem, three distinct options, and one recommendation. Compare benefits and drawbacks, then define measurable success criteria and practical steps for carrying out the recommended approach.

• Comparing approaches to a technical decision
• Preparing a proposal to share with a team
• Weighing tools or migration strategies

Add two or three specific follow-up questions after actionable advice, plans, estimates, or analysis. The questions help users examine facts, reasoning, and missing context before acting, with clear exclusions and a limit of once per conversation.

• Examining assumptions behind cost or timeline estimates
• Checking reasoning behind a recommended plan
• Spotting missing context in proposals or email drafts

Create investment-banking-style presentations for board discussions and M&A decisions. Organize comparable companies, past transactions, valuation ranges, and strategic options into slides that distinguish sourced figures from assumptions and explain the recommended course of action.

• Preparing board materials on strategic alternatives
• Comparing peer companies and past M&A transactions
• Presenting valuation ranges and sensitivity analysis

Build web animations with GSAP ScrollTrigger that respond to scrolling. The skill covers pinned sections, animation progress linked to scroll position, horizontal movement, and the position updates and cleanup needed when page content changes.

• Animate elements as they enter the viewport
• Keep a section pinned while its content animates
• Move content horizontally through vertical scrolling

Analyze networks of people, web pages, or systems using PageRank, a method that scores importance from connections. Identify influential nodes and communities, generate personalized recommendations, and examine communication bottlenecks to guide network improvements.

• Finding influential users and communities on social networks
• Analyzing links and page importance on a website
• Generating recommendations based on user preferences

Choose an agent and model tier using the task description and learned patterns. The skill can explain its recommendation, use compiler-based edits for supported simple changes, and record task outcomes to inform future routing decisions.

• Choose an agent and model before starting a task
• Review the reasoning behind a routing recommendation
• Apply supported code changes through the TypeScript compiler

Find ruflo plugins suited to your current task, workflow, and installed MCP tools, which connect agents to external capabilities. Browse recommendations, search by need, and review what each candidate adds before choosing plugins.

• Choosing plugins for a new project
• Finding tools for security audits or UI tests
• Exploring capabilities beyond installed plugins

Analyze the last seven days of agent and model token usage to identify unnecessary spending. Recommend changes to model selection, prompt caching, and overlapping work, with estimated dollar savings, percentage reductions, and implementation priorities.

• Investigating higher-than-expected AI costs
• Matching model choices to task complexity
• Reviewing prompt caching efficiency

Analyze performance in Claude Flow swarms, groups of agents working together. Identify delays in communication, processing, memory, and networking, then generate reports and prioritized recommendations. Compare previous runs or apply available automatic optimizations.

• Investigating slow tasks across multiple agents
• Comparing performance before and after changes
• Creating weekly performance reports