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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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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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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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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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按专业领域列出可用代理，最多选择五个并行完成同一任务，再将各自结果、共同结论、建议分歧和后续行动汇总为一份报告。
读取仓库设置和智能体运行环境，盘点已连接工具与可用功能，区分缺少工作流程和缺少外部集成的情况，并按优先级提出三至五项ECC后续建议。
将最新公开信息与用户提供的资料相互核对，区分事实、推断和建议，并根据事实核查、选项比较或人物与企业调查选择方法，整理有依据的结果。
将按用户或情境选择优先候选项的推荐与排序流程，拆分为获取、补充信息、过滤、评分、选择和记录六个阶段，并生成可运行的代码框架。
将已评分的竞品资料整理为包含市场地图、比较表、差异化空间、威胁和优先行动的报告，供管理层汇报及团队制定竞争策略时使用。
将需要决策的问题概括为一句话，比较三个选项的优缺点并给出推荐方案，同时整理可验证的完成标准和具体执行步骤，形成便于向团队分享的提案。
在AI的建议、计划、估算或分析后附上两三个简短问题，帮助用户在行动前检查数字、逻辑与缺失信息。问题紧扣回答中的具体内容，可直接向代理追问。
为董事会讨论和并购决策制作投资银行风格的演示资料，整理可比公司、历史交易、企业估值和战略选项，并注明数据来源与假设，说明建议方案。
使用GSAP实现随滚动变化的网页动画，涵盖元素固定、动画进度联动和横向移动，也处理布局变化后的触发位置重算及页面切换时的清理。
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.
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根据任务内容、过去的成功模式和预测结果选择负责的智能体与AI模型，支持查看分配理由，并记录完成结果，为后续任务分配提供依据。
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.
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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.
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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.
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Review and rewrite Twitter drafts using insights from its open-source recommendation algorithm. The skill examines audience fit, replies, sharing, and negative reactions, then explains suggested edits and ways to refine your content strategy.
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Review early connections to third-party services such as fonts and APIs. Use loading measurements to identify essential domains, recommend preconnect hints that prepare connections in advance, and remove unnecessary hints that add overhead.
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Review slow page loads and choose resource hints, browser instructions that start loading or connecting earlier. Use measurements to identify bottlenecks, then recommend preload, preconnect, prefetch, or dns-prefetch for important assets and likely next-page resources.
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