「personalization」の検索結果

Reusable approval-loop pattern for fanning out lead personalization across parallel Claude Code Task sub-agents. Shows the user 1 sample personalization, collects feedback, runs 10 more, approves, runs 10 more — stops when 2 consecutive rounds have zero edits, then scales to the full list. ALWAYS uses Claude Code Task tool sub-agents — never an external Anthropic/OpenAI API key. Use when any skill needs per-lead custom variables (situation lines, value lines, CTAs).

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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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Design AI personalization engines for content. TRIGGERS - Use when user needs help with ai-personalization-engine related tasks.

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Use when gathering or evaluating requirements for a Salesforce Commerce Cloud digital storefront — branding strategy, content management approach, personalization, mobile experience, and accessibility compliance. Trigger keywords: SFRA branding, PWA Kit storefront, composable storefront, storefront accessibility, WCAG Commerce Cloud, overlay cartridge branding, mobile storefront, storefront personalization, content slots. NOT for building the site in Business Manager — use admin/b2c-commerce-store-setup. NOT for SCAPI design, Managed Runtime and caching strategy — use architect/headless-commerce-architecture.

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 AI personalization engines for content. TRIGGERS - Use when user needs help with ai-personalization-engine related 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。

Builds a hyperpersonalization packet for each lead by chaining company-researcher, cro-auditor, and person-researcher. The packet feeds into message-generator for Tier 3 personalized outreach.

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Design personalization strategies for e-commerce stores. Product recommendations, dynamic content, customer segmentation, and personalized email/SMS based on browsing and purchase behavior.

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Build Tier 1/2/3 cold outreach sequences differentiated by personalization depth and research signal strength during PRD v0.9 Go-to-Market. Triggers on requests to plan cold outreach, founder-led sales, or when user asks "cold email sequence", "outbound campaign", "founder outreach", "LinkedIn outreach", "Tier 1 personalization", "predictable revenue", "sales cadence", "BDR sequence". Outputs GTM-OUT-* sequence entries and lead-list references.

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Configure platform-level global search in a Salesforce org — per-object Search Layouts (Search Results, Lookup Dialogs, Lookup Phone Dialogs, Tab columns), the Setup → Search Settings page (Sidebar Search, Lookup Auto-Completion, Drop-Down List size), and Synonym Groups (Setup → User Interface → Synonyms). NOT for Einstein Search personalization signals or Natural Language Search (use agentforce/einstein-search-personalization), NOT for Experience Cloud site Search Manager (use lwc/experience-cloud-search-customization), NOT for SOSL query authoring (use data/sosl-search-patterns), NOT for Commerce storefront search tuning.

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Audit adaptive learning and student personalization systems for pedagogical quality. Triggers: you need to evaluate learning path algorithms (branching, remediation, acceleration).

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Evaluate physical therapy personalization and adaptive treatment systems.

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Load this skill whenever the project has personalization features, user preference controls, theme selectors, font size adjusters, motion toggles, contrast settings, or any user-configurable accessibility accommodations. Under no circumstances override or ignore user OS-level accessibility preferences without explicit user consent. Absolutely always persist user preferences, apply them immediately, and respect prefers-reduced-motion, prefers-contrast, and similar media queries.

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Expert en prospection à froid avancé (email sequences, LinkedIn, personalization, CRM sync, DZ market)

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Framework-based cold email composer with 3 personalization tiers. Generates subject line, body, and follow-up variants. Never uses templates -- builds from frameworks (PAS, BAB, AIDA, Signal-Proof-Ask).

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Deep research on a prospect company using WebSearch for outreach personalization

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Multi-channel personalized message generation with 3 tiers of personalization

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Use to change the wallpaper on the user's dot's computer and/or the accent color used for the Dock and window titlebars

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Email has the highest ROI of any marketing channel. $36 for every $1 spent. Yet most startups treat it as an afterthought - bulk blasts, no personalization, landing in spam folders. This skill covers transactional email that works, marketing automation that converts, deliverability that reaches inboxes, and the infrastructure decisions that scale. Use when: keywords, file_patterns, code_patterns.

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When the user wants to build video-first cold outreach, create personalized video at scale, implement async selling, or use AI demo generation for prospecting. Also use when the user mentions 'video outreach,' 'personalized video,' 'video prospecting,' 'Tavus,' 'Sendspark,' 'HeyGen,' 'video email,' 'async selling,' 'video demo,' or 'made this for you.' This skill covers video-first outreach systems from personalization through conversion optimization. Do NOT use for technical implementation, code review, or software architecture.

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When the user wants to build an AI-powered outreach system, write cold emails, improve deliverability, or scale personalized outreach. Also use when the user mentions 'cold email,' 'cold outreach,' 'outreach automation,' 'Instantly,' 'Smartlead,' 'Clay,' 'email sequences,' 'deliverability,' 'personalization at scale,' 'reply rate,' or 'outreach stack.' This skill covers the complete AI cold outreach system from signal detection through conversion. Do NOT use for technical implementation, code review, or software architecture.

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Requests about what is remembered, however short, from an ask to remember one thing to asks to say what is on file, correct it, or forget it; a bare save or an answer from the profile alone does not cover these. Also applying what the store already knows, recalling an older fact (a past recipient, a size, a recurring order) when it would change the pick, and deciding what should outlast the session. Not needed when the request carries no personal context to apply or keep.

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Use when the user asks to "personalize the email", "add merge tags / dynamic content", "set up conditional blocks per segment", or "make first-name and product-recommendation fields fall back safely"; produces a merge-tag map with per-tag fallbacks, conditional-block rules with per-segment variations, a fallback-safety audit, and a PII guard on what may render, informing the SEND E (Engagement/personalization) dimension. Not for building the segments — use list-segment-builder; not for writing the base copy — use email-creative-builder; not for scoring EQS or running vetoes — use email-quality-auditor. 邮件个性化/合并标签/条件内容块/兜底默认值

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Activate when developers have latent caching needs: slow API responses, database read bottlenecks, DynamoDB throttling or cost, RDS/Aurora scaling pressure, Bedrock latency or cost, or adding a cache; activate when working with Redis, Valkey, Memcached, or any in-memory data store, cache-aside patterns, session stores, rate limiting, leaderboards, counters, streams, queues, pub/sub, distributed locks, feature flags, shopping carts, or other caching strategies. Activate for GenAI and ML retrieval: vector similarity search for low-latency retrieval, semantic caching, RAG, LLM response caching, embedding stores, AI agent memory, recommendation, personalization. Activate for ElastiCache lifecycle: provisioning (serverless or node-based), engine selection, CloudFormation/CDK/Terraform IaC, VPC connectivity, TLS, RBAC, IAM auth, Global Datastore, monitoring, troubleshooting, cost optimization, and migration from self-managed Redis. Do not trigger for browser caches, CDN/CloudFront, HTTP Cache-Control, CPU caches.

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Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.

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。