「segment」の検索結果

Brand Tailored Promotions — audience targeting, discount tiers, customer segmentation, repeat purchase incentives

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Designs products around price using the 9 rules from Ramanujam and Tacke - WTP conversations, needs-based segmentation, Good/Better/Best configuration, monetization models, behavioral pricing, and price integrity. Use when designing new products, validating pricing for SaaS/B2B/B2C launches, choosing between subscription/usage/freemium models, fixing post-launch sales below plan, diagnosing failed launches as Feature Shock/Minivation/Hidden Gem/Undead, or when product teams say 'let's price it later'. Not for pure commodities or cost-plus regulated environments.

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Build multi-sequence email automation flows with branching logic. Triggers on: "build email automation", "create email funnel", "email automation flow", "welcome series with branches", "conditional email sequence", "set up automation", "email workflow builder", "segmented email flow", "advanced email sequence", "nurture funnel", "cart abandonment sequence", "win-back email flow".

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 persona creation and JTBD analysis methodology - persona templates, ODI job step tables, pain point mapping, success metric quantification, and multi-persona segmentation

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Use when the user needs an overall marketing plan for a period — objectives, segments, positioning, channel mix, content direction, budget, timeline, KPIs, and a risk matrix, adapted to US, EU, SEA, or LATAM. Trigger on 'marketing plan', 'marketing strategy for next year', 'go-to-market roadmap', 'plan for Q3', 'we have no marketing plan', 'where should we spend next quarter'. Also use when a founder only says the business needs to grow and has no idea which channel to start with. Not for — one specific campaign, see `02-campaign-brief-global`; working backward from a revenue goal to spend, see `10-reverse-kpi-global`; splitting a budget across channels and months, see `61-budget-planning-global`; taking a brand new product to market, see `59-go-to-market-global`.

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 needs email or lifecycle automation — welcome series, nurture, abandoned cart, re-engagement, broadcast, and transactional flows with subject lines, segmentation, cadence, and deliverability, plus legal variants: CAN-SPAM for US, GDPR opt-in for EU, PDPA for SEA, LGPD for LATAM. Covers Klaviyo, Mailchimp, and ActiveCampaign. Trigger on 'email marketing', 'welcome sequence', 'drip campaign', 'newsletter', 'abandoned cart emails', 'nobody opens our emails'. Not for — coding HTML that renders correctly in Outlook, see `49-html-email-template-global`; cold outbound to B2B prospects, see `33-b2b-lead-gen-global`; refer-a-friend loops, see `18-referral-program-global`.

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Weekdays, heavy browser lane. Reads the member's own saved searches and the named sources for one buyer segment, captures candidate accounts together with the contactable people attached to them, scores every row against the named tests in the qualification file, and writes each one with the evidence that qualified it. Read only on every people surface, LinkedIn included. It holds every outbound action unless you released the channel, and it never touches a credential.

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Monthly, on the last weekday, light browser lane. Re-tests every named qualification test and every buyer segment against a month of this kit's own ledger evidence, then rewrites both files itself where the evidence disagrees with the assumption they were written on. It records every change in strategy/CHANGELOG.md with the ledger path that justified it. It sends only where you released the channel, never re-queues anyone, never edits the contacted ledger, and never writes a number it did not count out of a named file.

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。

Configure file-system routing with nested layouts, route groups, parallel routes, and error boundaries in Next.js App Router. Use when creating page routes, adding loading/error states, or organizing routes with groups and dynamic segments.

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把带时间戳的 narration.json 合成为中文解说音频。使用 MiMo TTS（mimo-v2.5-tts）或 Fish Audio（s2.1-pro-free）或显式配置的通用 IndexTTS HTTP 服务逐段生成语音， 按时间窗动态适配语速并处理响度；输入输出时间线上的旁白，产出 tts_segments 与 tts_meta.json。 外发说明：每段旁白文字会发给所选 TTS 服务（MiMo / Fish Audio / 用户自托管的 IndexTTS），--voice-ref 的参考音频会发给 MiMo。 另含实验性的英译中 dub 路径：只在显式选择 dub 模式并传 --confirm-voice-rights 时运行，会把源视频音频发到 MiMo ASR， 并以原说话人的声音为参考经 MiMo voiceclone 克隆配音；只可用于用户有权使用、且说话人同意被克隆声音的内容。 触发词：配音、语音合成、TTS、解说配音、 voiceover、text to speech、旁白配音。

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Train object-detection, image-classification, or SAM segmentation models on Hugging Face Jobs. Use for vision fine-tuning and evaluation; use huggingface-llm-trainer for language models.

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Design cloud network architectures with VPC patterns, subnet strategies, zero trust principles, and hybrid connectivity. Use when planning VPC topology, implementing multi-cloud networking, or establishing secure network segmentation for cloud workloads.

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Configure host-based firewalls (iptables, nftables, UFW) and cloud security groups (AWS, GCP, Azure) with practical rules for common scenarios like web servers, databases, and bastion hosts. Use when exposing services, hardening servers, or implementing network segmentation with defense-in-depth strategies.

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Operational Technology and industrial control system security — Purdue model segmentation, industrial protocol analysis (Modbus, DNP3, S7, EtherNet/IP), PLC/HMI exposure, IEC 62443 alignment, and MITRE ATT&CK for ICS, for authorized and safety-conscious assessments

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Conversion funnel analysis with drop-off investigation. Use when analyzing multi-step processes, identifying conversion bottlenecks, comparing segments through a funnel, or optimizing user journeys.

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视频前三秒钩子分析技能。从分镜拆解结果中提取前三秒分镜数据，为钩子吸引力评估提供结构化上下文。运行脚本 `python scripts/analyze_hook_segments.py "<breakdown_json_file>"` 或通过 stdin 传入 JSON。输出包含前三秒分镜数量、总时长和每个分镜的详细信息（含关键帧 URL）。

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。

智能抠图SKILL：从图片文件自动抠图。触发条件：当用户提及智能抠图、图片抠图、抠图、主体分割、去背景、背景剔除、商品抠图等关键词，或识别到用户表达等价意图时，调用此SKILL执行抠图任务。

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 cinematic AI shortfilm prompts (works with Seedance 2.0, Xiaoyunque, Sora, Kling, Jimeng, Veo) using the 5-stage structure from Mx-Shell's Zombie Scavenger. Trigger when the user wants transformation sequences, multi-shot narrative shorts, weapon-charge/combat segments, emotional family/pet/farewell narratives (催泪/亲情/萌宠/离别), or any cinematic video prompt.

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Audit applications and infrastructure handling payment card data against PCI DSS v4.0. Heavy emphasis on scope determination (the single most-leveraged variable) plus the engineering-relevant requirements — Req 3 (storage of CHD), Req 4 (transmission), Req 6 (secure SDLC), Req 7-8 (access), Req 10 (logging), Req 11 (testing), Req 12 (program). Use when the user mentions 'PCI,' 'PCI DSS,' 'PCI DSS 4.0,' 'payment card,' 'cardholder data,' 'CHD,' 'PAN,' 'PCI scope,' 'PCI compliance,' 'SAQ,' 'AoC,' 'attestation of compliance,' 'tokenization,' 'P2PE,' 'network segmentation for PCI,' or audits any system that stores, processes, or transmits payment card data.

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The consumer panel. Spins up a swarm of buyer sub-agents (default 100, --quick for 20) trained on the user's target customer, each with a different income, age, buying behaviour and objection, shows every one the same pitch, and runs the business through 100 buyer scenarios to see who buys, who doesn't, and why. Tallies the answers into buy rates by segment, the reasons people pass in their own words, what would flip a no, and price answers for founder-pricing. Use when the user says "would people buy this", "test it on customers", "run the consumer panel", "simulate buyers", "who is my customer", or "validate demand".

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。

Digital pathology image processing toolkit for whole slide images (WSI). Use this skill when working with histopathology slides, processing H&E or IHC stained tissue images, extracting tiles from gigapixel pathology images, detecting tissue regions, segmenting tissue masks, or preparing datasets for computational pathology deep learning pipelines. Applies to WSI formats (SVS, TIFF, NDPI), tile-based analysis, and histological image preprocessing workflows.

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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

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Detect somatic CNVs from WES/WGS/targeted BAMs (CNVkit v0.9.x). Bin coverage in target/antitarget regions, normalize vs reference, segment with CBS/HMM, call amps/dels, scatter/diagram plots, purity/ploidy, VCF/SEG export. CLI plus Python API (cnvlib). Use GATK CNV for deep WGS with population controls; use CNVkit for targeted/exome where antitarget bins matter.

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scikit-learn compatible Python toolkit for time series ML: classify, cluster, regress, segment, transform with 30+ algorithms (ROCKET, InceptionTime, KNN-DTW, HIVE-COTE, WEASEL). Handles panel, multivariate, and unequal-length series. Maintained successor to sktime. Alternatives: sktime (larger ecosystem), tslearn (fewer algorithms), catch22 (features only).

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。