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surge-landing

@tonone-ai · 收录于 昨天 · 上游提交 2 天前

Use when asked to design growth-optimized landing pages, activation funnel layouts, or experiment-friendly page structures. Examples: "growth-optimized landing", "activation funnel layout", "A/B testable page"

适合你,如果经常需要搭建高转化的营销着陆页

/ 通过 npx 安装 校验哈希
npx oh-my-skill add tonone-ai/tonone/surge-landing
/ 通过 bash 安装
curl -fsSL https://oh-my-skill.com/install.sh | bash -s -- tonone-ai/tonone/surge-landing
/ 已经装过?验证本机副本,不用重装
npx oh-my-skill verify tonone-ai/tonone/surge-landing
安装目标可用 --agent / --scope 或 --to 明确指定;省略时只会在唯一已存在的 agent 目录上自动选择,零命中或多命中会停止并提示。content_hash 缺失或不一致均拒装。
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怎么用

商店整理自技能原文 · 版本 d6b6925 · 表述以原文为准
它做什么

装好后,Claude会按照40行CLI格式生成增长优化着陆页布局表,包含各部分用途、是否可实验、激活触发器、漏斗结构、摩擦点和实验面。

什么时候触发

当用户要求设计增长导向的着陆页、激活漏斗布局或A/B测试页面时触发。

装好后可以这样说
Claude会搜索增长模式并输出布局表。
Claude会识别实验面并输出表格。
Claude会搜索产品推理和摩擦点。
技能原文 SKILL.md作者撰写 · MIT · d6b6925

surge-landing — Growth-Optimized Landing Page

Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.

When to use

User needs a landing page designed for growth: activation funnels, A/B testing, acquisition, or PLG flows.

Workflow
  1. Identify product type and growth goal from user request (acquisition, activation, PLG, trial, freemium, etc.)
  2. Search landing page patterns: ```bash python3 -m surge_agent.uiux search --domain landing --query "{product_type}" --limit 3 ```
  3. Search product reasoning: ```bash python3 -m surge_agent.uiux search --domain product --query "{product_type}" --limit 3 ```
  4. Search UX for friction points: ```bash python3 -m surge_agent.uiux search --domain ux --query "forms validation loading" --limit 3 ```
  5. Output experiment-friendly structure with activation triggers and friction audit
Output format
┌─ Growth Landing Page — {product_type} ──────────────────────────────┐
│ #  │ Section            │ Purpose                    │ Experiment?   │
├────┼────────────────────┼────────────────────────────┼───────────────┤
│  1 │ {section_name}     │ {purpose}                  │ A/B headline  │
│  2 │ {section_name}     │ {purpose}                  │ —             │
│  3 │ {section_name}     │ {purpose}                  │ A/B CTA copy  │
│  … │ …                  │ …                          │ …             │
└────┴────────────────────┴────────────────────────────┴───────────────┘

Activation triggers:   {activation_triggers}
Funnel structure:      {funnel_structure}
Friction points:       {friction_points}
Experiment surfaces:   {experiment_surfaces}
Anti-patterns
  • Never optimize for vanity metrics (page views, time on page) over activation metrics
  • Never add friction (sign-up gates, long forms) before demonstrating product value
  • Never design sections that can't be independently A/B tested
  • Never ship a growth page without identifying at least one experiment surface
Delivery

If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.

按 MIT 许可原样转载,未经改动 · 在 GitHub 查看 →

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