Name: Cross-Pollination Engine
Author: artyomx33

据 AlphaMoat 统计,截至 2026-09,Cross-Pollination Engine 累计下载 3,579 次、获 5 个星标,在全部 5,078 个 AI 技能中排名第 3,050 位,在「知识管理」分类(442 个)中排名第 278 位。

累计安装

205

累计下载

3,579

星标

全站排名

#3,050

全站前 61% · 「知识管理」前 63%

平台	累计安装	累计下载	星标	评论
ClawHub ↗	107	3,294	5	-
SkillHub ↗	205	3,579	5	—

各平台的计数口径不同,统计时间不一致。

这个技能是做什么的

主文举的例子：阻力轮 → 直排轮滑、游戏 XP 系统 → Duolingo、酒店礼宾 → 软件新手引导。流程四步：定义核心任务（剥离行业语境）→ 找到还有谁在解决它（往往是意外行业）→ 提取原理而非表面功能 → 翻译到你的语境（改造而非照抄）。行业灵感库按需求映射：信任看银行/医疗/航空（验证、凭证、清单）；参与度看游戏/健身应用/流媒体（XP、连击、个性化、进度）；新手引导看酒店/主题公园/奢侈零售（礼宾、期待感、个人触感）；简洁看 Apple/IKEA/Google（砍功能、隐藏复杂度）；紧迫感看电商/航空/快餐（稀缺、锚定、速度承诺）；社区看 CrossFit/Harley-Davidson/Peloton（部落认同、共享体验）。输出按固定模板：问题、核心任务、来自各行业的解法/关键原理/如何应用、综合方案与下一步动作；与 jtbd-analyzer、first-principles-decomposer、six-thinking-hats、app-planning-skill 组合使用效果更佳。

资料发布于 2026-10-27

可以用来做什么

用其他行业的成熟方案解自己的问题
为产品设计找跨行业灵感
把一个问题剥离到核心任务再找参照

什么时候用

用户说「跨界借鉴」「X 会怎么解决这个问题」「向某行业偷师」或需要跳出本行业思路时

示例提示词

以下是根据技能文档生成的示例,不代表实际运行结果。

迪士尼会怎么设计我们的新手引导？

如果这是一个游戏，它会怎么运作？

奢侈品酒店是怎么让人感觉被优待的？

月度数据趋势

累计下载3,579

星标5

2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。

月份	累计下载	环比	星标
2026-09	3,579	—	5
2026-08	3,265	+3.5%	5
2026-07	3,154	+8.1%	5
2026-06	2,918	+6.7%	5
2026-05	2,736	—	5
2026-03	2,356	—	5

SKILL.md 原文

以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 2026-10-23。

2.3 KB · ClawHub v1.0.0

---
name: cross-pollination-engine
description: Systematically borrow ideas from unrelated industries to solve problems. Innovation often comes from adjacent fields. Use when user says "cross-pollination", "how would X solve this", "borrow ideas from", "what can we learn from", "think outside the box", "how would Disney/Apple/Amazon do this", "different industry", "steal ideas".
---

# Cross-Pollination Engine

## The Core Insight

Most "innovation" is applying proven solutions from one domain to another.

展开全部(共 70 行)

- Resistance wheels → Rollerblades
- Gaming XP systems → Duolingo
- Hotel concierge → Software onboarding

## The Process

1. **Define the core job** (strip away industry context)
2. **Find who else solves it** (often surprising industries)
3. **Extract principles** (not surface features)
4. **Translate to your context** (adapt, don't copy)

## Industry Inspiration Library

| Need | Look At | Why |
|------|---------|-----|
| **Trust** | Banking, Healthcare, Aviation | Verification, credentials, checklists |
| **Engagement** | Gaming, Fitness apps, Streaming | XP, streaks, personalization, progress |
| **Onboarding** | Hotels, Theme parks, Luxury retail | Concierge, anticipation, personal touch |
| **Simplicity** | Apple, IKEA, Google | Feature cutting, hidden complexity |
| **Urgency** | E-commerce, Airlines, Fast food | Scarcity, anchoring, speed promises |
| **Community** | CrossFit, Harley-Davidson, Peloton | Tribal identity, shared experience |

## Output Format

```
PROBLEM: [What you're solving]
CORE JOB: [Stripped to fundamentals]

FROM [Industry 1]:
How they solve it: [x]
Key principle: [y]
Applied to us: [z]

FROM [Industry 2]:
How they solve it: [x]
Key principle: [y]
Applied to us: [z]

SYNTHESIS: [Combined approach]
NEXT STEP: [Concrete action]
```

## Prompt Starters

- "How would Disney solve our onboarding?"
- "What would Amazon do with our data?"
- "If this were a game, how would it work?"
- "How do luxury hotels make people feel special?"

## Integration

Compounds with:
- **jtbd-analyzer** → Understand job first, then find who else solves it
- **first-principles-decomposer** → Strip context to find fundamental need
- **six-thinking-hats** → Green Hat pairs naturally with cross-pollination
- **app-planning-skill** → Apply borrowed patterns to new apps

---
See references/examples.md for Artem-specific cross-pollinations