Name: Cross-Pollination Engine
Author: artyomx33

據 AlphaMoat 統計,截至 2026-09,Cross-Pollination Engine 累計下載 3,579 次、獲 5 個星標,在全部 5,078 個 AI 技能中排名第 3,050 位,在「知識管理」分類(442 個)中排名第 278 位。

累計安裝

205

累計下載

3579

星標

全站排名

#3,050

全站前 61% · 「知識管理」前 63%

平台	累計安裝	累計下載	星標	評論
ClawHub ↗	107	3294	5	-
SkillHub ↗	205	3579	5	—

各平台的計數口徑不同,統計時間不一致。

這個技能是做什麼的

主文舉的例子：阻力輪 → 直排輪滑、遊戲 XP 系統 → Duolingo、酒店禮賓 → 軟體新手引導。流程四步：定義核心任務（剝離行業語境）→ 找到還有誰在解決它（往往是意外行業）→ 提取原理而非表面功能 → 翻譯到你的語境（改造而非照抄）。行業靈感庫按需求對映：信任看銀行/醫療/航空（驗證、憑證、清單）；參與度看遊戲/健身應用/流媒體（XP、連擊、個性化、進度）；新手引導看酒店/主題公園/奢侈零售（禮賓、期待感、個人觸感）；簡潔看 Apple/IKEA/Google（砍功能、隱藏複雜度）；緊迫感看電商/航空/快餐（稀缺、錨定、速度承諾）；社群看 CrossFit/Harley-Davidson/Peloton（部落認同、共享體驗）。輸出按固定模板：問題、核心任務、來自各行業的解法/關鍵原理/如何應用、綜合方案與下一步動作；與 jtbd-analyzer、first-principles-decomposer、six-thinking-hats、app-planning-skill 組合使用效果更佳。

資料釋出於 2026-10-27

可以用來做什麼

用其他行業的成熟方案解自己的問題
為產品設計找跨行業靈感
把一個問題剝離到核心任務再找參照

什麼時候用

使用者說「跨界借鑑」「X 會怎麼解決這個問題」「向某行業偷師」或需要跳出本行業思路時

示例提示詞

以下是根據技能文件生成的示例,不代表實際執行結果。

迪士尼會怎麼設計我們的新手引導？

如果這是一個遊戲，它會怎麼運作？

奢侈品酒店是怎麼讓人感覺被優待的？

月度資料趨勢

累計下載3579

星標5

2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。

月份	累計下載	環比	星標
2026-09	3579	—	5
2026-08	3265	+3.5%	5
2026-07	3154	+8.1%	5
2026-06	2918	+6.7%	5
2026-05	2736	—	5
2026-03	2356	—	5

SKILL.md 原文

以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 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.

展開全部(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