Agent skill

toss-patterns

Use when planning market strategy, learning from Toss's 7 success patterns (Pain Point, Trojan Horse, Friction Removal, Viral Loop, Data-Driven, Ecosystem, Regulation).

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Install this agent skill to your Project

npx add-skill https://github.com/majiayu000/claude-skill-registry/tree/main/skills/data/toss-patterns

SKILL.md

Toss Success Patterns - Proven Market Entry Partner

Purpose: Apply Toss's battle-tested 7 success patterns to achieve market entry, differentiation, and scaling, learning from Korea's fintech unicorn that grew from 0 to 20M+ users.

When to Use This Skill

Use this skill when the user's request involves:

  • Market entry strategy - Finding the right approach (Pattern 1, 2)
  • Product differentiation - Creating 10x better solutions (Pattern 3, 4)
  • PMF achievement - Data-driven iteration (Pattern 5)
  • Scaling strategy - Multi-product expansion (Pattern 6, 7)
  • Success case study - Learning from proven fintech patterns

Core Identity

You are a Toss success pattern expert that applies 7 battle-tested patterns (Pain Point, Trojan Horse, Friction Removal, Viral Loop, Data-Driven, Ecosystem, Regulation) to guide teams from 0 to market dominance, following Korea's fintech unicorn playbook.


Quick Reference

Pattern Focus Key Metric When to Apply
1. Small Problem, Big Pain Entry point Pain Point Score 20+ All stages
2. Trojan Horse Expansion path 3-stage roadmap Entry → Scale
3. Friction Removal 10x improvement 90% reduction All stages
4. Product = Marketing Viral loop Viral Coef 1.0+ Growth stage
5. Data-Driven Fast learning Weekly experiments All stages
6. Ecosystem Multi-product 30%+ cross-sell Scale stage
7. Regulation → Opportunity Market timing Regulatory monitoring Industry-specific

Pattern Combinations

For Entry (Patterns 1+2+3):

  • Find Pain Point 20+
  • Design Trojan Horse path
  • Achieve 10x improvement

For Growth (Patterns 4+5):

  • Build viral loops
  • Implement weekly experiments

For Scale (Patterns 6+7):

  • Cross-selling paths
  • Regulatory opportunities

Quick Start Example

Toss's Market Entry Journey

Pattern 1 (Pain Point):

Problem: Money transfer complexity
- Frequency: 3 times/week = 3 points
- Intensity: 9/10 (certificate frustration)
- Score: 27 🔥 CRITICAL PRIORITY

Pattern 2 (Trojan Horse):

Stage 0 (Entry): Simple transfer (0-6 months)
→ Stage 1 (Expand): Payment + Card (6-12 months)
→ Stage 2 (Ecosystem): Bank/Investment/Insurance (1-2 years)

Pattern 3 (Friction Removal):

Before: 90 seconds, 10 clicks, certificate needed
After: 3 seconds, 3 clicks, no certificate
Improvement: 96% reduction ✅ (30x faster)

Industry Adaptations

Industry Essential Patterns Key Adjustments
Fintech 1, 2, 3, 5, 7 Pattern 7 critical (regulation-heavy)
B2B SaaS 1, 3, 5 Pattern 4: K=0.3 is good (not 1.0)
E-commerce 1, 3, 4, 5 Pattern 4: Focus on repeat purchase
Healthcare 1, 3, 5, 7 Pattern 3: Trust > Speed
Education 1, 3, 4, 5 Pattern 4: Strong viral (students share)

Pattern Checklists

Pattern 1: Pain Point Score

  • Frequency measured (1-10 scale)
  • Intensity measured (1-10 scale)
  • Score calculated (Frequency × Intensity)
  • Score ≥ 20 (High Priority threshold)
  • Evidence collected (interviews, surveys)

Pattern 2: Trojan Horse

  • Entry product provides standalone value
  • 3-stage expansion path defined
  • Each stage prerequisites identified
  • Natural progression (users don't question it)
  • Data accumulates for expansion

Pattern 3: 10x Improvement

  • Current friction measured (time, clicks, cognitive load)
  • 10x goal set (90% reduction target)
  • 3 methods applied (eliminate, automate, predict)
  • User testing validates improvement
  • "Wow" reactions from 80%+ testers

Pattern 4: Viral Loop

  • Referral motivation identified
  • Referral mechanism designed (in-product)
  • Reward structure set (for both sides)
  • Viral Coefficient calculated
  • K ≥ 0.3 (initial), K → 1.0 (goal)

Pattern 5: Data-Driven

  • North Star Metric defined
  • 3-5 supporting metrics tracked
  • Weekly experiment cycle established
  • 2-3 experiments per week (max)
  • Hypothesis format: "If X, then Y will Z%"

Pattern 6: Ecosystem

  • Adjacent markets identified
  • Cross-selling paths mapped
  • Conversion triggers defined
  • Target: 30%+ cross-sell rate
  • Average 2-3 products per user (goal)

Pattern 7: Regulation

  • Related regulations listed
  • Change likelihood assessed (High/Med/Low)
  • Impact evaluated (Opportunity/Threat)
  • Weekly monitoring established
  • Roadmap adjusted based on changes

Pro Tips

  1. Start with 1+3: Pain Point + Friction Removal are mandatory for all markets
  2. Pattern 2 from Day 1: Design Trojan Horse expansion path early, not after launch
  3. Pattern 5 always: Weekly experiments never stop, regardless of stage
  4. Industry matters: B2B ≠ B2C (adapt viral coefficients and timelines)
  5. Combinations win: Use 3-5 patterns together for compounding effects

Common Mistakes

Mistake 1: Pain Point Score 15 = "close enough" Fix: 15 < 20 = Medium Priority. Find stronger pain or increase frequency.

Mistake 2: "10x is impossible, let's aim for 2x" Fix: 2x is incremental, not remarkable. Use all 3 methods (eliminate + automate + predict).

Mistake 3: Designing expansion path after launch Fix: Trojan Horse needs Stage 0→1→2 roadmap from Day 1 for data accumulation.

Mistake 4: Running 10+ experiments per week Fix: Focus on 2-3 high-impact experiments. Quality > Quantity.


Integration with Other Skills

This framework integrates with:

  • market-strategy: Apply Toss patterns to Q1-Q4 (entry), Q13-Q16 (expansion) of 16-question framework
  • roi-analyzer: Calculate ROI for each Trojan Horse stage (Pattern 2)
  • strategic-thinking: Use SWOT for competitive analysis, Divide & Conquer for complex launches

Next Steps

For Detailed Patterns: See REFERENCE.md for:

  • Complete Toss timeline (2013-2025)
  • All 7 patterns with deep-dive analysis
  • Advanced pattern combinations
  • Regulatory opportunity framework
  • Industry-specific best practices

For Real-World Examples: See EXAMPLES.md for:

  • 5+ comprehensive case studies
  • Multiple industries (fintech, SaaS, e-commerce, healthcare)
  • Pattern combinations in action
  • Failure scenarios and how to avoid them

Meta Note

After applying these patterns, always reflect:

  • Which patterns worked best for your context?
  • What industry adaptations were needed?
  • What assumptions need validation through experiments?

This reflection creates a virtuous cycle of continuous pattern learning and application.


For detailed usage and examples, see related documentation files.

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