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name: scenario-thinking description: Scenario-driven solution design that focuses on future use cases, dimensional analysis, and explicit trade-offs

Scenario-Driven Thinking

When to Use

Use this skill whenever you are designing solutions, making architectural decisions, or choosing between different approaches. This skill ensures that solutions are grounded in real-world usage scenarios and that trade-offs are made consciously and explicitly.

Always apply this skill when:

• Designing new features or systems
• Making technology or architecture choices
• Refactoring existing code
• Optimizing performance or structure
• Choosing between multiple implementation approaches
• Any decision that has multiple viable options

Core Concepts

1. Scenario-First Thinking

Solutions should be driven by concrete future usage scenarios, not abstract ideals. Before proposing any solution, understand how it will actually be used.

2. Dimensional Analysis

Every scenario has multiple dimensions to consider (performance, maintainability, scalability, cost, complexity, etc.). Different scenarios prioritize different dimensions.

3. Conscious Trade-offs

There is no perfect solution - every choice sacrifices something. The key is to make these sacrifices intentionally based on the scenario's priorities.

4. Explicit Communication

Users must understand what they're gaining and what they're giving up with each decision.

How to Use

Step 1: Request Future Usage Scenarios

Always start by asking the user about their intended usage scenarios. Never proceed with a solution until you understand the context.

Questions to ask:

• "What are the expected usage scenarios for this feature?"
• "How will this be used in production?"
• "What are the typical and edge case scenarios?"
• "How frequently will this be used?"
• "Who are the users and what are their constraints?"

Step 2: Identify Scenario Dimensions

For each scenario, analyze the relevant dimensions. Common dimensions include:

Performance Dimensions:

• Latency (response time)
• Throughput (requests per second)
• Resource usage (CPU, memory, disk)
• Concurrency (number of simultaneous operations)

Maintainability Dimensions:

• Code readability and clarity
• Test coverage and testability
• Documentation completeness
• Debugging ease
• Onboarding time for new developers

Scalability Dimensions:

• Horizontal scalability (add more machines)
• Vertical scalability (add more resources)
• Data growth handling
• User growth handling

Complexity Dimensions:

• Implementation complexity
• Operational complexity
• Learning curve
• Number of dependencies

Cost Dimensions:

• Development time
• Infrastructure cost
• Maintenance cost
• Technical debt

Reliability Dimensions:

• Fault tolerance
• Data consistency
• Error handling
• Recovery mechanisms

Flexibility Dimensions:

• Extensibility
• Configuration options
• Integration capabilities
• Future-proofing

Step 3: Prioritize Dimensions Based on Scenario

Based on the usage scenario, determine which dimensions are:

• Critical: Must be optimized, cannot be sacrificed
• Important: Should be balanced, some compromise acceptable
• Acceptable: Can be sacrificed if needed for critical dimensions

Example:

CopyScenario: Real-time trading system
Critical: Latency, reliability, data consistency
Important: Monitoring, error handling
Acceptable: Development speed, code simplicity (can use complex optimizations)

Step 4: Propose Solutions with Explicit Trade-offs

For each solution option, clearly state:

1. What it optimizes for (which dimensions it prioritizes)
2. What it sacrifices (which dimensions are compromised)
3. Why this makes sense (how it aligns with the scenario)

Template:

CopyOption [X]: [Solution Name]

Optimizes for:
- [Dimension 1]: [How/Why]
- [Dimension 2]: [How/Why]

Sacrifices:
- [Dimension 3]: [What is compromised and to what extent]
- [Dimension 4]: [What is compromised and to what extent]

Scenario fit:
[Explain why this trade-off makes sense for the stated scenario]

Step 5: Provide Comparison Table (for multiple options)

When presenting multiple options, provide a clear comparison:

Copy| Dimension       | Option A | Option B | Option C |
|-----------------|----------|----------|----------|
| Performance     | ★★★★★    | ★★★☆☆    | ★★☆☆☆    |
| Maintainability | ★★☆☆☆    | ★★★★☆    | ★★★★★    |
| Complexity      | ★☆☆☆☆    | ★★★☆☆    | ★★★★☆    |
| Cost            | ★★☆☆☆    | ★★★★☆    | ★★★★★    |

Recommendation: [Option X] because [scenario requires Y and Z]

Common Scenario Patterns

Pattern 1: MVP / Early Stage Product

Prioritize: Development speed, flexibility, cost Sacrifice: Performance optimization, scalability (can refactor later) Reasoning: Need to validate product-market fit quickly

Pattern 2: High-Traffic Production System

Prioritize: Performance, reliability, scalability Sacrifice: Development speed, code simplicity Reasoning: System maturity justifies investment in optimization

Pattern 3: Internal Tool

Prioritize: Simplicity, maintainability, development speed Sacrifice: Polish, performance optimization, edge case handling Reasoning: Limited users, can fix issues as they arise

Pattern 4: Library/Framework

Prioritize: Flexibility, documentation, backward compatibility Sacrifice: Specific optimizations, opinionated design Reasoning: Must serve diverse use cases

Pattern 5: Data Pipeline / Batch Processing

Prioritize: Throughput, cost efficiency, fault tolerance Sacrifice: Latency (can be slower as long as it completes) Reasoning: Eventual completion more important than speed

Pattern 6: Prototype / Proof of Concept

Prioritize: Speed of implementation, demonstration of core concept Sacrifice: Production quality, error handling, testing Reasoning: Exploratory phase, may be discarded

Best Practices

DO:

✓ Always ask about usage scenarios before proposing solutions ✓ Make trade-offs explicit and conscious ✓ Revisit scenarios if requirements change ✓ Document the reasoning behind trade-off decisions ✓ Validate assumptions about scenarios with users ✓ Consider both current and future scenarios (6-12 months) ✓ Be honest about limitations of proposed solutions

DON'T:

✗ Propose solutions before understanding the scenario ✗ Hide or downplay trade-offs ✗ Optimize for dimensions that don't matter for the scenario ✗ Assume one-size-fits-all solutions ✗ Over-engineer for hypothetical future needs ✗ Under-engineer for known near-term needs ✗ Make decisions based on personal preferences rather than scenario needs

Example: Complete Scenario Analysis

User Request

"I need to add a caching layer to our API."

Scenario Discovery

Question: "What are the expected usage scenarios for this cache?"

User Response: "Our API serves product catalog data. We have 10,000 products, each product is viewed 50-100 times per day on average, and product data is updated once per day during a nightly batch job. We have about 5,000 concurrent users during peak hours."

Dimensional Analysis

Based on this scenario:

Critical Dimensions:

• Read performance (high read frequency)
• Cost efficiency (predictable access patterns allow optimization)
• Staleness tolerance (data changes only daily)

Important Dimensions:

• Cache hit rate
• Memory usage
• Simplicity (small team)

Acceptable to Sacrifice:

• Write performance (only nightly updates)
• Immediate consistency (daily updates are acceptable)
• Distributed cache complexity (single instance may suffice)

Solution Proposal

Option A: In-Memory Cache with Daily Refresh

Optimizes for:

• Read performance: Sub-millisecond latency from memory
• Cost efficiency: No external cache infrastructure needed
• Simplicity: Single-process cache, easy to maintain

Sacrifices:

• Cache durability: Lost on restart (acceptable since data refreshes daily)
• Distributed scalability: Single instance only (may need change if traffic grows 10x)
• Fine-grained updates: Cannot update individual products mid-day

Scenario fit: This is ideal for your scenario because:

1. Daily refresh matches your update frequency perfectly
2. 10,000 products easily fit in memory (~100MB with product details)
3. Simplicity reduces operational overhead for small team
4. Cost is minimal (just memory on existing servers)

Option B: Redis Distributed Cache

Optimizes for:

• Scalability: Multiple API servers can share cache
• Durability: Cache persists across restarts
• Fine-grained updates: Can invalidate specific products

Sacrifices:

• Complexity: Additional infrastructure to manage
• Cost: Redis hosting/management overhead
• Latency: Network hop adds 1-2ms vs in-memory

Scenario fit: Over-engineered for current scenario, but consider if:

1. You plan to scale to 10+ API servers
2. You need to update specific products outside the nightly batch
3. You have ops team to manage Redis

Option C: CDN Edge Caching

Optimizes for:

• Global latency: Cache at edge locations
• Scalability: Handles massive traffic spikes
• Offload: Reduces origin server load

Sacrifices:

• Cost: CDN fees
• Control: Cache invalidation can be delayed
• Complexity: Configuration and debugging

Scenario fit: Not recommended for your scenario because:

1. Your traffic (5K concurrent) doesn't require CDN
2. All users likely in same region
3. Cost doesn't justify the benefit

Comparison Table

Recommendation: Option A (In-Memory Cache) because:

• Matches your daily refresh pattern perfectly
• Minimal complexity for small team
• Best cost-performance ratio for your traffic level
• Can migrate to Redis later if you need distribution

Migration Path: If traffic grows 10x or you add multiple API servers, consider Redis at that time. The code changes will be minimal.

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Summary

Scenario-driven thinking ensures that solutions are practical, well-reasoned, and aligned with actual needs. By always starting with usage scenarios, analyzing relevant dimensions, and making explicit trade-offs, you create solutions that are fit for purpose rather than theoretically optimal but impractical.

Remember: There is no perfect solution, only the right solution for the specific scenario.