Add Intelligent Design Pattern Recommendation System

.claude/commands/dp/analyze.md

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+# /dp::analyze - Comprehensive Design Pattern Analysis
+
+**Purpose**: Deep analysis using sequential thinking for complex pattern decisions
+
+## Command Usage
+```
+/dp::analyze <problem description>
+```
+
+## Execution Strategy
+
+You are a design pattern expert with deep knowledge of the 10 patterns in this repository. When a user provides a problem description, perform comprehensive pattern analysis using the sequential thinking tool.
+
+### Step 1: Use Sequential Thinking
+Activate sequential thinking to systematically analyze the problem:
+
+```
+Use the mcp__sequential-thinking__sequentialthinking tool with these thoughts:
+
+Thought 1: Problem Understanding
+- What specific problem is the user trying to solve?
+- What are the constraints, requirements, and context?
+- What's the expected scale and complexity?
+
+Thought 2: Complexity Assessment
+- How many variations/algorithms/types are involved?
+- What's the growth potential and change frequency?
+- What are the performance and threading requirements?
+- What's the team's expertise level?
+
+Thought 3: Pattern Candidate Evaluation
+- Which patterns from our knowledge base could apply?
+- What are the specific threshold criteria each pattern requires?
+- How do the candidates compare against our complexity thresholds?
+
+Thought 4: Repository Knowledge Application
+- What do our extracted notebooks say about this scenario?
+- Are there specific use cases or examples that match?
+- What are the threading/performance considerations?
+
+Thought 5: Simple Alternative Analysis
+- Could a non-pattern approach solve this effectively?
+- What would the simple Python solution look like?
+- Would the pattern add real value or just complexity?
+
+Thought 6: Anti-Pattern Detection
+- Do I see any red flags from our anti-pattern knowledge?
+- Is this a case of overengineering or premature optimization?
+- Are there better architectural approaches?
+
+Thought 7: Advanced Scenario Considerations
+- Does this require thread-safety considerations?
+- What are the testing implications?
+- Are there performance trade-offs to consider?
+- How does this fit into larger architectural patterns?
+
+Thought 8: Confidence and Recommendation
+- What's my confidence level in the recommendation?
+- What are the key deciding factors?
+- What alternatives should be mentioned?
+- What implementation guidance should I provide?
+```
+
+### Step 2: Apply Repository Knowledge
+Reference the specific patterns and their thresholds from this repository:
+
+#### Pattern Thresholds (from pattern_knowledge.py):
+- **Singleton**: Use for expensive objects, global access, shared state. Avoid for data models.
+- **Factory**: Use for 3+ similar classes, complex creation logic. Avoid for simple cases.
+- **Observer**: Use for 2+ observers, event-driven architecture. Consider thread safety.
+- **Strategy**: Use for 3+ algorithms, runtime switching. Consider if/else for 2-3 cases.
+- **Command**: Use for undo/redo, queuing, macros. Avoid for simple operations.
+- **Builder**: Use for 5+ parameters, complex construction. Consider dataclasses for simple cases.
+- **Repository**: Use for multiple data sources, complex queries. Avoid generic repository anti-pattern.
+- **Adapter**: Use for incompatible interfaces, third-party integration. Simplest pattern when you can't modify interfaces.
+- **Decorator**: Use for 3+ optional features, multiple combinations. Consider inheritance for 2-3 features.
+- **State**: Use for 3+ states, different behaviors per state. Consider enum for simple states.
+
+### Step 3: Provide Structured Response
+Format your final response as:
+
+```
+🧠 **Pattern Analysis: [Problem Summary]**
+
+## Problem Assessment
+- **Complexity**: [Low/Medium/High]
+- **Scale**: [Number of components/algorithms/states]
+- **Growth Potential**: [Expected evolution]
+
+## Pattern Evaluation
+### Primary Recommendation: **[Pattern Name]** 
+**Confidence**: [LOW/MEDIUM/HIGH/CRITICAL]
+
+**Why this pattern fits**:
+- [Specific threshold criteria met]
+- [Repository use case match]
+- [Expected benefits]
+
+**Implementation approach**:
+- [Key design decisions]
+- [Threading considerations if applicable]
+- [Performance implications]
+
+### Alternative Approaches
+1. **[Alternative 1]**: [Brief description and trade-offs]
+2. **[Alternative 2]**: [Brief description and trade-offs]
+
+## Anti-Pattern Check
+[Any warnings about overengineering or misuse]
+
+## Repository References
+- **Notebook**: `learning-resources/notebooks/[XX]_[pattern]_pattern.ipynb`
+- **Implementation**: `learning-resources/examples/implementations/patterns/[pattern].py`
+- **Tests**: `learning-resources/examples/tests/test_patterns/test_[pattern].py`
+
+## Next Steps
+1. [Immediate implementation guidance]
+2. [Testing strategy]
+3. [Future considerations]
+```
+
+### Step 4: Handle Edge Cases
+- **Multiple viable patterns**: Provide comparison matrix
+- **No pattern needed**: Explain why simple approach is better
+- **Borderline cases**: Discuss decision factors and thresholds
+- **Anti-pattern detected**: Provide strong warning and alternatives
+
+## Pattern Recognition Triggers
+Auto-activate deeper analysis when detecting:
+- Multiple pattern keywords in problem description
+- Complex scenarios with >3 components
+- Growth/scalability requirements mentioned
+- Performance or threading concerns
+- Team experience level mentioned
+- Legacy system integration
+- "Future-proofing" or "extensibility" mentioned
+
+## Examples of Quality Analysis
+
+### High-Quality Response Pattern:
+```
+🧠 **Pattern Analysis: Multi-format Report Generation System**
+
+## Problem Assessment
+- **Complexity**: High (4+ formats, different logic per format)
+- **Scale**: 4 current formats, growth expected
+- **Growth Potential**: High (customer requests for new formats)
+
+## Pattern Evaluation
+### Primary Recommendation: **Strategy Pattern**
+**Confidence**: HIGH
+
+**Why this pattern fits**:
+✓ 4+ algorithms (exceeds threshold of 3)
+✓ Runtime selection needed
+✓ Expected growth potential
+✓ Different logic per format
+
+**Implementation approach**:
+- Create ReportGenerator interface
+- Implement strategy for each format (PDF, Excel, CSV, Word)
+- Use ReportService as context for strategy selection
+- Consider factory for strategy creation
+
+### Alternative Approaches
+1. **Simple if/else**: Would work but becomes unwieldy at 4+ formats
+2. **Plugin architecture**: Overkill unless external format providers needed
+
+## Anti-Pattern Check
+✅ No overengineering detected - complexity justifies pattern usage
+
+## Repository References
+- **Notebook**: `learning-resources/notebooks/04_strategy_pattern.ipynb`
+- **Implementation**: `learning-resources/examples/implementations/patterns/strategy.py`
+
+## Next Steps
+1. Define ReportGenerator interface
+2. Extract existing format logic to separate strategies
+3. Implement strategy selection mechanism
+4. Add comprehensive tests for each format
+```
+
+Remember: Always prioritize solving real problems over pattern usage. When in doubt, recommend the simpler approach.