# Strategic Analysis Features Release Plan (v0.6.x+)
## Executive Summary
This release plan introduces **Strategic Analysis** capabilities - a new layer of intelligence that synthesizes insights from multiple sources to support feature planning and technical decision-making. Unlike tactical bug fixes, this focuses on strategic project direction while maintaining extreme token efficiency.
## Core Design Principles
### 1. **Token Efficiency First**
- Pre-computed metrics stored outside conversation
- Hierarchical summaries (500 → 2000 → detailed tokens)
- Background processing for expensive computations
- Cache everything reusable
### 2. **Analytics, Not Search**
- Move from "finding code" to "understanding patterns"
- Aggregate insights, not raw data
- Strategic recommendations, not tactical fixes
### 3. **Progressive Disclosure**
- Start with high-level insights
- Drill down only when needed
- User controls token consumption
## Release Timeline
### v0.6.0: Analytics Foundation (2 weeks)
**Goal**: Establish the core analytics infrastructure
#### Components:
1. **Metrics Collection System**
- Hook into existing indexing pipeline
- Extract code complexity metrics (cyclomatic, cognitive)
- Track file churn and modification patterns
- Store in lightweight SQLite database
2. **Analytics Storage Layer**
```python
analytics/
├── metrics.db # SQLite for structured metrics
├── summaries/ # Pre-computed JSON summaries
│ ├── daily/ # Daily snapshots
│ ├── weekly/ # Weekly aggregates
│ └── current.json # Latest state
└── cache/ # Temporary computations
```
3. **Background Processing**
- GitHub Action for nightly analytics updates
- Incremental updates during reindex operations
- Configurable processing schedules
4. **MCP Tools**:
- `get_code_metrics`: Module-level complexity and churn
- `get_project_health`: Overall project health indicators
- `get_analytics_summary`: High-level project insights
#### Deliverables:
- [ ] SQLite schema for metrics storage
- [ ] Metrics extraction during indexing
- [ ] Background analytics processor
- [ ] 3 basic MCP tools
- [ ] Documentation and examples
### v0.6.1: Issue Pattern Analysis (1.5 weeks)
**Goal**: Identify patterns across GitHub issues for strategic planning
#### Components:
1. **Issue Clustering Engine**
- Group issues by semantic similarity
- Extract common themes and pain points
- Track issue velocity and resolution patterns
2. **Pattern Storage**
```json
{
"clusters": {
"authentication": {
"issue_count": 8,
"avg_resolution_time": "3.5 days",
"related_modules": ["auth/", "middleware/"],
"complexity_correlation": 0.82,
"trending": "increasing"
}
}
}
```
3. **MCP Tools**:
- `analyze_issue_patterns`: Get issue clusters and trends
- `get_feature_landscape`: Cross-reference issues with code metrics
- `predict_impact`: Estimate complexity of proposed changes
#### Deliverables:
- [ ] Issue clustering algorithm
- [ ] Pattern detection system
- [ ] Trend analysis
- [ ] 3 pattern analysis MCP tools
- [ ] Integration with existing GitHub tools
### v0.6.2: Technical Debt Intelligence (1.5 weeks)
**Goal**: Identify and prioritize technical debt based on impact
#### Components:
1. **Debt Detection System**
- High complexity + high churn = debt hotspot
- Outdated dependencies tracking
- Code smell detection (basic)
- Test coverage correlation
2. **Debt Prioritization**
```json
{
"debt_items": [
{
"module": "auth/login.py",
"debt_score": 8.5,
"reasons": ["complexity: 12", "churn: 15/month", "no tests"],
"blocking_features": ["SSO", "2FA"],
"estimated_effort": "3-5 days"
}
]
}
```
3. **MCP Tools**:
- `analyze_technical_debt`: Get prioritized debt list
- `get_refactoring_targets`: Modules needing refactoring
- `estimate_debt_impact`: Impact of debt on new features
#### Deliverables:
- [ ] Debt scoring algorithm
- [ ] Dependency analyzer
- [ ] Impact estimation
- [ ] 3 debt analysis MCP tools
- [ ] Debt dashboard JSON
### v0.6.3: Strategic Planning Assistant (2 weeks)
**Goal**: Synthesize all analytics into actionable recommendations
#### Components:
1. **Recommendation Engine**
- Combines metrics, patterns, and debt analysis
- Generates prioritized action items
- Considers dependencies and blockers
2. **Planning Summaries**
```json
{
"recommendations": [
{
"priority": 1,
"type": "refactor",
"target": "authentication module",
"rationale": "3 open issues + blocking SSO feature + high complexity",
"estimated_impact": "Unblocks 5 features, reduces support by 30%",
"effort": "1 sprint",
"dependencies": []
}
]
}
```
3. **MCP Tools**:
- `suggest_next_priority`: Get top recommendations
- `plan_sprint`: Generate sprint plan based on analytics
- `analyze_feature_feasibility`: Assess new feature complexity
#### Deliverables:
- [ ] Recommendation algorithm
- [ ] Priority scoring system
- [ ] Sprint planning logic
- [ ] 3 planning MCP tools
- [ ] Integration tests
### v0.6.4: Analytics Optimization (1 week)
**Goal**: Optimize performance and token usage
#### Components:
1. **Performance Tuning**
- Query optimization for SQLite
- Smarter caching strategies
- Incremental computation
2. **Token Optimization**
- Response compression
- Smart truncation
- Progressive detail levels
3. **Monitoring**
- Analytics performance metrics
- Token usage tracking
- Cache hit rates
#### Deliverables:
- [ ] Performance benchmarks
- [ ] Optimization implementation
- [ ] Monitoring dashboard
- [ ] Updated documentation
## v0.7.x: Advanced Analytics
### v0.7.0: Predictive Analytics (3 weeks)
- Development velocity predictions
- Bug rate forecasting
- Feature complexity estimation
- Resource planning recommendations
### v0.7.1: Architecture Analysis (2 weeks)
- Dependency graph analysis
- Architecture debt detection
- Modularity scoring
- Refactoring impact analysis
### v0.7.2: Team Analytics (2 weeks)
- Code ownership patterns
- Expertise mapping
- Collaboration insights
- Knowledge gap identification
## Implementation Strategy
### Phase 1: Foundation (v0.6.0)
1. **Week 1**: Storage layer and schema design
2. **Week 2**: Basic metrics collection and MCP tools
### Phase 2: Intelligence (v0.6.1-v0.6.2)
3. **Week 3-4**: Issue pattern analysis
4. **Week 5-6**: Technical debt detection
### Phase 3: Synthesis (v0.6.3-v0.6.4)
5. **Week 7-8**: Strategic planning assistant
6. **Week 9**: Optimization and polish
## Token Budget Example
### Analyzing Next Sprint Priority
```
Input: "suggest next priority"
Token Cost: ~50 tokens
Output (Summary Level):
- Priority: Refactor auth module
- Reason: 3 issues + blocks SSO
- Effort: 3-5 days
- Impact: High
Token Cost: ~200 tokens
Total: 250 tokens (vs 50,000+ for full analysis)
```
### Progressive Detail
```
Level 1 (200 tokens): Summary recommendation
Level 2 (800 tokens): + specific issues and metrics
Level 3 (2000 tokens): + code examples and implementation plan
Level 4 (5000 tokens): + full dependency analysis
```
## Storage Requirements
### Estimated Sizes
- **Metrics DB**: ~10MB per 100k LOC
- **Summaries**: ~1MB per project
- **Cache**: ~5MB temporary
- **Total**: <20MB overhead
### Update Frequency
- **Real-time**: File metrics on reindex
- **Hourly**: Issue pattern updates
- **Daily**: Full analytics refresh
- **Weekly**: Trend analysis
## Success Metrics
### Token Efficiency
- Target: <500 tokens per strategic query
- 95% of queries answered from cache
- 10x reduction vs. raw analysis
### Decision Quality
- Time to identify priority: -80%
- Accuracy of effort estimates: ±20%
- Feature planning efficiency: +50%
### User Satisfaction
- "Helps me plan sprints": >4.5/5
- "Identifies real problems": >4.5/5
- "Saves planning time": >4.5/5
## Risk Mitigation
### Performance Risks
- **Risk**: Slow analytics computation
- **Mitigation**: Background processing, incremental updates
### Accuracy Risks
- **Risk**: Wrong recommendations
- **Mitigation**: Confidence scores, human override
### Adoption Risks
- **Risk**: Users don't trust analytics
- **Mitigation**: Explainable recommendations, gradual rollout
## Migration Path
### From v0.3.x to v0.6.x
1. **No breaking changes**: Analytics are additive
2. **Gradual adoption**: Start with metrics, add intelligence
3. **Backward compatible**: All existing tools continue working
### Data Migration
1. **Initial scan**: One-time full project analysis
2. **Incremental updates**: Hook into existing reindex
3. **No downtime**: Analytics built in background
## Future Vision (v0.8.x+)
### ML-Enhanced Analytics
- Learn from user decisions
- Improve recommendations over time
- Project-specific pattern recognition
### Cross-Project Intelligence
- Learn patterns across projects
- Industry best practices
- Community insights (opt-in)
### Real-time Collaboration
- Team planning sessions
- Shared analytics dashboards
- Collaborative decision making
## Conclusion
This strategic analysis layer transforms our RAG server from a "code finder" to a "project intelligence system" while maintaining extreme token efficiency. By pre-computing analytics and using progressive disclosure, we enable strategic planning without burning through context windows.
The key innovation is moving expensive computation outside the conversation, storing insights not data, and giving users exactly the level of detail they need for decision-making.
