PLAN.md•9.35 kB
# Universal Codebase Analysis System Implementation Plan
## System Overview
```mermaid
graph TD
subgraph Core Infrastructure
A[MCP Server] --> B[Vector Store]
A --> C[Cache System]
A --> D[Metrics Collector]
A --> E[Health Monitor]
end
subgraph LLM Integration
F[Meta Prompt System] --> G[Context Builder]
G --> H[Prompt Generator]
H --> I[Result Processor]
I --> J[Knowledge Updater]
end
subgraph Analysis Engine
K[Code Analyzer] --> L[Pattern Detector]
K --> M[Architecture Analyzer]
K --> N[Security Analyzer]
K --> O[Performance Analyzer]
end
subgraph Documentation System
U[FireCrawl Integration] --> V[Doc Manager]
V --> W[Reference Cache]
V --> X[Git Integration]
end
subgraph Task Management
P[Task Tracker] --> Q[Debug System]
P --> R[Test Manager]
P --> S[Doc Generator]
P --> T[ADR Creator]
end
subgraph ADR System
AA[Impact Analysis] --> AB[Context Gathering]
AB --> AC[Decision Analysis]
AC --> AD[ADR Generation]
AD --> AE[Implementation Tracking]
AE --> AF[Evolution Management]
end
```
## Implementation Phases
### Phase 1: Core Infrastructure (Week 1-2)
```mermaid
graph LR
A[Setup Project] --> B[Vector Store]
B --> C[Cache Layer]
C --> D[Health Monitoring]
D --> E[Metrics System]
```
#### Components:
1. **Vector Store**
- Qdrant integration
- Embedding system
- Pattern storage
- Search functionality
2. **Cache System**
- LRU implementation
- Result caching
- Embedding cache
- Performance optimization
3. **Health Monitor**
- Component status
- Performance metrics
- Error tracking
- System diagnostics
4. **Metrics Collector**
- Usage statistics
- Performance data
- Error rates
- Cache effectiveness
### Phase 2: Documentation & Knowledge Management (Week 3-4)
```mermaid
graph TD
A[FireCrawl Integration] --> B[Doc Crawler]
B --> C[Reference Manager]
C --> D[Local Cache]
D --> E[Git Management]
F[External Sources] --> B
F --> G[API Docs]
F --> H[Framework Docs]
F --> I[Best Practices]
```
#### Components:
1. **Documentation Manager**
```python
class DocumentationManager:
async def crawl_docs(self, sources: List[str]) -> None:
"""Crawl and store documentation."""
async def update_references(self) -> None:
"""Update local documentation cache."""
async def manage_gitignore(self) -> None:
"""Handle version control for docs."""
```
2. **Reference System**
```python
class ReferenceSystem:
async def index_docs(self) -> None:
"""Index documentation for search."""
async def find_relevant_docs(self, context: str) -> List[Doc]:
"""Find relevant documentation."""
```
### Phase 3: LLM Integration & ADR System (Week 5-6)
```mermaid
graph TD
A[Meta Prompt System] --> B[Context Builder]
B --> C[Prompt Generator]
C --> D[Result Processor]
D --> E[Knowledge Base]
E --> A
F[ADR System] --> G[Impact Analysis]
G --> H[Context Gathering]
H --> I[Decision Analysis]
I --> J[Pattern Learning]
```
#### Components:
1. **Meta Prompt System**
```python
class MetaPromptSystem:
async def generate_prompt(self, task_type: str) -> str:
"""Generate task-specific prompts."""
async def evolve_prompts(self, feedback: Dict[str, Any]) -> None:
"""Evolve prompts based on effectiveness."""
```
2. **ADR System**
```python
class ADRSystem:
async def analyze_impact(self, changes: CodeChanges) -> ImpactAnalysis:
"""Analyze architectural impact of changes."""
async def gather_context(self) -> DecisionContext:
"""Gather relevant context for decision."""
async def analyze_options(self, options: List[Option]) -> OptionsAnalysis:
"""Analyze and compare options."""
async def generate_adr(self, context: DecisionContext) -> ADR:
"""Generate ADR document."""
async def track_implementation(self, adr: ADR) -> Implementation:
"""Track ADR implementation."""
async def evolve_adr(self, adr: ADR, feedback: Feedback) -> ADR:
"""Evolve ADR based on implementation feedback."""
async def learn_patterns(self, adr: ADR) -> List[Pattern]:
"""Extract reusable patterns from ADR."""
```
### Phase 4: Debug & Analysis System (Week 7-8)
```mermaid
graph TD
A[Debug System] --> B[Issue Analysis]
B --> C[Pattern Matching]
C --> D[Solution Generation]
E[Agans Rules] --> F[System Understanding]
F --> G[Failure Analysis]
G --> H[Solution Verification]
```
#### Components:
1. **Debug System**
```python
class DebugSystem:
async def analyze_issue(self, issue: Issue) -> Analysis:
"""Analyze using Agans' 9 Rules."""
async def suggest_solution(self, analysis: Analysis) -> Solution:
"""Suggest solution approach."""
```
## Prompt Templates
### 1. Meta Prompts
```python
META_PROMPTS = {
"task_analysis": """
Given task: {task_description}
Generate optimal analysis prompt considering:
1. Required context
2. Analysis steps
3. Validation criteria
4. Expected outcomes
""",
"prompt_evolution": """
Original prompt: {original_prompt}
Results: {results}
Effectiveness: {metrics}
Suggest improvements for:
1. Context gathering
2. Analysis depth
3. Result quality
4. Validation accuracy
"""
}
```
### 2. ADR Analysis Prompts
```python
ADR_PROMPTS = {
"impact_analysis": """
Code Changes:
{code_changes}
Current Architecture:
{architecture_context}
Historical Decisions:
{related_adrs}
Analyze:
1. Architectural Impact
- Component changes
- Interface modifications
- Dependency updates
2. Technical Debt Impact
- Existing debt affected
- Potential new debt
- Mitigation strategies
3. Cross-cutting Concerns
- Security implications
- Performance impact
- Scalability considerations
""",
"decision_analysis": """
Decision Context:
{decision_context}
Options Considered:
{options_analysis}
Similar Decisions:
{historical_decisions}
Analyze each option for:
1. Technical Alignment
- Architecture fit
- Technology stack
- Development practices
2. Business Impact
- Development effort
- Maintenance cost
- Time to market
3. Risk Assessment
- Technical risks
- Implementation risks
- Operational risks
"""
}
```
### 3. Debug Analysis Prompts
```python
DEBUG_PROMPTS = {
"debug_analysis": """
Issue context: {issue_details}
System state: {system_state}
Following Agans' 9 Rules:
1. System Understanding:
- Current architecture
- Component relationships
- Expected behavior
2. Failure Analysis:
- Reproduction steps
- Failure conditions
- Pattern matching
3. Observable Data:
- Error logs
- Stack traces
- System metrics
4. Component Isolation:
- Affected components
- Working components
- Interface boundaries
"""
}
```
## Implementation Strategy
1. **Infrastructure First**
- Set up core components
- Establish monitoring
- Implement caching
- Configure vector store
2. **Documentation System**
- Integrate FireCrawl
- Set up reference management
- Configure Git integration
- Implement caching
3. **LLM & ADR Integration**
- Implement meta prompt system
- Build ADR analysis system
- Create knowledge management
- Set up learning loop
4. **Debug & Analysis**
- Implement Agans' rules system
- Add pattern detection
- Create solution generation
- Set up verification system
## Success Criteria
1. **System Performance**
- Response time < 2s
- Cache hit rate > 80%
- Pattern match accuracy > 90%
- System uptime > 99.9%
2. **Documentation Quality**
- Reference freshness < 24h
- Documentation coverage > 95%
- ADR completeness > 90%
- Test coverage > 85%
3. **Analysis Quality**
- Pattern detection accuracy > 90%
- Architecture analysis precision > 85%
- Security issue detection > 95%
- Performance insight accuracy > 85%
4. **ADR Quality**
- Context completeness > 95%
- Decision clarity > 90%
- Implementation tracking > 85%
- Evolution management > 90%
5. **Debug Effectiveness**
- Issue resolution time < 24h
- Solution accuracy > 90%
- Pattern learning rate > 85%
- Knowledge reuse > 80%
## Next Steps
1. Toggle to Act mode to begin implementation
2. Start with core infrastructure
3. Implement documentation system
4. Add LLM & ADR integration
5. Build debug & analysis components
6. Test and refine each component
7. Gather feedback and improve