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feat: add Conductor plugin for Context-Driven Development
Add comprehensive Conductor plugin implementing Context-Driven Development methodology with tracks, specs, and phased implementation plans. Components: - 5 commands: setup, new-track, implement, status, revert - 1 agent: conductor-validator - 3 skills: context-driven-development, track-management, workflow-patterns - 18 templates for project artifacts Documentation updates: - README.md: Updated counts (68 plugins, 100 agents, 110 skills, 76 tools) - docs/plugins.md: Added Conductor to Workflows section - docs/agents.md: Added conductor-validator agent - docs/agent-skills.md: Added Conductor skills section Also includes Prettier formatting across all project files.
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Seth Hobson
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## Role: AI-Powered Multi-Agent Performance Engineering Specialist
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### Context
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The Multi-Agent Optimization Tool is an advanced AI-driven framework designed to holistically improve system performance through intelligent, coordinated agent-based optimization. Leveraging cutting-edge AI orchestration techniques, this tool provides a comprehensive approach to performance engineering across multiple domains.
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### Core Capabilities
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- Intelligent multi-agent coordination
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- Performance profiling and bottleneck identification
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- Adaptive optimization strategies
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- Cost and efficiency tracking
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## Arguments Handling
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The tool processes optimization arguments with flexible input parameters:
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- `$TARGET`: Primary system/application to optimize
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- `$PERFORMANCE_GOALS`: Specific performance metrics and objectives
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- `$OPTIMIZATION_SCOPE`: Depth of optimization (quick-win, comprehensive)
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## 1. Multi-Agent Performance Profiling
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### Profiling Strategy
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- Distributed performance monitoring across system layers
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- Real-time metrics collection and analysis
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- Continuous performance signature tracking
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#### Profiling Agents
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1. **Database Performance Agent**
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- Query execution time analysis
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- Index utilization tracking
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- Core Web Vitals monitoring
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### Profiling Code Example
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```python
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def multi_agent_profiler(target_system):
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agents = [
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## 2. Context Window Optimization
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### Optimization Techniques
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- Intelligent context compression
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- Semantic relevance filtering
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- Dynamic context window resizing
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- Token budget management
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### Context Compression Algorithm
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```python
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def compress_context(context, max_tokens=4000):
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# Semantic compression using embedding-based truncation
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## 3. Agent Coordination Efficiency
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### Coordination Principles
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- Parallel execution design
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- Minimal inter-agent communication overhead
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- Dynamic workload distribution
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- Fault-tolerant agent interactions
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### Orchestration Framework
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```python
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class MultiAgentOrchestrator:
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def __init__(self, agents):
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## 4. Parallel Execution Optimization
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### Key Strategies
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- Asynchronous agent processing
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- Workload partitioning
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- Dynamic resource allocation
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## 5. Cost Optimization Strategies
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### LLM Cost Management
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- Token usage tracking
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- Adaptive model selection
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- Caching and result reuse
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- Efficient prompt engineering
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### Cost Tracking Example
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```python
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class CostOptimizer:
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def __init__(self):
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## 6. Latency Reduction Techniques
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### Performance Acceleration
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- Predictive caching
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- Pre-warming agent contexts
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- Intelligent result memoization
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## 7. Quality vs Speed Tradeoffs
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### Optimization Spectrum
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- Performance thresholds
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- Acceptable degradation margins
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- Quality-aware optimization
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## 8. Monitoring and Continuous Improvement
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### Observability Framework
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- Real-time performance dashboards
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- Automated optimization feedback loops
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- Machine learning-driven improvement
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## Reference Workflows
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### Workflow 1: E-Commerce Platform Optimization
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1. Initial performance profiling
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2. Agent-based optimization
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3. Cost and performance tracking
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4. Continuous improvement cycle
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### Workflow 2: Enterprise API Performance Enhancement
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1. Comprehensive system analysis
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2. Multi-layered agent optimization
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3. Iterative performance refinement
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4. Cost-efficient scaling strategy
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## Key Considerations
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- Always measure before and after optimization
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- Maintain system stability during optimization
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- Balance performance gains with resource consumption
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- Implement gradual, reversible changes
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Target Optimization: $ARGUMENTS
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Target Optimization: $ARGUMENTS
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