claude-code-collective/templates/.claude-collective/research.md

Sub-agent collective framework for Claude Code with TDD validation, hub-spoke coordination, and automated handoffs

# Research Hypotheses Framework

## JIT Hypothesis (Just-in-Time Context Loading) - IMPLEMENTED IN ARCHITECTURE
**Theory**: On-demand resource allocation improves efficiency over pre-loading
**Implementation**: Modular file imports - Claude only loads specific context when needed via @ imports
**Validation**: 
- **Before**: 270-line monolithic CLAUDE.md with all technical details loaded always
- **After**: 97-line behavioral core + on-demand imports of technical details
- **Result**: ~65% context reduction, focused behavioral processing

**Success Metrics ACHIEVED**: 
- Context load reduction: 65% (exceeded 30% target)
- Behavioral focus: Core identity fits on 2 screens
- Modular loading: Technical details loaded only when relevant

## Hub-Spoke Hypothesis (Centralized Coordination)
**Theory**: Central hub coordination outperforms distributed agent communication
**Validation**: Compare coordination overhead and error rates
**Success Metrics**:
- Routing accuracy >95%
- Coordination overhead <10% of total execution
- Zero peer-to-peer communication violations

## TDD Hypothesis (Test-Driven Development)
**Theory**: Test-first handoffs improve quality and reduce integration failures
**Validation**: Track handoff success rates and defect density
**Success Metrics**:
- Handoff success rate >98%
- Integration defect reduction >50%
- Test coverage >90% for all agent interactions

## Success Metrics and KPIs

### Collective Performance Metrics
- **Routing Accuracy**: Target >95% correct agent selection
- **Implementation Success**: Target >98% first-pass success
- **Directive Compliance**: Target 100% (zero violations)
- **Context Retention**: Target >90% context preservation across handoffs
- **Time to Resolution**: Target <50% improvement over direct implementation

### Research Validation Metrics
- **JIT Efficiency**: Context loading time and memory usage
- **Hub-Spoke Overhead**: Coordination vs execution time ratio
- **TDD Quality**: Defect rates and handoff success rates

## Continuous Learning and Adaptation

### Pattern Recognition
- Track successful routing patterns
- Identify common failure modes
- Optimize agent selection algorithms
- Refine handoff protocols

### Collective Evolution
- Agent capability expansion based on demand
- New agent creation for emerging needs
- Retired agent lifecycle management
- Performance optimization and tuning