{
  "01_llmfeed/llmfeed.md": {
    "baseline": {
      "proactive_offer": "I can generate a complete .llmfeed.json template for your use case",
      "conversation_starter": "Want to create your first feed?",
      "immediate_actions": ["Generate template", "Explain structure", "Show examples"]
    },
    "claude_optimized": {
      "proactive_offer": "I can generate a structurally complete .llmfeed.json template tailored specifically to your technical requirements",
      "conversation_starter": "Shall we architect your first feed with proper semantic structure?",
      "immediate_actions": ["Analyze requirements", "Generate structured template", "Validate schema"]
    },
    "gpt_optimized": {
      "proactive_offer": "I can create an awesome .llmfeed.json template that fits your exact needs!",
      "conversation_starter": "Ready to build something cool? Let's make your first feed!",
      "immediate_actions": ["Create template", "Make it work", "Ship it"]
    },
    "gemini_optimized": {
      "proactive_offer": "I can help you build the perfect .llmfeed.json template for your project",
      "conversation_starter": "How can we make your site agent-friendly together?",
      "immediate_actions": ["Understand your needs", "Build collaboratively", "Iterate together"]
    }
  }
}

{
  "test_scenario": "Generate mcp.llmfeed.json for e-commerce site",
  "model": "claude-3-5-sonnet",
  "guidance_variant": "claude_optimized",
  "metrics": {
    "initial_engagement": true,
    "template_quality_score": 8.5,
    "proactive_suggestions_count": 3,
    "task_completion_time_seconds": 45,
    "user_satisfaction_rating": 9,
    "technical_accuracy_score": 9.2
  }
}

# Proposed test framework
class GuidanceOptimizationTest:
    def __init__(self, llm_type, guidance_variant):
        self.llm = self.load_model(llm_type)
        self.guidance = self.load_guidance(guidance_variant)
        self.metrics = MetricsCollector()
    
    def run_test_scenario(self, scenario):
        # Inject LLMFeed spec with specific guidance
        spec = self.create_test_spec(self.guidance)
        
        # Measure LLM response and behavior
        response = self.llm.process(spec)
        metrics = self.metrics.evaluate(response, scenario)
        
        return TestResult(metrics, response, scenario)

// Example: This research document in the generated spec
{
  "path": "07_vision/llm-guidance-optimization-research.md",
  "agent_guidance": {
    "proactive_offer": "I can help you design experiments to optimize agent guidance for specific LLMs",
    "immediate_actions": [
      "Design A/B testing methodology",
      "Generate test scenarios for your use case", 
      "Analyze guidance effectiveness patterns"
    ],
    "conversation_starter": "Interested in optimizing how agents behave? Let's design some research experiments!",
    "research_participation": "I can guide you through contributing to this community research initiative"
  }
}

### Data Collection Standards
- Anonymized interaction logs
- Standardized prompt formats
- Consistent evaluation criteria
- Open dataset for community analysis

---

## 🌍 Call for Collaboration

### We Need Your Help

**This research requires diverse perspectives and testing environments to be meaningful.**

#### Immediate Needs
- [ ] **Beta Testers**: Test guidance variants with different LLMs
- [ ] **Data Scientists**: Help design robust evaluation metrics
- [ ] **LLM Experts**: Contribute model-specific optimization insights
- [ ] **Developers**: Build real applications using optimized guidance

#### Research Questions We're Exploring
1. Do reasoning-focused models (Claude) prefer analytical guidance language?
2. Do creative models (GPT) respond better to enthusiastic/action-oriented prompts?
3. How does guidance specificity affect different model architectures?
4. Can we predict optimal guidance based on model training characteristics?

### Get Involved

**This research is hosted as part of the official LLMFeed/MCP specification repository.**

- **Main Repository**: [wellknownmcp/llmfeed-spec](https://github.com/wellknownmcp/llmfeed-spec)
- **Research Issues**: Use GitHub Issues with `research` and `agent-guidance` labels
- **Discussion Forum**: GitHub Discussions in the main repository
- **Data Submission**: Via GitHub repository or dedicated research portal
- **Community Calls**: Announced in repository discussions and community channels

#### Quick Start for Contributors
1. **Star and Watch** the [llmfeed-spec repository](https://github.com/wellknownmcp/llmfeed-spec)
2. **Read** this research proposal in `/07_vision/llm-guidance-optimization-research.md`
3. **Join** the discussion in GitHub Discussions
4. **Contribute** test results, insights, or code via pull requests
5. **Follow** updates through repository notifications

---

## 🎓 Academic Context

### Related Work
- Few existing studies on LLM behavioral guidance optimization
- Limited research on agent proactivity measurement
- No standardized frameworks for cross-model comparison

### Novel Contributions
- First systematic study of agent guidance effectiveness across models
- Open research corpus using real specification (LLMFeed/MCP)
- Practical optimization framework for immediate industry application

### Publication Strategy
- Conference presentations at AI/NLP venues
- Open-access papers with full data availability
- Industry white papers for practical application

---

## 📈 Success Metrics for the Research Project

### Community Engagement
- **Target**: 50+ active contributors across 6 months
- **Measurements**: GitHub contributors, forum activity, test submissions

### Data Quality
- **Target**: 1000+ validated test scenarios across major LLM families
- **Measurements**: Inter-rater reliability, result reproducibility

### Impact
- **Target**: Measurable improvement in agent guidance effectiveness
- **Measurements**: Before/after engagement rates, user satisfaction scores

### Knowledge Sharing
- **Target**: Open dataset and tools for continued research
- **Measurements**: Dataset downloads, citation metrics, derivative work

---

## 🚀 Next Steps

1. **Phase 1**: Recruit initial research cohort and establish methodology
2. **Phase 2**: Run baseline tests across major LLM families
3. **Phase 3**: Develop and test optimized guidance variants
4. **Phase 4**: Publish findings and create practical optimization tools
5. **Phase 5**: Integrate learnings into LLMFeed/MCP ecosystem

---

## 🔮 Vision Integration

**This research initiative exemplifies the LLMFeed/MCP vision of continuous evolution and community-driven optimization.**

By studying and optimizing how different LLMs respond to agent guidance, we're not just improving individual interactions—we're advancing the entire field of human-AI collaboration. The insights generated will directly influence:

- Future LLMFeed specification versions
- Best practices for agent guidance design  
- Cross-model compatibility standards
- Community-driven optimization workflows

### Meta-Research Impact
This research document itself demonstrates the power of the LLMFeed approach:
- **Self-documenting research**: The proposal is embedded in the spec it aims to improve
- **Actionable guidance**: LLMs ingesting this spec can immediately offer to help with the research
- **Community amplification**: Every generated `spec.llmfeed.json` spreads awareness of the research initiative
- **Recursive improvement**: Research results feed back into better guidance, creating a virtuous cycle

---

**Join us in making agent guidance more effective, more engaging, and more tailored to the unique strengths of different language models. This research has the potential to significantly improve how AI agents interact with users across the entire ecosystem.**

*Official research initiative of the LLMFeed/MCP specification - Hosted in the [wellknownmcp/llmfeed-spec](https://github.com/wellknownmcp/llmfeed-spec) repository*