agent-user-testing/01-mechanical-engineering/INSTRUCTIONS.md

@@ -1,11 +1,11 @@
-# Test Scenario 01: Mechanical Engineering - CAD Standards Research
+# Test Scenario 01: Mechanical Engineering - CAD Standards Intelligence
 
 ## 🔧 **Industry Context**: Mechanical Engineering Firm
 **Role**: Junior Mechanical Engineer  
-**Task**: Research CAD modeling standards and best practices for automotive component design
+**Task**: Build a smart CAD standards knowledge base that can instantly answer design questions
 
 ## 📋 **Scenario Description**
-You're a junior mechanical engineer at an automotive parts manufacturer. Your team is developing new suspension components and needs to establish CAD modeling standards that comply with industry requirements. You've been asked to research and compile information about CAD best practices, modeling standards, and automotive design guidelines.
+You're a junior mechanical engineer at an automotive parts manufacturer. Your team constantly struggles to find specific CAD modeling standards, tolerance requirements, and design guidelines buried in hundreds of pages of documentation. You'll use FSS-Mini-RAG to create an intelligent system that can instantly answer any CAD standards question.
 
 ## 🎯 **Your Mission (Completely Autonomous)**
 
@@ -14,49 +14,221 @@ You're a junior mechanical engineer at an automotive parts manufacturer. Your te
 2. Follow the installation instructions for your platform
 3. Verify the installation works by running `rag-mini --help`
 
-### **Step 2: Gather Research Materials**
-Create a folder called `cad-standards-research` and populate it with relevant documentation:
-- Search for and download 3-5 PDF documents about CAD modeling standards (ASME, ISO standards)
-- Find technical documentation about automotive component design guidelines
-- Collect best practices documents for SolidWorks, AutoCAD, or similar CAD software
-- Include any relevant mechanical engineering design standards
+### **Step 2: Gather CAD Standards Documentation**
+Create a folder called `cad-standards-docs` and populate it with relevant documentation:
+- Download ASME Y14.5 geometric dimensioning and tolerancing standards
+- Get ISO 2768 general tolerance standards documentation  
+- Find automotive CAD modeling best practices guides
+- Include SolidWorks/AutoCAD design guidelines
+- Add manufacturing DFM (Design for Manufacturing) standards
 
-**Sources to explore**:
-- ASME standards documentation
-- Automotive industry design guidelines
-- CAD software documentation and tutorials
-- Engineering forums and best practices guides
+**Pro tip**: Look for PDF standards documents, technical guides, and industry best practices.
 
-### **Step 3: Index and Search**
-1. Use FSS-Mini-RAG to index your `cad-standards-research` folder
-2. Perform searches to answer these engineering questions:
-   - "What are the standard tolerances for automotive suspension components?"
-   - "How should CAD models be structured for manufacturing readiness?"
-   - "What are the file naming conventions for engineering drawings?"
-   - "What quality control checks are required for CAD models?"
+### **Step 3: Build Your Intelligent Knowledge Base**
+```bash
+# Navigate to your research folder
+cd cad-standards-docs
 
-### **Step 4: Document Your Findings**
+# Initialize FSS-Mini-RAG index  
+rag-mini init
+
+# Check the index was created successfully
+rag-mini stats
+
+# Get system info to verify everything is working
+rag-mini info
+```
+
+### **Step 4: Test Your CAD Standards Oracle**
+Now for the cool part - your documentation is now searchable with natural language! Test these engineering queries:
+
+```bash
+# Search for tolerance information
+rag-mini search "What are the standard tolerances for holes in automotive suspension components?"
+
+# Find CAD modeling guidelines  
+rag-mini search "How should CAD assembly models be structured for manufacturing?"
+
+# Query design standards
+rag-mini search "What geometric tolerancing symbols are required for shaft fits?"
+
+# Search for file organization
+rag-mini search "What are the file naming conventions for engineering drawings?"
+
+# Look for quality requirements
+rag-mini search "What inspection requirements exist for automotive safety components?"
+```
+
+### **Step 5: Advanced Engineering Searches**
+Try these more sophisticated queries:
+
+```bash
+# Search for specific functions (if you have code documentation)
+rag-mini find-function "tolerance_calculation" 
+
+# Look for classes in programming guides
+rag-mini find-class "DrawingTemplate"
+
+# Update your knowledge base (when you add new documents)
+rag-mini update
+
+# Get detailed statistics about your knowledge base
+rag-mini stats
+```
+
+### **Step 6: Document Your Engineering Intelligence System**
 Write your findings in `RESULTS.md` including:
-- Summary of key CAD modeling standards discovered
-- Specific tolerance requirements for automotive components
-- Best practices for model organization and file management
-- Quality control recommendations for CAD workflows
 
-### **Step 5: Evaluation**
+#### **Knowledge Base Performance**
+- How many documents were indexed?
+- How fast were the search responses?
+- Which types of questions worked best?
+
+#### **Real Engineering Value** 
+- Can you quickly find specific tolerance requirements?
+- Does it help locate CAD best practices efficiently?
+- How does this compare to manual PDF searching?
+
+#### **Professional Impact**
+- How much time would this save during design reviews?
+- Could this help with compliance and standards verification?
+- What would be the value for training new engineers?
+
+### **Step 7: Complete Professional Evaluation**
 Rate FSS-Mini-RAG's effectiveness for:
-- Finding specific engineering standards information
-- Searching through technical documentation
-- Helping with compliance research
-- Overall usefulness for mechanical engineering workflows
+- **Finding specific engineering standards** (1-10)
+- **Answering tolerance and design questions** (1-10)  
+- **Helping with CAD workflow optimization** (1-10)
+- **Overall usefulness for mechanical engineering** (1-10)
 
-## 📁 **Deliverables**
-- `cad-standards-research/` folder with research materials
-- `RESULTS.md` with findings and FSS-Mini-RAG evaluation
-- Documentation of your search queries and results
+### **Step 8: Document Your Experience**
+Create a comprehensive `RESULTS.md` including:
 
-## ⏱️ **Expected Duration**: 2-3 hours
+#### **Executive Summary**
+- What you built (CAD Standards Intelligence System)
+- Key findings and success metrics
+- Professional impact assessment
+
+#### **Technical Details**
+- Number of documents indexed and file sizes
+- Search response times and accuracy ratings
+- Most effective query types and examples
+- Command usage statistics (init, search, stats, etc.)
+
+#### **Professional Value Assessment**
+- Time saved compared to manual document searching
+- Potential impact on design review processes
+- Training value for new engineers
+- Compliance and standards verification improvements
+
+#### **User Experience Report**
+- Installation process evaluation
+- Command usability ratings
+- Documentation quality assessment
+- Suggested improvements or missing features
+
+### **Step 9: Repository Contribution Workflow**
+
+#### **Repository Information**
+- **Repository URL**: `http://192.168.1.3:3000/foxadmin/fss-mini-rag-github.git`
+- **Main Branch**: `main`
+- **Testing Branch**: `agent-user-testing` (where scenarios are located)
+
+#### **Branch Management**
+```bash
+# Clone the repository
+git clone http://192.168.1.3:3000/foxadmin/fss-mini-rag-github.git
+cd fss-mini-rag-github
+
+# Start from the agent-user-testing branch
+git checkout agent-user-testing
+
+# Create your own branch for your results
+git checkout -b agent-test-mechanical-engineering-$(date +%Y%m%d)
+
+# Navigate to your scenario
+cd agent-user-testing/01-mechanical-engineering/
+```
+
+#### **Submit Your Results**
+```bash
+# Add your completed RESULTS.md
+git add RESULTS.md
+
+# Commit with descriptive message
+git commit -m "Agent Test Results: Mechanical Engineering CAD Standards Intelligence
+
+- Tested FSS-Mini-RAG with automotive CAD standards documentation
+- Created intelligent knowledge base for tolerance and design queries
+- Evaluated semantic search effectiveness for engineering workflows
+- Documented professional impact and time-saving potential
+- Rating: [X]/10 overall effectiveness"
+
+# Push your branch
+git push origin agent-test-mechanical-engineering-$(date +%Y%m%d)
+```
+
+#### **Create Pull Request**
+```bash
+# Use gitea CLI to create PR
+gitea prs create "Agent Test: Mechanical Engineering Results" agent-test-mechanical-engineering-$(date +%Y%m%d) agent-user-testing --body "Completed comprehensive testing of FSS-Mini-RAG for mechanical engineering workflows.
+
+## Test Summary
+- Built CAD Standards Intelligence System
+- Indexed [X] engineering documents
+- Tested [X] search queries with [X]% accuracy
+- Overall effectiveness rating: [X]/10
+
+## Key Findings
+[Brief summary of major discoveries]
+
+## Professional Impact
+[Assessment of real-world value for engineers]
+
+## Recommendations
+[Suggestions for improvements or additional features]"
+```
+
+### **Step 10: Validation Requirements**
+
+Your submission must include:
+
+#### **Required Evidence**
+- ✅ **Screenshots** of successful `rag-mini init` and `rag-mini stats` output
+- ✅ **Search examples** with actual query results (at least 5 different searches)
+- ✅ **Performance metrics** (response times, index size, document count)
+- ✅ **Professional assessment** with specific use cases and value propositions
+
+#### **Quality Standards**
+- ✅ **Functional completeness**: All major commands tested (init, search, stats, info)
+- ✅ **Real-world relevance**: Actual industry documents and realistic queries
+- ✅ **Professional writing**: Clear, actionable insights for engineering teams
+- ✅ **Quantitative data**: Specific metrics and measurable outcomes
+
+#### **Submission Checklist**
+- [ ] Created intelligent knowledge base successfully
+- [ ] Tested minimum 5 different search queries
+- [ ] Documented all command usage and results
+- [ ] Provided professional impact assessment
+- [ ] Created proper git branch with descriptive name
+- [ ] Submitted PR with comprehensive description
+- [ ] Included evidence screenshots/outputs
+- [ ] Met all validation requirements
+
+## 📁 **Final Deliverables**
+- `cad-standards-docs/` folder with indexed technical documentation
+- `RESULTS.md` with comprehensive evaluation and evidence
+- Git branch with proper commit history
+- Pull request with detailed description
+- Professional assessment of FSS-Mini-RAG effectiveness
+
+## ⏱️ **Expected Duration**: 3-4 hours (including documentation and PR submission)
+
+## 🎉 **Success Outcome**
+You'll have created an **intelligent CAD standards assistant** AND provided valuable feedback to improve FSS-Mini-RAG for engineering professionals!
 
 ## 🎓 **Learning Objectives**
-- Test FSS-Mini-RAG with technical engineering content
-- Evaluate search effectiveness with standards and specifications
-- Assess usefulness for compliance and best practices research
+- Experience semantic search with technical engineering content
+- Evaluate AI-powered documentation assistance for professional workflows  
+- Test real-world applicability of RAG systems in mechanical engineering
+- Practice professional software evaluation and contribution workflows

agent-user-testing/COMPLETION_WORKFLOW_TEMPLATE.md

@@ -0,0 +1,144 @@
+# Agent Testing Completion Workflow Template
+
+## 🎯 **Universal Completion Steps for All Scenarios**
+
+### **Step 8: Document Your Experience**
+Create a comprehensive `RESULTS.md` including:
+
+#### **Executive Summary**
+- What you built ([Industry-Specific Intelligence System])
+- Key findings and success metrics
+- Professional impact assessment
+
+#### **Technical Details**
+- Number of documents indexed and file sizes
+- Search response times and accuracy ratings
+- Most effective query types and examples
+- Command usage statistics (init, search, stats, info, find-function, find-class, update)
+
+#### **Professional Value Assessment**
+- Time saved compared to manual document searching
+- Potential impact on [industry-specific processes]
+- Training value for new [professionals]
+- [Industry-specific compliance/workflow] improvements
+
+#### **User Experience Report**
+- Installation process evaluation
+- Command usability ratings
+- Documentation quality assessment
+- Suggested improvements or missing features
+
+### **Step 9: Repository Contribution Workflow**
+
+#### **Repository Information**
+- **Repository URL**: `http://192.168.1.3:3000/foxadmin/fss-mini-rag-github.git`
+- **Main Branch**: `main`
+- **Testing Branch**: `agent-user-testing` (where scenarios are located)
+
+#### **Branch Management**
+```bash
+# Clone the repository
+git clone http://192.168.1.3:3000/foxadmin/fss-mini-rag-github.git
+cd fss-mini-rag-github
+
+# Start from the agent-user-testing branch
+git checkout agent-user-testing
+
+# Create your own branch for your results
+git checkout -b agent-test-[SCENARIO-NAME]-$(date +%Y%m%d)
+
+# Navigate to your scenario
+cd agent-user-testing/[XX-scenario-folder]/
+```
+
+#### **Submit Your Results**
+```bash
+# Add your completed RESULTS.md
+git add RESULTS.md
+
+# Commit with descriptive message
+git commit -m "Agent Test Results: [Industry] [System Name]
+
+- Tested FSS-Mini-RAG with [industry-specific] documentation
+- Created intelligent knowledge base for [specific use cases]
+- Evaluated semantic search effectiveness for [industry] workflows
+- Documented professional impact and time-saving potential
+- Rating: [X]/10 overall effectiveness"
+
+# Push your branch
+git push origin agent-test-[SCENARIO-NAME]-$(date +%Y%m%d)
+```
+
+#### **Create Pull Request**
+```bash
+# Use gitea CLI to create PR
+gitea prs create "Agent Test: [Industry] Results" agent-test-[SCENARIO-NAME]-$(date +%Y%m%d) agent-user-testing --body "Completed comprehensive testing of FSS-Mini-RAG for [industry] workflows.
+
+## Test Summary
+- Built [Intelligence System Name]
+- Indexed [X] [industry] documents
+- Tested [X] search queries with [X]% accuracy
+- Overall effectiveness rating: [X]/10
+
+## Key Findings
+[Brief summary of major discoveries]
+
+## Professional Impact
+[Assessment of real-world value for [professionals]]
+
+## Recommendations
+[Suggestions for improvements or additional features]"
+```
+
+### **Step 10: Validation Requirements**
+
+Your submission must include:
+
+#### **Required Evidence**
+- ✅ **Screenshots** of successful `rag-mini init` and `rag-mini stats` output
+- ✅ **Search examples** with actual query results (at least 5 different searches)
+- ✅ **Performance metrics** (response times, index size, document count)
+- ✅ **Professional assessment** with specific use cases and value propositions
+
+#### **Quality Standards**
+- ✅ **Functional completeness**: All major commands tested (init, search, stats, info)
+- ✅ **Real-world relevance**: Actual industry documents and realistic queries
+- ✅ **Professional writing**: Clear, actionable insights for [industry] teams
+- ✅ **Quantitative data**: Specific metrics and measurable outcomes
+
+#### **Submission Checklist**
+- [ ] Created intelligent knowledge base successfully
+- [ ] Tested minimum 5 different search queries
+- [ ] Documented all command usage and results
+- [ ] Provided professional impact assessment
+- [ ] Created proper git branch with descriptive name
+- [ ] Submitted PR with comprehensive description
+- [ ] Included evidence screenshots/outputs
+- [ ] Met all validation requirements
+
+## 📁 **Final Deliverables**
+- `[industry-folder]/` with indexed documentation
+- `RESULTS.md` with comprehensive evaluation and evidence
+- Git branch with proper commit history
+- Pull request with detailed description
+- Professional assessment of FSS-Mini-RAG effectiveness
+
+## ⏱️ **Expected Duration**: 3-4 hours (including documentation and PR submission)
+
+## 🎉 **Success Outcome**
+You'll have created an **intelligent [industry] assistant** AND provided valuable feedback to improve FSS-Mini-RAG for [industry] professionals!
+
+---
+
+## 🔧 **Customization Variables**
+
+For each scenario, replace:
+- `[Industry-Specific Intelligence System]` - e.g., "CAD Standards Intelligence System"
+- `[SCENARIO-NAME]` - e.g., "mechanical-engineering"
+- `[XX-scenario-folder]` - e.g., "01-mechanical-engineering"
+- `[industry-specific]` - e.g., "automotive CAD standards"
+- `[specific use cases]` - e.g., "tolerance and design queries"
+- `[industry]` - e.g., "mechanical engineering"
+- `[professionals]` - e.g., "engineers"
+- `[Intelligence System Name]` - e.g., "CAD Standards Intelligence System"
+- `[industry-folder]` - e.g., "cad-standards-docs"

agent-user-testing/enhance_all_scenarios.py

@@ -0,0 +1,401 @@
+#!/usr/bin/env python3
+"""
+Enhance all agent testing scenarios with functional demonstrations,
+comprehensive command testing, and professional completion workflows.
+"""
+
+import os
+import re
+from pathlib import Path
+
+# Scenario enhancements with functional demonstrations
+scenario_enhancements = {
+    "02-childcare-regulations": {
+        "title": "Childcare Center - Regulatory Compliance Intelligence",
+        "task": "Build a smart regulatory compliance assistant that instantly answers licensing questions",
+        "description": "You're opening a new childcare center and drowning in regulatory requirements from multiple agencies. You'll use FSS-Mini-RAG to create an intelligent compliance system that can instantly answer any licensing, safety, or operational question.",
+        "folder": "childcare-compliance-docs",
+        "system_name": "Childcare Compliance Intelligence System",
+        "commands": [
+            'rag-mini search "What is the minimum square footage required per child in play areas?"',
+            'rag-mini search "What background check requirements exist for childcare staff?"',
+            'rag-mini search "What are the handwashing and sanitation requirements?"',
+            'rag-mini search "How many emergency exits are required for a 50-child facility?"',
+            'rag-mini search "What staff-to-child ratios are mandated for different age groups?"'
+        ],
+        "advanced_commands": [
+            'rag-mini find-function "safety_checklist"',
+            'rag-mini find-class "ComplianceRecord"'
+        ],
+        "professional_impact": [
+            "How much time would this save during licensing preparation?",
+            "Could this help ensure full compliance and avoid violations?",
+            "What would be the value for training new childcare staff?"
+        ]
+    },
+    
+    "03-plant-logistics": {
+        "title": "Plant Logistics - Warehouse Intelligence System",
+        "task": "Build a smart logistics assistant that optimizes warehouse operations and supply chain efficiency",
+        "description": "You're managing a manufacturing plant with supply chain bottlenecks and warehouse inefficiencies. You'll use FSS-Mini-RAG to create an intelligent operations system that can instantly provide optimization strategies and best practices.",
+        "folder": "logistics-optimization-docs",
+        "system_name": "Warehouse Operations Intelligence System",
+        "commands": [
+            'rag-mini search "What are the key principles of efficient warehouse layout design?"',
+            'rag-mini search "How can Just-In-Time inventory reduce carrying costs?"',
+            'rag-mini search "What metrics should be used to measure supply chain performance?"',
+            'rag-mini search "How can automation improve warehouse picking accuracy?"',
+            'rag-mini search "What strategies reduce supply chain disruption risks?"'
+        ],
+        "advanced_commands": [
+            'rag-mini find-function "inventory_optimization"',
+            'rag-mini find-class "SupplyChainMetrics"'
+        ],
+        "professional_impact": [
+            "How much cost savings could these optimizations provide?",
+            "Could this help reduce inventory carrying costs and waste?",
+            "What would be the value for training logistics coordinators?"
+        ]
+    },
+
+    "04-financial-compliance": {
+        "title": "Financial Services - Regulatory Intelligence Hub",
+        "task": "Build a smart financial compliance assistant that navigates complex SEC and FINRA regulations",
+        "description": "You're a compliance officer drowning in ever-changing financial regulations. You'll use FSS-Mini-RAG to create an intelligent regulatory system that can instantly answer any compliance question and keep you ahead of regulatory changes.",
+        "folder": "financial-regulations-docs", 
+        "system_name": "Financial Compliance Intelligence Hub",
+        "commands": [
+            'rag-mini search "What are the reporting requirements for Form ADV updates?"',
+            'rag-mini search "How often must AML policies be reviewed and updated?"',
+            'rag-mini search "What cybersecurity measures are required for client data protection?"',
+            'rag-mini search "What documentation is required for demonstrating fiduciary duty?"',
+            'rag-mini search "What are the penalties for non-compliance with SEC regulations?"'
+        ],
+        "advanced_commands": [
+            'rag-mini find-function "compliance_check"',
+            'rag-mini find-class "RegulatoryRequirement"'
+        ],
+        "professional_impact": [
+            "How much time would this save during compliance reviews?",
+            "Could this help avoid costly regulatory violations?",
+            "What would be the value for training compliance staff?"
+        ]
+    },
+
+    "05-medical-research": {
+        "title": "Medical Research - Clinical Trial Intelligence System",
+        "task": "Build a smart clinical research assistant that navigates FDA regulations and GCP guidelines",
+        "description": "You're coordinating clinical trials and struggling with complex FDA requirements and GCP guidelines. You'll use FSS-Mini-RAG to create an intelligent research system that can instantly answer any protocol, safety, or regulatory question.",
+        "folder": "clinical-research-docs",
+        "system_name": "Clinical Trial Intelligence System", 
+        "commands": [
+            'rag-mini search "What are the FDA requirements for Phase II trial design?"',
+            'rag-mini search "How should adverse events be classified and reported?"',
+            'rag-mini search "What statistical power calculations are needed for efficacy endpoints?"',
+            'rag-mini search "What informed consent elements are required?"',
+            'rag-mini search "How should patient eligibility criteria be defined?"'
+        ],
+        "advanced_commands": [
+            'rag-mini find-function "adverse_event_report"',
+            'rag-mini find-class "TrialProtocol"'
+        ],
+        "professional_impact": [
+            "How much time would this save during protocol development?",
+            "Could this help ensure FDA compliance and patient safety?",
+            "What would be the value for training clinical research coordinators?"
+        ]
+    },
+
+    # Add more scenarios here...
+}
+
+def create_functional_instructions(scenario_id, enhancement):
+    """Create functional instructions with comprehensive command testing."""
+    
+    instructions = f"""# Test Scenario {scenario_id.split('-')[0].zfill(2)}: {enhancement['title']}
+
+## 🏢 **Industry Context**: {enhancement['title'].split(' - ')[0]}
+**Role**: {get_role_from_title(enhancement['title'])}  
+**Task**: {enhancement['task']}
+
+## 📋 **Scenario Description**
+{enhancement['description']}
+
+## 🎯 **Your Mission (Completely Autonomous)**
+
+### **Step 1: Setup FSS-Mini-RAG**
+1. Read the repository README.md to understand how to install FSS-Mini-RAG
+2. Follow the installation instructions for your platform
+3. Verify the installation works by running `rag-mini --help`
+
+### **Step 2: Gather Industry Documentation**
+Create a folder called `{enhancement['folder']}` and populate it with relevant documentation:
+{get_materials_list(scenario_id)}
+
+**Pro tip**: Look for PDF documents, technical guides, and industry best practices.
+
+### **Step 3: Build Your Intelligent Knowledge Base**
+```bash
+# Navigate to your research folder
+cd {enhancement['folder']}
+
+# Initialize FSS-Mini-RAG index  
+rag-mini init
+
+# Check the index was created successfully
+rag-mini stats
+
+# Get system info to verify everything is working
+rag-mini info
+```
+
+### **Step 4: Test Your Intelligence System**
+Now for the cool part - your documentation is now searchable with natural language! Test these queries:
+
+```bash"""
+
+    # Add search commands
+    for cmd in enhancement['commands']:
+        instructions += f"\n# {get_command_description(cmd)}\n{cmd}\n"
+
+    instructions += f"""```
+
+### **Step 5: Advanced Searches**
+Try these more sophisticated queries:
+
+```bash"""
+
+    # Add advanced commands
+    for cmd in enhancement['advanced_commands']:
+        instructions += f"\n# {get_advanced_description(cmd)}\n{cmd}\n"
+
+    instructions += f"""
+# Update your knowledge base (when you add new documents)
+rag-mini update
+
+# Get detailed statistics about your knowledge base
+rag-mini stats
+```
+
+### **Step 6: Document Your Intelligence System**
+Write your findings in `RESULTS.md` including:
+
+#### **Knowledge Base Performance**
+- How many documents were indexed?
+- How fast were the search responses?
+- Which types of questions worked best?
+
+#### **Professional Value** 
+{get_professional_questions(enhancement['professional_impact'])}
+
+#### **Professional Impact**
+{get_impact_questions(enhancement['professional_impact'])}
+
+{get_completion_workflow(scenario_id, enhancement)}
+
+## 📁 **Final Deliverables**
+- `{enhancement['folder']}/` folder with indexed documentation
+- `RESULTS.md` with comprehensive evaluation and evidence
+- Git branch with proper commit history
+- Pull request with detailed description
+- Professional assessment of FSS-Mini-RAG effectiveness
+
+## ⏱️ **Expected Duration**: 3-4 hours (including documentation and PR submission)
+
+## 🎉 **Success Outcome**
+You'll have created an **intelligent {enhancement['system_name'].lower()}** AND provided valuable feedback to improve FSS-Mini-RAG for industry professionals!
+
+## 🎓 **Learning Objectives**
+- Experience semantic search with industry-specific content
+- Evaluate AI-powered documentation assistance for professional workflows  
+- Test real-world applicability of RAG systems in your industry
+- Practice professional software evaluation and contribution workflows"""
+
+    return instructions
+
+def get_role_from_title(title):
+    """Extract role from title."""
+    roles = {
+        "Childcare": "Childcare Center Director",
+        "Plant": "Logistics Coordinator", 
+        "Financial": "Compliance Officer",
+        "Medical": "Clinical Research Coordinator",
+    }
+    
+    for key, role in roles.items():
+        if key in title:
+            return role
+    return "Professional"
+
+def get_materials_list(scenario_id):
+    """Get materials list based on scenario.""" 
+    # This would be customized per scenario
+    return "- Relevant industry documentation\n- Standards and guidelines\n- Best practices documents\n- Regulatory requirements\n- Technical specifications"
+
+def get_command_description(cmd):
+    """Get description for search command."""
+    return f"Search for specific information"
+
+def get_advanced_description(cmd):
+    """Get description for advanced command."""
+    return f"Advanced search functionality"
+
+def get_professional_questions(impact_list):
+    """Format professional impact questions."""
+    return "\n".join([f"- {q}" for q in impact_list])
+
+def get_impact_questions(impact_list):
+    """Get impact assessment questions."""
+    return "\n".join([f"- {q}" for q in impact_list])
+
+def get_completion_workflow(scenario_id, enhancement):
+    """Get the completion workflow with repository details."""
+    
+    scenario_name = scenario_id.replace('-', '_')
+    
+    return f"""
+### **Step 7: Complete Professional Evaluation**
+Rate FSS-Mini-RAG's effectiveness for:
+- **Finding specific industry information** (1-10)
+- **Answering domain-specific questions** (1-10)  
+- **Helping with workflow optimization** (1-10)
+- **Overall usefulness for your industry** (1-10)
+
+### **Step 8: Document Your Experience**
+Create a comprehensive `RESULTS.md` including:
+
+#### **Executive Summary**
+- What you built ({enhancement['system_name']})
+- Key findings and success metrics
+- Professional impact assessment
+
+#### **Technical Details**
+- Number of documents indexed and file sizes
+- Search response times and accuracy ratings
+- Most effective query types and examples
+- Command usage statistics (init, search, stats, info, find-function, find-class, update)
+
+#### **Professional Value Assessment**
+- Time saved compared to manual document searching
+- Potential impact on industry-specific processes
+- Training value for new professionals
+- Industry-specific compliance/workflow improvements
+
+#### **User Experience Report**
+- Installation process evaluation
+- Command usability ratings
+- Documentation quality assessment
+- Suggested improvements or missing features
+
+### **Step 9: Repository Contribution Workflow**
+
+#### **Repository Information**
+- **Repository URL**: `http://192.168.1.3:3000/foxadmin/fss-mini-rag-github.git`
+- **Main Branch**: `main`
+- **Testing Branch**: `agent-user-testing` (where scenarios are located)
+
+#### **Branch Management**
+```bash
+# Clone the repository
+git clone http://192.168.1.3:3000/foxadmin/fss-mini-rag-github.git
+cd fss-mini-rag-github
+
+# Start from the agent-user-testing branch
+git checkout agent-user-testing
+
+# Create your own branch for your results
+git checkout -b agent-test-{scenario_name}-$(date +%Y%m%d)
+
+# Navigate to your scenario
+cd agent-user-testing/{scenario_id}/
+```
+
+#### **Submit Your Results**
+```bash
+# Add your completed RESULTS.md
+git add RESULTS.md
+
+# Commit with descriptive message
+git commit -m "Agent Test Results: {enhancement['system_name']}
+
+- Tested FSS-Mini-RAG with industry-specific documentation
+- Created intelligent knowledge base for domain queries
+- Evaluated semantic search effectiveness for professional workflows
+- Documented professional impact and time-saving potential
+- Rating: [X]/10 overall effectiveness"
+
+# Push your branch
+git push origin agent-test-{scenario_name}-$(date +%Y%m%d)
+```
+
+#### **Create Pull Request**
+```bash
+# Use gitea CLI to create PR
+gitea prs create "Agent Test: {enhancement['system_name']} Results" agent-test-{scenario_name}-$(date +%Y%m%d) agent-user-testing --body "Completed comprehensive testing of FSS-Mini-RAG for industry workflows.
+
+## Test Summary
+- Built {enhancement['system_name']}
+- Indexed [X] industry documents
+- Tested [X] search queries with [X]% accuracy
+- Overall effectiveness rating: [X]/10
+
+## Key Findings
+[Brief summary of major discoveries]
+
+## Professional Impact
+[Assessment of real-world value for professionals]
+
+## Recommendations
+[Suggestions for improvements or additional features]"
+```
+
+### **Step 10: Validation Requirements**
+
+Your submission must include:
+
+#### **Required Evidence**
+- ✅ **Screenshots** of successful `rag-mini init` and `rag-mini stats` output
+- ✅ **Search examples** with actual query results (at least 5 different searches)
+- ✅ **Performance metrics** (response times, index size, document count)
+- ✅ **Professional assessment** with specific use cases and value propositions
+
+#### **Quality Standards**
+- ✅ **Functional completeness**: All major commands tested (init, search, stats, info)
+- ✅ **Real-world relevance**: Actual industry documents and realistic queries
+- ✅ **Professional writing**: Clear, actionable insights for industry teams
+- ✅ **Quantitative data**: Specific metrics and measurable outcomes
+
+#### **Submission Checklist**
+- [ ] Created intelligent knowledge base successfully
+- [ ] Tested minimum 5 different search queries
+- [ ] Documented all command usage and results
+- [ ] Provided professional impact assessment
+- [ ] Created proper git branch with descriptive name
+- [ ] Submitted PR with comprehensive description
+- [ ] Included evidence screenshots/outputs
+- [ ] Met all validation requirements"""
+
+def main():
+    """Generate enhanced instructions for key scenarios."""
+    
+    print("Enhancing agent testing scenarios with functional demonstrations...")
+    
+    for scenario_id, enhancement in scenario_enhancements.items():
+        scenario_dir = Path(f"agent-user-testing/{scenario_id}")
+        if scenario_dir.exists():
+            print(f"Enhancing scenario: {scenario_id}")
+            
+            instructions_file = scenario_dir / "INSTRUCTIONS.md"
+            instructions_content = create_functional_instructions(scenario_id, enhancement)
+            
+            with open(instructions_file, 'w') as f:
+                f.write(instructions_content)
+            
+            print(f"  ✅ Updated {instructions_file}")
+        else:
+            print(f"  ⚠️  Scenario directory not found: {scenario_dir}")
+    
+    print(f"\\nEnhanced {len(scenario_enhancements)} scenarios with functional demonstrations!")
+
+if __name__ == "__main__":
+    main()