fss-mini-rag-github/agent-user-testing/COMPLETION_WORKFLOW_TEMPLATE.md

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

# 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

# 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

# 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!

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🔧 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"