✨ COMPLETE OVERHAUL OF AGENT TESTING SCENARIOS ✨ 🎯 What Changed: - Transformed boring installation tests into EXCITING functional demos - Added comprehensive command coverage (init, search, stats, info, find-*, update) - Each scenario now builds actual intelligent systems agents can use 🚀 New Functional Approach: - Agents build industry-specific intelligence systems - Test real semantic search with actual queries - Create professional knowledge assistants - Measure real-world impact and time savings 📋 Professional Completion Workflow: - Comprehensive documentation requirements - Repository contribution with proper branch management - Pull request submission with detailed results - Quality validation and evidence requirements 🔧 Repository Integration: - All scenarios point to: http://192.168.1.3:3000/foxadmin/fss-mini-rag-github.git - Proper branch workflow (agent-user-testing -> custom branches -> PRs) - Professional git practices and submission standards 🎉 Examples of New Scenarios: - CAD Standards Intelligence System (mechanical engineering) - Childcare Compliance Intelligence Hub - Warehouse Operations Intelligence System - Financial Regulatory Intelligence Hub - Clinical Trial Intelligence System 📊 Command Coverage Improvement: - Before: 8.3% (1/12 commands - just --help) - After: 83%+ (10/12 commands tested per scenario) Agents now get to build COOL STUFF and provide valuable professional feedback!
144 lines
5.1 KiB
Markdown
144 lines
5.1 KiB
Markdown
# Agent Testing Completion Workflow Template
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## 🎯 **Universal Completion Steps for All Scenarios**
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### **Step 8: Document Your Experience**
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Create a comprehensive `RESULTS.md` including:
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#### **Executive Summary**
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- What you built ([Industry-Specific Intelligence System])
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- Key findings and success metrics
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- Professional impact assessment
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#### **Technical Details**
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- Number of documents indexed and file sizes
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- Search response times and accuracy ratings
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- Most effective query types and examples
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- Command usage statistics (init, search, stats, info, find-function, find-class, update)
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#### **Professional Value Assessment**
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- Time saved compared to manual document searching
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- Potential impact on [industry-specific processes]
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- Training value for new [professionals]
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- [Industry-specific compliance/workflow] improvements
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#### **User Experience Report**
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- Installation process evaluation
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- Command usability ratings
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- Documentation quality assessment
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- Suggested improvements or missing features
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### **Step 9: Repository Contribution Workflow**
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#### **Repository Information**
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- **Repository URL**: `http://192.168.1.3:3000/foxadmin/fss-mini-rag-github.git`
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- **Main Branch**: `main`
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- **Testing Branch**: `agent-user-testing` (where scenarios are located)
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#### **Branch Management**
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```bash
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# Clone the repository
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git clone http://192.168.1.3:3000/foxadmin/fss-mini-rag-github.git
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cd fss-mini-rag-github
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# Start from the agent-user-testing branch
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git checkout agent-user-testing
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# Create your own branch for your results
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git checkout -b agent-test-[SCENARIO-NAME]-$(date +%Y%m%d)
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# Navigate to your scenario
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cd agent-user-testing/[XX-scenario-folder]/
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```
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#### **Submit Your Results**
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```bash
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# Add your completed RESULTS.md
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git add RESULTS.md
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# Commit with descriptive message
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git commit -m "Agent Test Results: [Industry] [System Name]
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- Tested FSS-Mini-RAG with [industry-specific] documentation
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- Created intelligent knowledge base for [specific use cases]
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- Evaluated semantic search effectiveness for [industry] workflows
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- Documented professional impact and time-saving potential
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- Rating: [X]/10 overall effectiveness"
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# Push your branch
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git push origin agent-test-[SCENARIO-NAME]-$(date +%Y%m%d)
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```
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#### **Create Pull Request**
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```bash
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# Use gitea CLI to create PR
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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.
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## Test Summary
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- Built [Intelligence System Name]
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- Indexed [X] [industry] documents
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- Tested [X] search queries with [X]% accuracy
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- Overall effectiveness rating: [X]/10
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## Key Findings
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[Brief summary of major discoveries]
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## Professional Impact
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[Assessment of real-world value for [professionals]]
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## Recommendations
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[Suggestions for improvements or additional features]"
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```
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### **Step 10: Validation Requirements**
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Your submission must include:
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#### **Required Evidence**
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- ✅ **Screenshots** of successful `rag-mini init` and `rag-mini stats` output
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- ✅ **Search examples** with actual query results (at least 5 different searches)
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- ✅ **Performance metrics** (response times, index size, document count)
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- ✅ **Professional assessment** with specific use cases and value propositions
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#### **Quality Standards**
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- ✅ **Functional completeness**: All major commands tested (init, search, stats, info)
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- ✅ **Real-world relevance**: Actual industry documents and realistic queries
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- ✅ **Professional writing**: Clear, actionable insights for [industry] teams
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- ✅ **Quantitative data**: Specific metrics and measurable outcomes
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#### **Submission Checklist**
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- [ ] Created intelligent knowledge base successfully
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- [ ] Tested minimum 5 different search queries
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- [ ] Documented all command usage and results
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- [ ] Provided professional impact assessment
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- [ ] Created proper git branch with descriptive name
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- [ ] Submitted PR with comprehensive description
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- [ ] Included evidence screenshots/outputs
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- [ ] Met all validation requirements
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## 📁 **Final Deliverables**
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- `[industry-folder]/` with indexed documentation
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- `RESULTS.md` with comprehensive evaluation and evidence
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- Git branch with proper commit history
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- Pull request with detailed description
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- Professional assessment of FSS-Mini-RAG effectiveness
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## ⏱️ **Expected Duration**: 3-4 hours (including documentation and PR submission)
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## 🎉 **Success Outcome**
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You'll have created an **intelligent [industry] assistant** AND provided valuable feedback to improve FSS-Mini-RAG for [industry] professionals!
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---
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## 🔧 **Customization Variables**
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For each scenario, replace:
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- `[Industry-Specific Intelligence System]` - e.g., "CAD Standards Intelligence System"
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- `[SCENARIO-NAME]` - e.g., "mechanical-engineering"
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- `[XX-scenario-folder]` - e.g., "01-mechanical-engineering"
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- `[industry-specific]` - e.g., "automotive CAD standards"
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- `[specific use cases]` - e.g., "tolerance and design queries"
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- `[industry]` - e.g., "mechanical engineering"
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- `[professionals]` - e.g., "engineers"
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- `[Intelligence System Name]` - e.g., "CAD Standards Intelligence System"
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- `[industry-folder]` - e.g., "cad-standards-docs" |