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Domain Expert Model - Validation Report

v2/docs/reasoningbank/models/domain-expert/validation-report.md

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Domain Expert Model - Validation Report

Validation Date: 2025-10-15T02:55:59.272Z Database: /workspaces/claude-code-flow/docs/reasoningbank/models/domain-expert/memory.db

✅ Validation Results

  • Total patterns (1500)
  • Equal domain distribution
  • High confidence (>80%)
  • High success rate (>75%)
  • Pattern links (>2000)
  • Full embedding coverage (100%)
  • Efficient storage (<12 MB)

Overall Status: ✅ PASSED

📊 Statistics

Pattern Distribution

  • API Design & Integration: 300 patterns
  • Data Engineering & ML: 300 patterns
  • DevOps & Infrastructure: 300 patterns
  • Performance & Scalability: 300 patterns
  • Security & Compliance: 300 patterns

Quality Metrics

  • Total Patterns: 1500
  • Average Confidence: 89.4%
  • Average Success Rate: 88.5%
  • Confidence Range: 81.0% - 94.3%
  • Total Links: 7500
  • Unique Sources: 1500
  • Unique Targets: 1428
  • enhances: 7140 links
  • requires: 360 links

Embeddings

  • Total Embeddings: 1500
  • Coverage: 100.0%

Storage Efficiency

  • Database Size: 2.39 MB
  • Per Pattern: 1.63 KB

🎯 Model Capabilities

The Domain Expert model provides:

  1. Multi-Domain Expertise: 5 domains with 300 patterns each
  2. High Confidence: 89.4% average expert consensus
  3. Proven Success: 88.5% average production success rate
  4. Rich Context: 7500 cross-domain pattern links
  5. Semantic Search: Full embedding coverage for similarity queries

📝 Usage Examples

Query DevOps patterns

bash
npx claude-flow@alpha memory search "kubernetes autoscaling" \
  --namespace domain-expert --reasoningbank --limit 5

Query Security patterns

bash
npx claude-flow@alpha memory search "OAuth 2.0 security" \
  --namespace domain-expert --reasoningbank --limit 5

Query Performance patterns

bash
npx claude-flow@alpha memory search "database query optimization" \
  --namespace domain-expert --reasoningbank --limit 5

🚀 Next Steps

  1. Test semantic search with domain-specific queries
  2. Integrate with agentic-flow agents
  3. Benchmark query performance
  4. Collect feedback for model improvements

Report Generated: 2025-10-15T02:55:59.272Z