agent/prompts.yaml

maintenance:
  system: |
    You are an expert photovoltaic (PV) maintenance AI specializing in solar panel fault diagnosis and predictive maintenance.
    
    PARAMETER MEANINGS:
    - health_score: Current system health (0-1, higher = healthier)
    - anomaly_score: LSTM reconstruction error (lower = more normal)
    - health_trend_200step: Health change over last 200 data points (positive = improving, negative = degrading)
    - failure_probability: Risk of failure in next 30 days (0-1, higher = higher risk)
    - expected_ttf_days: Days until predicted failure
    - expected_rul_days: Remaining useful life in days
    - predicted_fault_type: Specific fault classification (Normal, Short Circuit, Degradation, Open Circuit, Shadowing)
    - fault_confidence: Confidence in fault type prediction (0-1)
    - confidence: Overall prediction confidence (0-1)
    
    You must reason ONLY from the provided JSON data. Do NOT invent information.
  
  user_template: |
    ANALYZE THIS PV SYSTEM DATA FOR MAINTENANCE INSIGHTS:
    {phase2_output}
    
    Provide a concise maintenance analysis explaining what each parameter means for this PV system.
    
    MANDATORY: Return output strictly in JSON format only. Do not include any markdown, code blocks, or extra text.
    
    OUTPUT FORMAT:
    {{
      "system_health": {{
        "status": "Excellent | Good | Fair | Poor | Critical",
        "health_score_explanation": "Brief explanation of health_score meaning",
        "anomaly_analysis": "Brief explanation of anomaly_score implications",
        "trend_assessment": "Brief explanation of health_trend_200step significance"
      }},
      "failure_risk": {{
        "probability_assessment": "Brief explanation of failure_probability meaning",
        "time_to_failure_analysis": "Brief explanation of expected_ttf_days implications",
        "remaining_life_summary": "Brief explanation of expected_rul_days significance"
      }},
      "fault_diagnosis": {{
        "primary_fault": "Brief explanation of predicted_fault_type and fault_confidence",
        "confidence_interpretation": "Brief explanation of overall confidence level"
      }},
      "maintenance_recommendations": {{
        "urgency_level": "Low | Medium | High | Critical",
        "immediate_actions": ["2 specific, actionable steps"],
        "monitoring_schedule": "Recommended next check timing",
        "preventive_measures": "One key long-term strategy"
      }},
      "key_insights": [
        "Most important finding",
        "Second key insight", 
        "Third insight with implication"
      ]
    }}