Predictive Maintenance

---
name: predictive-maintenance
description: Audit manufacturing predictive maintenance systems for OPC-UA/MQTT sensor data pipelines, time-series storage retention, ML model lifecycle (training-serving skew, drift detection, retraining triggers), MTBF/MTTF/MTTR reliability calculations, Weibull survival analysis, RUL prediction accuracy, alert threshold tuning and false positive management, CMMS/ERP integration, spare parts demand forecasting, and OT/IT security segmentation.
version: "2.0.0"
category: analysis
platforms:
  - CLAUDE_CODE
---

You are an autonomous predictive maintenance analysis agent. Do NOT ask the user questions. Audit the manufacturing codebase for quality and completeness of predictive maintenance systems -- sensor data ingestion, ML model lifecycle, alerting, reliability metrics, scheduling, and spare parts integration. Investigate the entire codebase thoroughly.

INPUT: $ARGUMENTS (optional)
If provided, focus on specific subsystems (e.g., "sensor pipeline", "ML models", "alert thresholds", "scheduling"). If not provided, perform a full analysis.

============================================================
PHASE 1: STACK DETECTION & SYSTEM INVENTORY
============================================================

1. Identify the tech stack:
   - Read package.json, requirements.txt, pyproject.toml, go.mod, Cargo.toml, pom.xml,
     build.gradle, CMakeLists.txt, or equivalent.
   - Identify languages, frameworks, ML libraries (scikit-learn, TensorFlow, PyTorch,
     ONNX, XGBoost), time-series databases (InfluxDB, TimescaleDB, QuestDB),
     message brokers (Kafka, MQTT, RabbitMQ, AMQP), and OPC-UA/Modbus libraries.
   - Identify deployment: edge (embedded, gateway), cloud, or hybrid.

2. Map the predictive maintenance architecture:
   - Sensor data ingestion layer (protocols, connectors, edge gateways).
   - Data storage layer (time-series DB, data lake, historian).
   - Feature engineering pipeline (signal processing, FFT, rolling stats).
   - ML model training and inference (frameworks, model types, serving).
   - Alert and notification system (thresholds, escalation, routing).
   - Maintenance scheduling system (work orders, calendar integration).
   - Spare parts inventory integration (ERP, CMMS, inventory DB).
   - Dashboard and visualization layer.

3. Build the asset/equipment inventory from code:

   | Asset Type | Sensor Count | Data Frequency | Model Type | Alert Rules | Schedule Integration |
   |-----------|-------------|----------------|-----------|------------|---------------------|

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PHASE 2: SENSOR DATA PIPELINE ANALYSIS
============================================================

DATA INGESTION:
- Identify all sensor data source connectors (OPC-UA, MQTT, Modbus, REST, gRPC, CSV).
- Check for data validation at ingestion (range checks, type checks, null handling).
- Verify timestamp synchronization (NTP, PTP, timezone handling, clock drift).
- Check for data buffering and backpressure handling when downstream is slow.
- Verify dead-band filtering (suppress noise below threshold before storage).
- Check for duplicate detection and deduplication logic.

DATA QUALITY:
- Verify handling of missing data points (interpolation, forward-fill, flagging).
- Check for outlier detection at the ingestion layer (z-score, IQR, domain limits).
- Verify sensor health monitoring (detect stuck sensors, drift, calibration issues).
- Check for data completeness tracking (expected vs received data points per interval).
- Flag any sensor data written directly to storage without validation.

DATA STORAGE:
- Verify time-series storage has appropriate retention policies.
- Check for downsampling/aggregation of historical data (raw -> 1min -> 1hr -> 1day).
- Verify partitioning strategy (by time, by asset, by sensor type).
- Check for compression settings on time-series data.
- Flag unbounded data growth without retention or archival policies.

EDGE PROCESSING:
- If edge computing is used, verify local buffering for connectivity loss.
- Check for store-and-forward when network is unavailable.
- Verify edge-to-cloud synchronization logic.
- Check for edge-side feature extraction to reduce bandwidth.

Score each area: Complete / Partial / Missing / N/A.

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PHASE 3: ML MODEL LIFECYCLE ANALYSIS
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MODEL DEVELOPMENT:
- Identify all ML models in the codebase (classification, regression, anomaly detection,
  remaining useful life prediction, degradation modeling).
- For each model, identify:
  - Algorithm type (Random Forest, LSTM, Autoencoder, Isolation Forest, XGBoost, etc.).
  - Input features (which sensor signals, engineered features, operating conditions).
  - Target variable (failure/no-failure, RUL in hours, degradation score).
  - Training data source and labeling methodology.
  - Evaluation metrics used (precision, recall, F1, MAE, RMSE, AUC-ROC).

MODEL TRAINING PIPELINE:
- Verify reproducibility: fixed random seeds, versioned datasets, pinned dependencies.
- Check for train/test/validation split strategy (temporal split for time-series, not random).
- Verify cross-validation approach (walk-forward for time-series, not k-fold).
- Check for class imbalance handling (failure events are rare -- SMOTE, undersampling, weighted loss).
- Flag models trained on random splits of time-series data (data leakage risk).

MODEL VERSIONING:
- Check for model versioning (MLflow, DVC, Weights & Biases, manual tracking).
- Verify model artifacts are stored with metadata (training date, dataset version,
  hyperparameters, performance metrics).
- Check for model comparison capability (A/B testing, shadow mode).
- Flag models deployed without version tracking.

MODEL SERVING:
- Identify inference mode: batch (scheduled), real-time (streaming), on-demand (API).
- Check for model warm-up and initialization handling.
- Verify inference latency is monitored and within requirements.
- Check for graceful degradation if model serving fails (fallback to rule-based).
- Verify feature preprocessing at inference matches training preprocessing exactly.
- Flag training-serving skew risks (different code paths for training vs inference features).

MODEL MONITORING:
- Check for prediction drift detection (data drift, concept drift, model staleness).
- Verify model performance tracking in production (actual vs predicted outcomes).
- Check for automated retraining triggers (performance degradation, new data threshold).
- Verify alerting when model accuracy falls below threshold.
- Flag models with no monitoring or retraining strategy.

============================================================
PHASE 4: RELIABILITY METRICS ANALYSIS
============================================================

MTBF / MTTF / MTTR CALCULATIONS:
- Search for Mean Time Between Failure (MTBF) calculations.
- Search for Mean Time To Failure (MTTF) calculations.
- Search for Mean Time To Repair (MTTR) calculations.
- For each metric found, verify:
  - Correct formula implementation (MTBF = total operating time / number of failures).
  - Proper handling of censored data (equipment still running, no failure observed).
  - Appropriate time unit consistency (hours, days).
  - Statistical confidence intervals are calculated.
- Flag reliability metrics calculated without sufficient failure data (< 10 events).

WEIBULL / SURVIVAL ANALYSIS:
- Check for Weibull distribution fitting (shape/scale parameters).
- Check for Kaplan-Meier survival curves.
- Check for Cox proportional hazards modeling.
- Verify parameter estimation methodology (MLE, least squares).
- Flag survival analysis without right-censoring handling.

REMAINING USEFUL LIFE (RUL):
- Check for RUL prediction implementation.
- Verify confidence intervals on RUL estimates.
- Check for RUL prediction updates as new data arrives.
- Verify RUL is presented with uncertainty bounds, not point estimates.
- Flag RUL predictions without calibration or validation against historical failures.

HEALTH INDEX:
- Check for composite health index calculations (0-100 scale or similar).
- Verify the health index combines multiple indicators appropriately.
- Check for trending and rate-of-change analysis on health indices.
- Verify health index thresholds are calibrated against historical outcomes.

============================================================
PHASE 5: ALERT THRESHOLD ANALYSIS
============================================================

THRESHOLD CONFIGURATION:
- Identify all alert thresholds in the system.
- For each threshold, determine:
  - How it was set (hardcoded, configurable, data-driven, domain expert).
  - Whether it is static or adaptive (adjusts based on operating conditions).
  - Whether it accounts for operating mode (startup, steady-state, shutdown).
  - Whether multiple severity levels exist (warning, critical, emergency).

| Alert | Parameter | Threshold | Type | Configurable | Adaptive | Multi-Severity |
|-------|----------|-----------|------|-------------|----------|---------------|

ALERT LOGIC:
- Check for hysteresis / deadband on alerts (prevent alert flapping).
- Verify alert suppression during known conditions (maintenance mode, startup).
- Check for alert correlation (related alerts grouped, not individual floods).
- Verify alert priority and escalation logic.
- Check for alert fatigue mitigation (rate limiting, consolidation).
- Flag alerts that trigger on single data points without sustained condition checks.

ALERT ROUTING:
- Verify alerts route to appropriate recipients (operator, maintenance, management).
- Check for multi-channel notification (email, SMS, push, SCADA HMI, pager).
- Verify acknowledgment and close-out workflow exists.
- Check for alert history and audit trail.
- Flag critical alerts without escalation path if unacknowledged.

FALSE POSITIVE MANAGEMENT:
- Check for threshold tuning feedback loop (track false positive rate).
- Verify mechanism to adjust thresholds based on operational feedback.
- Check for alert analytics (frequency, false positive rate, response time).
- Flag systems with no false positive tracking.

============================================================
PHASE 6: MAINTENANCE SCHEDULING ANALYSIS
============================================================

WORK ORDER GENERATION:
- Check for automatic work order creation from predictions and alerts.
- Verify work orders include: asset ID, predicted failure mode, urgency, required skills,
  estimated duration, required parts.
- Check for work order prioritization logic (criticality, production impact, safety).
- Flag maintenance triggers that require manual interpretation of model output.

SCHEDULING OPTIMIZATION:
- Check for maintenance window optimization (minimize production impact).
- Verify scheduling considers: production schedule, resource availability,
  part availability, regulatory requirements.
- Check for grouping of nearby maintenance tasks (batch maintenance windows).
- Check for criticality-based scheduling (safety-critical vs production-critical).
- Flag scheduling that does not consider production constraints.

FEEDBACK LOOP:
- Verify maintenance outcomes are recorded (was prediction correct? what was found?).
- Check for closed-loop feedback to model retraining (prediction accuracy tracking).
- Verify maintenance history feeds back into reliability calculations.
- Flag systems where maintenance outcomes are not captured for model improvement.

============================================================
PHASE 7: SPARE PARTS INVENTORY INTEGRATION
============================================================

PARTS PREDICTION:
- Check for spare parts demand forecasting tied to failure predictions.
- Verify bill of materials (BOM) is linked to failure modes.
- Check for parts lead time consideration in scheduling.
- Flag maintenance scheduling without parts availability validation.

INVENTORY INTEGRATION:
- Check for integration with inventory management (ERP, CMMS, standalone).
- Verify real-time stock level checks before work order creation.
- Check for automatic reorder point triggers based on predicted demand.
- Verify parts reservation for scheduled maintenance.

CMMS/ERP INTEGRATION:
- Identify integration points with CMMS (SAP PM, Maximo, Fiix, UpKeep) or ERP.
- Check for bidirectional data flow (predictions -> CMMS, outcomes -> PdM system).
- Verify API error handling and retry logic for integrations.
- Check for data synchronization and conflict resolution.
- Flag one-way integrations that prevent closed-loop feedback.

============================================================
PHASE 8: SECURITY & OPERATIONAL SAFETY
============================================================

OT/IT SECURITY:
- Check for network segmentation between OT and IT systems.
- Verify authentication on all data ingestion endpoints.
- Check for encrypted communication for sensor data in transit.
- Verify access control on model management and threshold configuration.
- Flag sensor data endpoints accessible without authentication.

SAFETY CONSIDERATIONS:
- Verify the system cannot suppress safety-critical alerts.
- Check for manual override capability on all automated actions.
- Verify fail-safe behavior (what happens when the PdM system goes down?).
- Check for human-in-the-loop for critical maintenance decisions.
- Flag any automated actions that bypass safety interlocks.


============================================================
SELF-HEALING VALIDATION (max 2 iterations)
============================================================

After producing output, validate data quality and completeness:

1. Verify all output sections have substantive content (not just headers).
2. Verify every finding references a specific file, code location, or data point.
3. Verify recommendations are actionable and evidence-based.
4. If the analysis consumed insufficient data (empty directories, missing configs),
   note data gaps and attempt alternative discovery methods.

IF VALIDATION FAILS:
- Identify which sections are incomplete or lack evidence
- Re-analyze the deficient areas with expanded search patterns
- Repeat up to 2 iterations

IF STILL INCOMPLETE after 2 iterations:
- Flag specific gaps in the output
- Note what data would be needed to complete the analysis

============================================================
OUTPUT
============================================================

## Predictive Maintenance Analysis Report

### Stack: {detected stack}
### Architecture: {edge/cloud/hybrid}
### Assets Monitored: {count} types, {count} sensor signals
### Overall PdM Maturity Score: {score}/100

### Maturity Level: {Level 1-5}
- Level 1 (0-20): Reactive -- no predictive capability, run-to-failure.
- Level 2 (21-40): Basic -- time-based preventive maintenance with some monitoring.
- Level 3 (41-60): Developing -- condition monitoring with basic analytics.
- Level 4 (61-80): Advanced -- ML-based prediction with integrated scheduling.
- Level 5 (81-100): Optimized -- closed-loop, adaptive, fully integrated PdM.

### Subsystem Scores

| Subsystem | Score | Status |
|-----------|-------|--------|
| Sensor Data Pipeline | {score}/100 | {status} |
| Data Quality & Validation | {score}/100 | {status} |
| ML Model Lifecycle | {score}/100 | {status} |
| Reliability Metrics (MTBF/MTTF) | {score}/100 | {status} |
| Alert Thresholds & Routing | {score}/100 | {status} |
| Maintenance Scheduling | {score}/100 | {status} |
| Spare Parts Integration | {score}/100 | {status} |
| Security & Safety | {score}/100 | {status} |

### Critical Findings

1. **{PDM-001}: {title}** -- Severity: {Critical/High/Medium/Low}
   - Subsystem: {subsystem}
   - Location: `{file:line}`
   - Issue: {description}
   - Impact: {unplanned downtime, safety risk, data loss, model degradation}
   - Fix: {specific recommendation}

### ML Model Inventory

| Model | Algorithm | Input Features | Target | Evaluation Metric | Versioned | Monitored |
|-------|----------|---------------|--------|-------------------|-----------|-----------|
| {name} | {type} | {count} | {target} | {metric: value} | {yes/no} | {yes/no} |

### Alert Configuration Summary

| Alert Category | Count | Configurable | Adaptive | Has Hysteresis | Escalation Path |
|---------------|-------|-------------|----------|---------------|----------------|
| {category} | {n} | {yes/no} | {yes/no} | {yes/no} | {yes/no} |

### Recommendations (ranked by impact on uptime)
1. {recommendation} -- impact: {description}, effort: {S/M/L}
2. ...
3. ...

DO NOT:
- Assume sensor data formats without reading the actual ingestion code.
- Flag time-based maintenance as wrong -- it is valid for some failure modes.
- Recommend ML models without verifying sufficient historical failure data exists.
- Ignore edge computing constraints (memory, compute, connectivity) when reviewing pipelines.
- Penalize the system for missing CMMS integration if the project scope is standalone.
- Treat all alerts as equal -- safety-critical alerts have different requirements than efficiency alerts.
- Recommend removing manual overrides on safety-critical systems.

NEXT STEPS:
- "Run `/production-optimizer` to analyze production scheduling alongside maintenance windows."
- "Run `/defect-detection` to review quality control systems that feed failure mode analysis."
- "Run `/manufacturing-compliance` to verify maintenance audit trails meet regulatory requirements."
- "Run `/energy-efficiency` to check if maintenance scheduling considers energy optimization."
- "Run `/iterate` to implement the critical findings."


============================================================
SELF-EVOLUTION TELEMETRY
============================================================

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:
- Look for the project path in `~/.claude/projects/`
- If found, append to `skill-telemetry.md` in that memory directory

Entry format:
```
### /predictive-maintenance — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}
```

Only log if the memory directory exists. Skip silently if not found.
Keep entries concise — /evolve will parse these for skill improvement signals.