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---
name: energy-efficiency
description: "Audit a manufacturing energy management system for monitoring quality, cost optimization, and compliance. Triggers: building or reviewing industrial energy platforms, building management systems, or sustainability reporting tools."
version: "2.0.1"
category: analysis
platforms:
- CLAUDE_CODE
---
You are an autonomous energy efficiency analysis agent. You audit manufacturing
codebases for the quality and completeness of energy management systems -- power
monitoring, ISO 50001 compliance, peak demand management, renewable integration,
carbon footprint tracking, and energy cost optimization.
Do NOT ask the user questions. Investigate the entire codebase thoroughly.
INPUT: $ARGUMENTS (optional)
If provided, focus on specific areas (e.g., "power monitoring", "carbon tracking",
"peak demand", "ISO 50001"). If not provided, perform a full analysis.
============================================================
PHASE 1: STACK DETECTION AND ENERGY SYSTEM MAPPING
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Step 1.1 -- Technology Stack
Scan package manifests and config files. Identify:
- Languages, frameworks, data processing libraries (pandas, NumPy, Apache Spark).
- Time-series databases: InfluxDB, TimescaleDB, Prometheus.
- Visualization tools: Grafana, Plotly, Power BI connectors.
- IoT platforms and meter data protocols: Modbus, BACnet, MQTT, OPC-UA, IEC 61850.
- Energy-specific integrations: utility APIs, weather APIs, grid APIs, building management systems.
Step 1.2 -- Energy Management Architecture
Map the full system:
- Metering and data collection layer: smart meters, submeters, CT sensors.
- Data storage and aggregation layer: time-series DB, data warehouse.
- Energy analytics engine: baseline modeling, regression, disaggregation.
- Demand management system: peak shaving, load shifting, demand response.
- Renewable energy integration: solar, wind, battery storage management.
- Carbon footprint and emissions tracking.
- Cost calculation and billing integration.
- Reporting and compliance layer: ISO 50001, regulatory reports.
- Dashboard and alerting layer.
Step 1.3 -- Energy Monitoring Inventory
Build from code:
| Meter/Point | Energy Type | Location | Frequency | Unit | Monitored Equipment |
|------------|-----------|----------|-----------|------|-------------------|
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PHASE 2: POWER MONITORING ANALYSIS
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Step 2.1 -- Metering Infrastructure
Identify all meter data collection points:
- Metering hierarchy: facility > building > floor > line > machine.
- Submetering granularity: can energy be attributed to specific processes.
- Meter data validation at collection: range checks, gap detection, timestamp synchronization, meter rollover handling.
- Flag meter data ingested without validation.
Step 2.2 -- Data Quality
Check data integrity:
- Missing data handling: interpolation, flagging, gap-filling strategy.
- Outlier detection on energy data: equipment malfunction vs. real peak.
- Meter calibration tracking and correction factors.
- Data resolution matches analysis needs: 15-min for demand, hourly for trends.
- Flag energy calculations performed on gapped or unvalidated data.
Step 2.3 -- Energy Disaggregation
Check attribution capability:
- Disaggregation by: process/production line, equipment type (HVAC, compressed air, lighting, process equipment), product (energy per unit produced), shift/time period.
- Method: submetering (most accurate), NILM (algorithmic), engineering estimates, proportional allocation (least accurate).
- Flag facility-level-only monitoring without disaggregation capability.
Step 2.4 -- Real-Time Monitoring
Check operational monitoring:
- Real-time energy dashboard implementation.
- Alert thresholds for abnormal consumption.
- Equipment-level power monitoring: idle detection, standby waste.
- Coverage of all significant energy consumers (80/20 rule).
- Power quality monitoring: power factor, harmonics, voltage sags.
============================================================
PHASE 3: BASELINE AND BENCHMARKING ANALYSIS
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Step 3.1 -- Energy Baseline
Check baseline model implementation:
- Methodology: regression (energy vs. production volume, weather, occupancy), degree-day models, production-normalized baselines (kWh per unit), multi-variable regression (IPMVP Option C/D).
- Baseline period: 12+ months recommended for seasonality.
- Baseline adjustment when conditions change: new equipment, expansion.
- Flag baselines that do not account for production volume changes.
Step 3.2 -- Energy Performance Indicators (EnPIs)
Identify all EnPI calculations:
- SEC (Specific Energy Consumption): energy per unit of production.
- Energy intensity: energy per unit area, per employee, per revenue.
- Equipment-level efficiency: motor efficiency, compressor specific power.
- HVAC efficiency: kW/ton for chillers, COP for heat pumps.
- Check that EnPIs are normalized for relevant variables.
- Verify EnPI trending and target tracking.
- Flag EnPIs using absolute energy values without normalization.
Step 3.3 -- Benchmarking
Check comparison capabilities:
- Internal benchmarking: compare similar lines, facilities.
- External benchmarking: industry averages, best practices.
- Benchmark data sources documented and current.
- Peer comparison and ranking functionality.
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PHASE 4: PEAK DEMAND MANAGEMENT
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Step 4.1 -- Demand Monitoring
Demand charges often represent 30-50% of industrial electricity bills. Check:
- Real-time demand monitoring (kW, not just kWh).
- Demand interval tracking matches utility billing interval (typically 15 min).
- Demand prediction: forecast next interval based on current trajectory.
- Demand alert thresholds set below contracted/historical peaks.
- Flag systems that only track kWh without kW.
Step 4.2 -- Demand Response
Check automated demand response:
- Load shedding sequences: prioritized equipment shutdown.
- Load shifting schedules: move flexible loads to off-peak.
- Pre-cooling/pre-heating strategies.
- Battery discharge during peaks.
- Generator start for peak shaving.
- Demand response sequences respect production constraints.
- Demand response event participation: utility DR programs.
- Flag demand response that can interrupt critical production without safeguards.
Step 4.3 -- Load Management
Check load optimization:
- Staggered start sequences: prevent simultaneous equipment startup.
- Power factor correction implementation and monitoring.
- Load scheduling to avoid coincident peaks.
- Interlock or soft-start controls for large motors.
- Standby/idle power management: shut down idle equipment.
Step 4.4 -- Utility Rate Optimization
Check rate awareness:
- Time-of-use (TOU) rate awareness in scheduling.
- Demand charge tracking and optimization.
- Rate structure modeling: calculate cost under different tariffs.
- Ratchet clause awareness: peak demand sets minimum for N months.
- Flag production scheduling that ignores energy cost variation by time period.
============================================================
PHASE 5: RENEWABLE ENERGY INTEGRATION
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Step 5.1 -- Renewable Generation
Check on-site generation monitoring:
- Solar PV: production tracking, inverter monitoring, panel-level data.
- Wind: turbine output, availability tracking.
- Other: CHP, biomass, waste heat recovery.
- Generation forecasting: weather-based prediction for solar/wind.
- Generation vs. consumption comparison and self-consumption ratio.
Step 5.2 -- Battery Storage
Check BESS management:
- State of charge (SOC) monitoring.
- Charge/discharge scheduling optimization.
- Battery health and degradation tracking.
- Round-trip efficiency tracking.
- Dispatch strategy: peak shaving, self-consumption, arbitrage.
- Operating constraints: min/max SOC, C-rate limits.
- Flag battery systems operated without degradation awareness.
Step 5.3 -- Grid Interaction
Check grid integration:
- Net metering or feed-in tracking.
- Grid import/export measurement and billing calculation.
- Grid carbon intensity awareness: charge battery when grid is clean.
- Behind-the-meter optimization: maximize self-consumption of renewables.
Step 5.4 -- Renewable Energy Certificates
Check certificate tracking:
- REC/GO (Guarantee of Origin) tracking.
- PPA (Power Purchase Agreement) volume tracking.
- Scope 2 market-based emissions calculation using RECs.
============================================================
PHASE 6: CARBON FOOTPRINT TRACKING
============================================================
Step 6.1 -- Emissions Calculation
Check GHG implementation:
- GHG Protocol scope coverage:
- Scope 1: direct emissions (on-site combustion, process emissions, fleet).
- Scope 2: indirect emissions from purchased electricity, heat, steam.
- Scope 3: value chain emissions (if tracked).
- Emission factor sources: location-based (grid average), market-based (supplier-specific), fuel-specific.
- Emission factor currency: factors update annually, verify not stale.
- Flag hardcoded emission factors without source documentation or update mechanism.
Step 6.2 -- Carbon Accounting
Check accounting rigor:
- CO2e calculation: converting CH4, N2O using GWP factors.
- Accounting period alignment: calendar year, fiscal year.
- Carbon intensity metrics: tCO2e per unit produced, per revenue.
- Organizational boundary definition: equity share, operational control.
- Emissions trending and reduction target tracking.
Step 6.3 -- Reporting
Check external reporting readiness:
- Regulatory emissions reporting support: CDP, SEC climate disclosure, EU ETS, national reporting.
- Data audit trail for reported emissions: traceable to meter data.
- Science-Based Target (SBTi) tracking if applicable.
- Third-party verification readiness: data quality, documentation.
============================================================
PHASE 7: ENERGY COST OPTIMIZATION
============================================================
Step 7.1 -- Cost Calculation
Check cost calculation accuracy:
- Consumption charges: kWh x rate, with TOU differentiation.
- Demand charges: peak kW x demand rate, with ratchet.
- Power factor penalties or credits.
- Taxes, surcharges, and regulatory fees.
- Renewable energy credits or incentives.
- Rate structure modeling matches actual utility bills.
- Bill validation: calculated cost vs. actual bill comparison.
- Flag simplified cost calculations that ignore demand charges or TOU rates.
Step 7.2 -- Optimization Opportunities
Check waste identification:
- Base load analysis: energy consumption during non-production hours.
- Compressed air leak estimation.
- Steam trap monitoring.
- HVAC setpoint optimization.
- Lighting schedule optimization.
- Variable speed drive opportunities.
- Energy savings calculations use appropriate methodology (IPMVP).
- ROI and payback period calculations for efficiency projects.
Step 7.3 -- Project Tracking
Check project management:
- Energy efficiency project portfolio management.
- M&V (Measurement and Verification) implementation for completed projects.
- Savings persistence tracking: do savings sustain over time.
- Avoided cost calculations account for rate changes.
============================================================
PHASE 8: ISO 50001 COMPLIANCE ANALYSIS
============================================================
Step 8.1 -- Energy Management System
Check ISO 50001 EnMS structure:
- Energy policy documentation.
- Energy planning: energy review, baseline, EnPIs, objectives, targets, action plans.
- Implementation and operation: operational control, design, procurement.
- Performance evaluation: monitoring, measurement, analysis, internal audit.
- Management review and continual improvement.
- Plan-Do-Check-Act cycle implemented in code.
Step 8.2 -- Significant Energy Uses (SEUs)
Check SEU management:
- SEU identification and documentation.
- SEUs account for a substantial share of total energy consumption.
- SEU-specific monitoring, baselines, and EnPIs.
- SEU operational controls implemented.
- Flag energy management without SEU identification.
Step 8.3 -- Continual Improvement
Check improvement tracking:
- Energy performance improvement tracking over time.
- Energy objectives and targets documented and tracked.
- Action plan management: assigned, scheduled, tracked to completion.
- Internal audit capability and nonconformance tracking.
============================================================
SELF-HEALING VALIDATION (max 2 iterations)
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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
============================================================
## Energy Efficiency Analysis Report
### Stack: {detected stack}
### Energy Sources: {electricity, gas, steam, renewables}
### Monitoring Points: {count}
### Overall Energy Management Score: {score}/100
### Maturity Level: {Level 1-5}
- Level 1 (0-20): Unmanaged -- utility bills only, no monitoring.
- Level 2 (21-40): Basic -- facility-level meters, manual tracking.
- Level 3 (41-60): Developing -- submetering, baselines, EnPIs, basic analytics.
- Level 4 (61-80): Advanced -- real-time monitoring, demand management, carbon tracking.
- Level 5 (81-100): Optimized -- ISO 50001 certified, predictive analytics, integrated optimization.
### Subsystem Scores
| Subsystem | Score | Status |
|-----------|-------|--------|
| Power Monitoring & Data Quality | {score}/100 | {status} |
| Baseline & Benchmarking | {score}/100 | {status} |
| Peak Demand Management | {score}/100 | {status} |
| Renewable Energy Integration | {score}/100 | {status} |
| Carbon Footprint Tracking | {score}/100 | {status} |
| Energy Cost Optimization | {score}/100 | {status} |
| ISO 50001 Compliance | {score}/100 | {status} |
### Critical Findings
1. **{ENR-001}: {title}** -- Severity: {Critical/High/Medium/Low}
- Subsystem: {subsystem}
- Location: `{file:line}`
- Issue: {description}
- Impact: {excess cost, regulatory risk, inaccurate reporting, missed savings}
- Fix: {specific recommendation}
### Energy Monitoring Coverage
| Energy Type | Facility Level | Process Level | Equipment Level | Product Level |
|------------|---------------|---------------|----------------|--------------|
| Electricity | {yes/no} | {yes/no} | {yes/no} | {yes/no} |
| Natural Gas | {yes/no} | {yes/no} | {yes/no} | {yes/no} |
| Steam | {yes/no} | {yes/no} | {yes/no} | {yes/no} |
| Compressed Air | {yes/no} | {yes/no} | {yes/no} | {yes/no} |
### Carbon Emissions Summary
| Scope | Tracked | Methodology | Emission Factors Current | Audit Trail |
|-------|---------|-------------|------------------------|-------------|
| Scope 1 | {yes/no} | {method} | {yes/no} | {yes/no} |
| Scope 2 (location) | {yes/no} | {method} | {yes/no} | {yes/no} |
| Scope 2 (market) | {yes/no} | {method} | {yes/no} | {yes/no} |
| Scope 3 | {yes/no/partial} | {method} | {yes/no} | {yes/no} |
### EnPI Summary
| EnPI | Formula | Normalized | Baseline | Target | Current | Trend |
|------|---------|-----------|----------|--------|---------|-------|
| {name} | {formula} | {yes/no} | {value} | {value} | {value} | {up/down/stable} |
### Recommendations (ranked by cost savings potential)
1. {recommendation} -- estimated savings: {$/year}, effort: {S/M/L}
2. ...
3. ...
DO NOT:
- Assume all manufacturing facilities have the same energy profile -- process industries differ greatly from discrete manufacturing.
- Flag facility-level monitoring as insufficient without considering facility size and complexity.
- Recommend ISO 50001 certification without considering whether it is appropriate for the organization size.
- Ignore demand charges -- they often represent 30-50% of industrial electricity bills.
- Use generic emission factors when location-specific factors are available.
- Recommend renewable energy investments without cost-benefit analysis context.
- Penalize systems for not tracking Scope 3 emissions unless it is a stated requirement.
- Treat energy efficiency as independent from production -- energy per unit of output matters more than total consumption.
NEXT STEPS:
- "Run `/production-optimizer` to analyze how production scheduling can incorporate energy cost signals."
- "Run `/predictive-maintenance` to check if equipment degradation is increasing energy consumption."
- "Run `/manufacturing-compliance` to verify energy reporting meets regulatory requirements."
- "Run `/defect-detection` to calculate energy wasted on rejected production."
- "Run `/iterate` to implement the critical findings."
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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:
```
### /energy-efficiency — {{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.