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Answer natural language questions about NUS campus energy performance, carbon footprint, BCA benchmarks, building comparisons, and cost savings. Use for any question about what the data shows — EUI rankings, carbon totals, savings estimates, calibration summaries.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
Generate per-building PDF calibration reports or the campus-wide summary PDF for the Applied Energy paper. Use when asked to generate a report, write up findings, create a calibration log, or produce paper-ready results.
Generate or refresh the NUS building registry (building_registry.json) by scanning all IDF files. Use when IDF files change, when a new building is added, or when asked about building metadata, floor areas, zone counts, or IDF parameters. Also used for pre-flight registry validation before simulation. Owned by SimulationAgent (Forge).
Run EnergyPlus simulations for NUS campus buildings and parse monthly energy output. Use when SimulationAgent is asked to simulate one or all buildings, check calibration status, or re-run after IDF changes.
Run targeted EnergyPlus intervention simulations for NUS buildings. Patches IDF parameters (cooling setpoint, lighting, equipment loads), re-simulates, and reports actual kWh/carbon savings vs baseline. Use when asked to propose or simulate interventions for a folder or set of buildings.
Format and route NUS energy system notifications to Slack. Two message types only — calibration approval requests and final intervention results. All other pipeline events stay in agent logs.
Run and manage the NUS Energy Assistant Slack bot server (Socket Mode). Handles inbound @Energy_assistant mentions and DMs in Slack, routes questions to QueryAgent, and posts answers in thread. Use when starting/stopping the server, checking its status, or diagnosing Slack query failures.
| name | nus-query |
| description | Answer natural language questions about NUS campus energy performance, carbon footprint, BCA benchmarks, building comparisons, and cost savings. Use for any question about what the data shows — EUI rankings, carbon totals, savings estimates, calibration summaries. |
| metadata | {"openclaw":{"emoji":"🔍","requires":{"bins":["python3"],"env":["NUS_PROJECT_DIR"]}}} |
Any question about campus energy data: "which building is most efficient", "total campus carbon", "how much can we save", "EUI ranking", "which buildings need calibration", "BCA Gold gap", "worst performing building", "what is the campus footprint", "how does FOE6 compare to FOE13", "cost of energy waste"
outputs/*/parsed/*_monthly.csv — simulated monthly kWh per buildingoutputs/*/*_calibration_metrics.json — ASHRAE pass/fail + measured vs simulatedoutputs/*/ground_truth/*_ground_truth.csv — actual meter kWhbuilding_registry.json — floor areas (m²) per buildingRead all available *_monthly.csv files, compute annual kWh, divide by floor_area_m2
from building_registry.json. Rank ascending. Flag BCA tier for each.
Sum annual kWh across all buildings. Multiply by 0.4168 kgCO2e/kWh ÷ 1000 = tCO2e. Also compute per-building contribution and identify the top 3 emitters.
For each building above BCA Gold (115 kWh/m²): savings_kwh = (current_eui - 115) × floor_area_m2 savings_sgd = savings_kwh × 0.28 savings_carbon = savings_kwh × 0.4168 / 1000 # tCO2e
Read all *_calibration_metrics.json. Show pass/fail table. List buildings needing action.
Always lead with the direct answer, then supporting data:
🔍 Campus carbon footprint (simulated, 2024):
Total: 4,821 tCO2e/year | Grid: 0.4168 kgCO2e/kWh
Top emitters:
FOS43 1,240 tCO2e (25.7%)
FOE18 987 tCO2e (20.5%)
FOE6 834 tCO2e (17.3%)
BCA Platinum target: save 1,120 tCO2e/year vs current
If a building's CSV doesn't exist yet, say:
"No simulation data for {BUILDING} — run nus-simulate + nus-parse first."
Never invent numbers. Mark missing data as missing.
Show side-by-side:
FOE6 FOE13
EUI: 142 kWh/m² 118 kWh/m²
Status: ⚠️ over BCA Gold ✅ BCA Gold
MAPE: 24.1% 11.2%
Action: Calibrate Monitor