Estimates building equipment configurations using portfolio archetypes, aerial imagery analysis, and utility service data. Creates draft equipment surveys for buildings without documented equipment details. Use when: "estimate equipment for [property]", "predict HVAC for this building", "create equipment baseline from archetypes", or proactively when user has buildings in Audette but no equipment surveys submitted.
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Une commande directe contourne le prompt de vérification. Examinez la source avant de l'exécuter.
Estimates building equipment configurations using portfolio archetypes, aerial imagery analysis, and utility service data. Creates draft equipment surveys for buildings without documented equipment details. Use when: "estimate equipment for [property]", "predict HVAC for this building", "create equipment baseline from archetypes", or proactively when user has buildings in Audette but no equipment surveys submitted.
Equipment Estimator
Predict building equipment configurations using portfolio intelligence, aerial imagery, and utility service data.
Capabilities:
Learn equipment patterns from existing portfolio buildings (data-driven archetypes)
Generate AI-based equipment baselines when portfolio data is sparse
Analyze satellite imagery to identify rooftop equipment (RTUs, chillers, solar)
Validate fuel types using utility service maps
Create draft equipment surveys ready for Audette submission
Playwright MCP (required) - browser automation for Google Maps
WebSearch (built-in) - utility service map lookup
Modes:
Single building: Detailed analysis with full evidence table
Batch portfolio: Process all buildings without surveys
Pre-flight
Call switch_customer_account with the Audette customer account UID from the system prompt.
This is required before any Audette write operations — omitting it causes HTTP 401.
If no account UID is in the system prompt, call list_customer_accounts and ask the user to select one.
Step 1: Prerequisites Check
Verify required dependencies before proceeding.
Check Audette MCP
Call ToolSearch to verify Audette MCP tools are available:
❌ Playwright MCP required for aerial imagery analysis.
To install:
1. Install via Claude Code plugin marketplace: search for "playwright"
2. Restart Claude Desktop
Requirements:
- Node.js/npx installed on your system
See: https://github.com/microsoft/playwright-mcp
Stop execution if Playwright MCP not available.
Load Portfolio Buildings
Call list_buildings to get all buildings in account:
list_buildings()
Extract building list. Store in memory for later use.
If no buildings found:
❌ No buildings found in Audette account.
Create buildings first using audette-create-building skill.
Stop execution if no buildings exist.
Count Buildings with Equipment Surveys
For each building from list_buildings:
get_equipment_survey(building_model_uid: "{uid}")
Count buildings where survey is not null/empty.
Portfolio assessment:
✓ Prerequisites check complete
Audette account: {account_name}
Total buildings: {total}
Buildings with surveys: {surveyed}
Portfolio data quality:
- ≥10 surveys → "Good - strong archetype data"
- 3-9 surveys → "Fair - limited data, will supplement with AI archetypes"
- <3 surveys → "Sparse - will rely primarily on AI-generated archetypes"
Aerial imagery: Enabled (Playwright installed)
Fuel service validation: Enabled (WebSearch available)
Present status to user before proceeding.
Step 2: Determine Execution Mode
Ask user whether to run single building or batch mode (if not specified in command args).
Parse Command Arguments
If skill invoked with arguments:
Building name or UID → single building mode
"all" or "portfolio" → batch mode
No arguments → ask user
Ask User for Mode (if needed)
Ask the user:
**Which mode would you like to run?**
A) Single building - Detailed analysis with full evidence table
B) Batch portfolio - Process all buildings without surveys
Store mode selection.
Step 3: Build Portfolio Archetypes
Create equipment archetypes from existing portfolio data (data-driven) and generate AI-based archetypes for gaps.
Load Equipment Surveys
For each building with equipment survey (from Step 1):
Climate zone function (see references/climate-zones.md):
def get_climate_zone(state):
cold = ['NY', 'MA', 'VT', 'NH', 'ME', 'WI', 'MN', 'ND', 'SD', 'MT', 'WY', 'ID']
mild = ['WA', 'OR']
hot_dry = ['AZ', 'NM', 'NV']
hot_humid = ['FL', 'LA', 'MS', 'AL', 'GA', 'SC', 'AR']
mixed = ['IL', 'IN', 'OH', 'PA', 'MI', 'VA', 'NC', 'TN', 'KY', 'WV', 'MD', 'DE', 'NJ', 'MO', 'KS']
if state == 'CA': return 'mild'
elif state == 'TX': return 'hot_humid'
elif state in cold: return 'cold'
elif state in mild: return 'mild'
elif state in hot_dry: return 'hot_dry'
elif state in hot_humid: return 'hot_humid'
elif state in mixed: return 'mixed'
else: return 'mixed'
Group buildings by cluster_key:
clusters = {}
for building in buildings_with_surveys:
cluster_key = get_cluster_key(building)
if cluster_key not in clusters:
clusters[cluster_key] = []
clusters[cluster_key].append(building.equipment_survey)
Data-driven archetypes take precedence over AI-generated for same cluster_key.
Report archetype status:
📊 Archetype Library Built
Data-driven archetypes: {count_data_driven}
AI-generated archetypes: {count_ai}
Total coverage: {total_unique_combinations}
Ready to estimate equipment for target buildings.
Step 4: Match Target Building to Archetype
For each target building (single mode: 1 building, batch mode: all buildings without surveys):
# Use browser_evaluate to wait
browser_evaluate(expression: "new Promise(resolve => setTimeout(resolve, 2000))")
Zoom to Rooftop Level
Zoom in to level 19-20 for rooftop detail:
# Zoom in 4-5 clicks (from default ~16 to 19-20)
for i in range(5):
browser_click(selector: "button[aria-label*='Zoom in']")
browser_evaluate(expression: "new Promise(resolve => setTimeout(resolve, 500))")
Capture Screenshot
Take screenshot of current view:
browser_take_screenshot(full_page: false)
Returns base64-encoded image.
Decode and save to /tmp/aerial_{building_uid}.png:
import base64
screenshot_data = base64.b64decode(screenshot_base64)
withopen(f"/tmp/aerial_{building.uid}.png", "wb") as f:
f.write(screenshot_data)
Analyze Screenshot for Equipment
Claude reads the screenshot directly (multimodal capability).
# Suggests central plant
aerial_findings.notes = "Penthouse suggests central boiler/chiller"
Handle Aerial Analysis Failures
If Google Maps navigation fails:
Try alternate address format (without unit number)
If still fails after 1 retry:
- Skip aerial analysis for this building
- Flag: "Aerial analysis failed - could not locate building"
- Continue with archetype + fuel service only
- aerial_findings.confidence = "none"
If screenshot capture fails:
Retry once
If still fails:
- Skip aerial analysis
- Flag: "Aerial analysis failed - screenshot capture error"
- Continue with archetype only
- aerial_findings.confidence = "none"
If image quality is poor:
Check for:
- Heavy shadows/trees obscuring roof
- Low resolution
- Snow/ice covering roof
If obscured:
- aerial_findings.confidence = "low"
- Use archetype prediction, flag aerial as unreliable
- Note: "Aerial view obscured - relied on archetype"
Step 6: Fuel Service Validation
Determine natural gas and steam service availability using WebSearch.
Extract Building Location
city = building.city
state = building.state_province
address = building.street_address # optional for precision
Search for Gas Service
Query 1: Service area map
WebSearch(query: f"{city} {state} natural gas service area map")
Query 2: Utility company (if Query 1 inconclusive)
WebSearch(query: f"{city} {state} gas utility company")
Query 3: Major utility (if city has known major utility - see references/fuel-service-heuristics.md)
# Example for Brooklyn:
WebSearch(query: "National Grid NY service territory map")
Parse Search Results
Look for:
Utility company service area maps (PDFs, interactive maps)
Municipality statements about gas service coverage
Utility company territory descriptions
Decision logic:
IF search finds service map AND city is within marked territory:
gas_service_available = true
confidence = "high"
utility_name = "..." (from map)
source_url = "..." (map URL)
ELSE IF search mentions utility serves city/county:
gas_service_available = true
confidence = "medium"
utility_name = "..." (from search)
source_url = "..." (search result URL)
ELSE IF search returns no clear results:
# Use heuristics based on city population
# Determine population: check building.city_population if available,
# or use Claude's knowledge of major cities, or search "{city} {state} population"
IF city population >100K (urban):
gas_service_available = true
confidence = "low - assumed from location type"
ELSE IF city population 10K-100K (suburban):
gas_service_available = true
confidence = "low - assumed from location type"
ELSE (rural):
gas_service_available = false
confidence = "low - assumed from location type"
ELSE IF search states "no gas service" or "propane only":
gas_service_available = false
confidence = "high"
Check for Steam Service (Large Buildings Only)
If building >100K sqft AND in known steam city:
Known steam systems:
- NYC: Con Edison Steam (Manhattan south of 96th St)
- Boston: Vicinity Energy
- San Francisco: limited district
- Denver: limited district
Query:
WebSearch(query: f"{city} district steam service")
If building in known steam district:
steam_service_available = true
confidence_steam = "medium" # requires user confirmation
Flag for user confirmation (steam less common)
Apply Fuel Constraints
Once gas service availability is determined:
IF gas_service_available = false:
# Override any gas predictions from archetype
IF draft_survey.central_plant_heating.heating_type in ['gas_boiler', 'gas_furnace']:
# Use climate/age-aware fallback (see references/fuel-service-heuristics.md)
IF climate_zone == 'cold' AND age_decade < '1990s':
draft_survey.central_plant_heating.heating_type = 'oil'
ELSE:
draft_survey.central_plant_heating.heating_type = 'electric_resistance'
fuel_override_applied = true
IF draft_survey.dhw_heater.dhw_fuel == 'natural_gas':
draft_survey.dhw_heater.dhw_fuel = 'electric'
fuel_override_applied = true
ELSE IF gas_service_available = true:
# Allow gas equipment types (no override needed)
fuel_override_applied = false
Store Fuel Service Findings
fuel_service = {
"location": f"{city}, {state}",
"gas_service_available": true/false,
"gas_utility": "National Grid" or None,
"steam_service_available": true/false,
"confidence": "high|medium|low",
"source": "https://..." or "heuristic: urban area",
"notes": "Building within utility territory" or "",
"fuel_override_applied": true/false
}
Handle WebSearch Failures
If WebSearch tool unavailable or fails:
⚠️ WebSearch unavailable - cannot validate fuel service
Falling back to heuristics:
- Urban area → assume gas available (low confidence)
- Rural area → assume no gas (low confidence)
Predictions may be inaccurate - verify fuel availability manually.
Continue with heuristics-based guess, flag all fuel-related predictions as low confidence.
Step 7: User Review and Confirmation
Present draft equipment survey with transparent evidence for all predictions.
Single Building Mode: Detailed Evidence Table
Present to user:
## Equipment Survey Draft: {building.building_name}
**Building**: {building.street_address}, {building.city} {building.state_province}
**Building UID**: {building.uid}
**Archetype match**: {matched_archetype.cluster_id} ({matched_archetype.building_count} similar buildings)
**Archetype source**: {Data-driven | AI-generated presumptive}
**Aerial imagery**: {aerial_findings.confidence} - {aerial_findings.notes}
**Gas service**: {fuel_service.gas_service_available} - {fuel_service.utility or "Not available"}
---
### Heating System
| Field | Predicted Value | Evidence | Confidence |
|-------|----------------|----------|------------|
| Exists | {Yes/No} | {Archetype: X% have heating} | {High/Medium/Low} |
| Type | {gas_boiler/etc} | {Aerial: RTUs visible | Archetype: Y% gas_boiler | AI archetype: typical} | {High/Medium/Low} |
| Fuel | {natural_gas/etc} | {Archetype + Gas service confirmed | Override: no gas service} | {High/Medium/Low} |
| Terminal units | {baseboards/etc} | {Archetype median} | {Medium} |
| Size (kW) | {175} | {Archetype median for size class} | {Medium} |
| Install year | {1995} | {Archetype median (building built {year})} | {Low} |
### Cooling System
| Field | Predicted Value | Evidence | Confidence |
|-------|----------------|----------|------------|
| Exists | {Yes/No} | {Aerial: 4 RTUs visible | Archetype: X% have cooling} | {Very High/High} |
| Type | {rooftop_units} | {Aerial: Direct observation | Archetype} | {Very High/High} |
| Count | {4} | {Aerial: Counted from imagery} | {High} |
| Size per unit (kW) | {35} | {Archetype median / count} | {Medium} |
| Install year | {1995} | {Archetype median} | {Low} |
### DHW (Domestic Hot Water)
| Field | Predicted Value | Evidence | Confidence |
|-------|----------------|----------|------------|
| Exists | {Yes/No} | {Archetype: X% have DHW} | {High/Medium} |
| Fuel | {natural_gas} | {Archetype + gas service confirmed} | {High} |
| Central distribution | {Yes} | {Archetype: X% centralized} | {Medium} |
| Tank size (litres) | {300} | {Archetype median} | {Medium} |
| Install year | {1995} | {Archetype median} | {Low} |
### Solar PV
| Field | Predicted Value | Evidence | Confidence |
|-------|----------------|----------|------------|
| Exists | {Yes/No} | {Aerial: Panels visible | Archetype} | {High/Medium} |
| Size (kW) | {75} | {Aerial: ~30% coverage × roof area} | {Medium} |
[... continue for all sections: elevators, air handling, etc.]
---
**⚠️ Low confidence fields** (please review):
- Install years: Based on archetype averages, not building-specific data
- Equipment sizes: Estimated from building size, not nameplate data
- {other low confidence fields}
**✓ High confidence fields**:
- Cooling type and count: Directly observed in aerial imagery
- Gas service: Confirmed via {utility} service map
- {other high confidence fields}
Ask for User Action
Ask the user:
**How would you like to proceed?**
A) Submit this draft as-is to Audette
B) Edit specific fields before submitting
C) Show me the aerial imagery to verify
D) Cancel - don't submit
If User Chooses "Edit" (Option B)
Ask which section to edit:
**Which section would you like to edit?**
A) Heating system
B) Cooling system
C) DHW (hot water)
D) Solar PV
E) Other sections
Present current values for selected section and ask for corrections:
For categorical fields (heating_type, cooling_type):
**Current: heating_type = gas_boiler**
Select new value:
A) gas_boiler (current)
B) electric_resistance
C) heat_pump
D) rooftop_units
E) Other (specify)
For numeric fields (size_kw, install_year):
**Current: heating_size_kw = 175**
Enter new value (or press Enter to keep current):
Ask the user for each field they want to change.
Update draft survey with user corrections.
Re-present evidence table with updated values and "User override" as evidence source:
| Field | Value | Evidence | Confidence |
|-------|-------|----------|------------|
| Type | heat_pump | User override (was: gas_boiler from archetype) | High |
Re-ask for action (Submit / Edit more / Cancel).
If User Chooses "Show Imagery" (Option C)
Display aerial screenshot:
Read image from /tmp/aerial_{building.uid}.png and display inline.
Point out identified equipment:
📸 Aerial Imagery Analysis
[Image shown above]
Identified equipment:
- 4 RTUs visible in center of roof (marked with red boxes in my analysis)
- No cooling tower visible
- Solar panels covering ~30% of south-facing roof area
- No penthouse structure
Do these identifications look correct?
Ask user to confirm or correct aerial analysis.
If user corrects:
Update aerial_findings
Regenerate draft survey with corrections
Re-present evidence table
Return to action menu (Submit / Edit / Cancel).
Batch Mode: Summary Table
For batch portfolio mode, present summary table instead of detailed evidence:
## Equipment Estimation Summary
Processed {N} buildings without surveys:
| Building | Heating | Cooling | Aerial | Gas Service | Confidence |
|----------|---------|---------|--------|-------------|------------|
| 123 Main St | gas_boiler | rooftop_units (4) | ✓ High | ✓ Available | High |
| 456 Oak Ave | heat_pump | chiller (2) | ✗ Failed | ✓ Available | Medium |
| 789 Elm Rd | gas_boiler | none | ✓ Medium | ✓ Available | Medium |
| ... | ... | ... | ... | ... | ... |
Legend:
- Aerial: ✓ = analysis succeeded, ✗ = failed/skipped
- Gas Service: ✓ = confirmed available, ✗ = not available
- Confidence: Overall prediction confidence (High/Medium/Low)
**Review options:**
A) Submit all drafts to Audette (triggers re-modeling for all buildings)
B) Review individual buildings before submitting (show detailed evidence for each)
C) Export all as JSON files for manual review
D) Cancel batch operation
Ask the user: for batch action selection.
If Batch User Chooses "Review Individual" (Option B)
For each building in batch:
Show detailed evidence table (same as single building mode)
Ask: Submit / Edit / Skip this building
Track: submitted, skipped, edited
After all buildings reviewed:
Show summary of actions taken
Proceed to submission step
If Batch User Chooses "Export" (Option C)
Create equipment-estimates/ directory:
mkdir -p equipment-estimates
For each building:
Save draft survey to equipment-estimates/{building_name}-draft.json
Save evidence report to equipment-estimates/{building_name}-report.md
Create summary CSV:
building_name,building_uid,heating_type,cooling_type,aerial_confidence,gas_service,overall_confidence
123 Main St,abc-123,gas_boiler,rooftop_units,high,available,high
...
Save to equipment-estimates/portfolio-summary.csv.
Report to user:
✓ Exported {N} draft surveys
Files saved:
- equipment-estimates/*.json - Draft survey JSONs
- equipment-estimates/*-report.md - Evidence reports
- equipment-estimates/portfolio-summary.csv - Summary table
You can review these files and manually submit surveys later using audette-equipment-survey skill.
Stop here (don't submit to Audette).
Step 8: Submit Equipment Survey to Audette
After user confirms, submit draft survey(s) to Audette MCP.
Validate Survey Completeness
Before submission, verify all required sections are present:
heating_type, cooling_type, dhw_fuel must match schema allowed values
See references/equipment-schema.md for valid enums
Check numeric ranges:
size_kw > 0
install_year between 1900 and current year
tank_size_litres > 0
If validation fails:
❌ Survey validation failed:
- {list of validation errors}
Cannot submit. Please review and correct.
Fix errors or ask user to correct, then retry validation.
Convert to ton-equivalents BEFORE any submit (the survey owns this)
⚠️ The draft above is in natural units (tons, MBH, kW, gallons, CFM). Audette stores every *_size
in ton-equivalents, so the draft is NOT submittable as-is. The conversion is owned by the
audette-equipment-survey skill — do NOT hand-roll it here. Before any submit_equipment_survey
call below, hand the natural-unit draft to that skill (or apply its references/submission-guide.md
"Unit conversions": MBH ÷ 12; kW ÷ 3.517; DHW nameplate MBH ÷ 12 or gallons ÷ 40; airflow stays CFM;
PV stays kW). Never submit a raw natural-unit capacity — that is exactly the kW/litres bug the
survey skill exists to prevent.
Single Building: Submit Survey
Call submit_equipment_survey (with the ton-converted draft from the step above):
✓ Equipment survey submitted successfully
Building: {building.building_name}
Audette is now re-modeling this building with updated equipment data.
Estimated re-modeling time: 2-5 minutes
Next steps:
- Wait for re-modeling to complete
- Review updated carbon reduction plan
- Generate decarbonization report with new baseline
On error:
❌ Survey submission failed
Error: {error_message}
Draft saved to: .audette-equipment-draft-{building_name}.json
You can:
- Review the error and retry
- Manually correct the JSON and submit via audette-equipment-survey skill
Save draft to file for manual review:
Write draft_survey JSON to file:
.audette-equipment-draft-{building_name}.json
Use JSON serialization with indent=2 for readability.
Batch Mode: Submit All Surveys
For each building in batch (that user didn't skip):
submission_failed[] - buildings that failed with error messages
After all submissions:
📊 Batch Submission Complete
✓ {N} surveys submitted successfully:
- {building_name_1}
- {building_name_2}
- ...
✗ {M} surveys failed:
- {building_name_X}: {error_message}
- {building_name_Y}: {error_message}
Failed drafts saved to:
- .audette-equipment-draft-{building_X}.json
- .audette-equipment-draft-{building_Y}.json
All successful buildings are now re-modeling in Audette.
Estimated completion: 2-5 minutes
Save Estimation Metadata
For each submitted survey, save metadata alongside:
{"building_uid":"abc-123","building_name":"123 Main Street","estimated_at":"2026-05-20T14:30:00Z","archetype_cluster":{"cluster_id":"office_medium_1990s_cold","building_count":12,"source":"data_driven","confidence":0.85},"aerial_imagery":{"analyzed":true,"screenshot_path":"/tmp/aerial_abc-123.png","rtus_counted":4,"confidence":"high"},"fuel_service":{"gas_available":true,"utility":"National Grid","source":"https://...","confidence":"high"},"overall_confidence":"high","low_confidence_fields":["heating_install_year","cooling_install_year","dhw_install_year"],"user_edits":[]}
Save to: equipment-estimates/{building_name}-metadata.json
Step 9: Post-Submission Summary
After all submissions complete:
Report Final Status
Single building mode:
✅ Equipment Estimation Complete
Building: {building.building_name}
Survey submitted: ✓
Archetype source: {data_driven | AI-generated}
Aerial analysis: {confidence}
Fuel service: {gas_service_available}
Overall confidence: {high | medium | low}
Low confidence fields flagged:
- {field_1}
- {field_2}
Recommendations:
- Review updated carbon plan when re-modeling completes (~3 min)
- Consider gathering additional operator input to improve low-confidence fields (equipment-gap-questionnaire skill - future enhancement)
- Run audette-equipment-survey skill if detailed documentation becomes available
Batch mode:
✅ Batch Equipment Estimation Complete
Total buildings processed: {N}
Surveys submitted: {N_success}
Surveys failed: {N_failed}
Average confidence: {avg_confidence}
Portfolio coverage:
- Buildings with surveys (before): {count_before}
- Buildings with surveys (after): {count_before + N_success}
- Coverage: {(count_before + N_success) / total_buildings * 100}%
Next steps:
- Wait ~5 minutes for all buildings to re-model
- Review carbon plans for newly estimated buildings
- Address low-confidence predictions with site surveys or additional operator input (equipment-gap-questionnaire skill - future enhancement)
Suggest Next Actions
Based on confidence levels:
If many low-confidence predictions:
💡 Suggestion: {N} buildings have low overall confidence
Consider:
- Gathering additional operator input to fill gaps (equipment-gap-questionnaire skill - future enhancement)
- Site visit to verify equipment types
- Reviewing aerial imagery for unclear buildings
If archetype data is sparse:
💡 Suggestion: Portfolio archetype data is limited ({N} surveyed buildings)
Recommendation:
- Submit detailed equipment surveys for 5-10 more buildings (diverse types/sizes)
- Run equipment estimator again after more surveys added
- Confidence will improve as portfolio data grows
If aerial analysis failed for many buildings:
⚠️ Aerial analysis failed for {N} buildings
Possible reasons:
- Buildings not found in Google Maps (check addresses)
- Tree/vegetation obscuring rooftops
- Image quality too low
These buildings relied solely on archetypes - lower confidence.
Cleanup
Remove temporary files:
rm /tmp/aerial_*.png
(Unless user requested to keep for review)
End of skill execution.
Notes
Confidence Levels
Very High (>90%):
Direct observation from aerial imagery
Hard constraint (fuel service confirmed/denied)
Data-driven archetype with ≥10 buildings
High (70-90%):
Data-driven archetype with 5-9 buildings
Aerial imagery medium quality
Fuel service medium confidence
Medium (50-70%):
Data-driven archetype with 3-4 buildings
AI-generated archetype
Aerial imagery unclear or failed
Fuel service low confidence (heuristics)
Low (<50%):
AI-generated archetype only
No aerial imagery
No fuel service validation
Install years (always estimates)
Error Recovery
Skill continues gracefully if:
Aerial imagery fails → use archetype only, flag it
WebSearch fails → use heuristics, flag it
Individual building submission fails → save draft, continue to next
