Equipment Estimator

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

Dependencies:

• Audette MCP (required) - list_buildings, get_equipment_survey, submit_equipment_survey
• 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:

ToolSearch(query: "select:list_buildings,get_equipment_survey,submit_equipment_survey")

If not found:

❌ Audette MCP not connected.

Please configure Audette MCP in Claude Desktop settings and restart.

Stop execution if Audette MCP not available.

Check Playwright MCP

Call ToolSearch to verify Playwright MCP tools are available:

ToolSearch(query: "select:playwright__browser_navigate,playwright__browser_take_screenshot")

If not found:

❌ 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):

get_equipment_survey(building_model_uid: "{uid}")

Extract:

• Building archetype (from building record)
• Gross floor area (for size binning)
• Year built (for age decade)
• State/province (for climate zone)
• Equipment survey data (all fields)

Store in list: buildings_with_surveys[]

Cluster Buildings (Data-Driven Archetypes)

For each building in buildings_with_surveys[]:

Extract clustering dimensions:

archetype = building.building_archetype
size_bin = get_size_bin(building.gross_floor_area_square_feet)
age_decade = get_age_decade(building.year_built_original)
climate_zone = get_climate_zone(building.state_province)

cluster_key = f"{archetype}_{size_bin}_{age_decade}_{climate_zone}"

Size binning function:

def get_size_bin(sqft):
    if sqft < 5000: return 'tiny'
    elif sqft < 25000: return 'small'
    elif sqft < 100000: return 'medium'
    elif sqft < 500000: return 'large'
    else: return 'xlarge'

Age decade function:

def get_age_decade(year):
    decade = (year // 10) * 10
    return f"{decade}s"

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)

Compute Median Equipment (Data-Driven)

For each cluster with ≥3 buildings:

Categorical fields (heating_type, cooling_type, dhw_fuel):

• Use mode (most common value)
• If tie, prefer more efficient option

Numeric fields (size_kw, install_year):

• Use median
• Exclude outliers (>2 std dev from mean)

Boolean fields (pv_exists):

• Use mode
• If ≥50% have it → exists = true

Count fields (cooling_count):

• Use median, round to integer
• Minimum 1 if exists = true

Confidence score:

confidence = min(cluster_size / 10, 1.0)

Store archetype:

data_driven_archetypes[cluster_key] = {
    "cluster_id": cluster_key,
    "building_count": len(cluster),
    "source": "data_driven",
    "median_equipment": {
        "heating_type": mode(heating_types),
        "cooling_type": mode(cooling_types),
        "dhw_fuel": mode(dhw_fuels),
        # ... all equipment fields
    },
    "confidence": min(len(cluster) / 10, 1.0)
}

Generate AI-Based Presumptive Archetypes

For archetype combinations present in portfolio but with <3 surveyed buildings:

Identify needed archetypes:

Get all buildings from list_buildings() (including those without surveys).

Extract unique combinations of [archetype, size_bin, age_decade, climate_zone].

For each combination NOT in data_driven_archetypes (or with <3 buildings):

Generate AI-based equipment prediction using Claude's knowledge:

Read references/ai-archetypes.md for templates.

Match target building to closest template:

• Office: size and age determine equipment type
• Multifamily: size determines central vs suite-level
• Retail: RTUs dominant across all sizes
• Warehouse: Large RTUs typical

Apply climate adjustments:

• Cold: prefer gas heating
• Mild: heat pumps common
• Hot: large cooling capacity
• Mixed: balanced systems

Apply age adjustments:

• Pre-1980: gas boiler + minimal cooling
• 1980-2000: transition period
• Post-2000: RTUs dominant
• Post-2015: high efficiency + heat pumps

Store AI archetype:

ai_archetypes[cluster_key] = {
    "cluster_id": f"{cluster_key}_PRESUMPTIVE",
    "building_count": 0,
    "source": "ai_generated",
    "median_equipment": {
        "heating_type": "gas_boiler",  # from template
        "cooling_type": "rooftop_units",  # from template
        # ... all fields based on template
    },
    "confidence": 0.4,  # Always low-medium for AI
    "rationale": "Typical {age} {archetype} in {climate} climate: {explanation}"
}

Merge Archetype Libraries

Create unified archetype library:

archetypes = {**ai_archetypes, **data_driven_archetypes}

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):

Extract Target Building Dimensions

target_archetype = building.building_archetype
target_size_bin = get_size_bin(building.gross_floor_area_square_feet)
target_age_decade = get_age_decade(building.year_built_original)
target_climate_zone = get_climate_zone(building.state_province)

target_cluster_key = f"{target_archetype}_{target_size_bin}_{target_age_decade}_{target_climate_zone}"

Find Matching Archetype

Exact match first:

if target_cluster_key in archetypes:
    matched_archetype = archetypes[target_cluster_key]

Relax one dimension if no exact match:

Try adjacent age decades (±10 years):
  target_age_decade = "1990s" → try "1980s", "2000s"

Try adjacent size bins:
  target_size_bin = "medium" → try "small", "large"

Try same archetype + climate, any age/size:
  "{target_archetype}_*_{target_climate_zone}"

Fallback to archetype-only:

Try same archetype, any size/age/climate:
  "{target_archetype}_*"

Last resort:

Use generic AI archetype for building type

Create Draft Equipment Survey

Copy equipment fields from matched archetype:

draft_survey = {
    "central_plant_heating": {
        "heating_exists": matched_archetype.heating_type is not None,
        "heating_type": matched_archetype.heating_type,
        "heating_terminal_units": matched_archetype.heating_terminal_units,
        "heating_size_kw": matched_archetype.heating_size_kw,
        "heating_install_year": matched_archetype.heating_install_year,
        "heating_fuel": matched_archetype.heating_fuel
    },
    "central_plant_cooling": {
        "cooling_exists": matched_archetype.cooling_type is not None,
        "cooling_type": matched_archetype.cooling_type,
        "cooling_size_kw": matched_archetype.cooling_size_kw,
        "cooling_install_year": matched_archetype.cooling_install_year,
        "cooling_count": matched_archetype.cooling_count
    },
    # ... all other sections from schema
}

Store:

• draft_survey - current predictions
• matched_archetype - for evidence table
• archetype_confidence - for user review

Step 5: Aerial Imagery Analysis

Capture satellite imagery and analyze for visible equipment.

Note: For brevity, Playwright MCP tools are referenced by short names:

• browser_navigate() → playwright__browser_navigate()
• browser_wait_for() → playwright__browser_wait_for()
• browser_fill_form() → playwright__browser_fill_form()
• browser_press_key() → playwright__browser_press_key()
• browser_click() → playwright__browser_click()
• browser_evaluate() → playwright__browser_evaluate()
• browser_take_screenshot() → playwright__browser_take_screenshot()

Navigate to Google Maps

Use Playwright to navigate to building location:

browser_navigate(url: "https://www.google.com/maps")

Wait for page load:

browser_wait_for(selector: "input[aria-label*='Search']", timeout: 5000)

Search for Building Address

Get full address from building record:

address = f"{building.street_address}, {building.city}, {building.state_province}"

Fill search box:

browser_fill_form(
    form_data: {
        "input[aria-label*='Search']": address
    }
)

Press Enter:

browser_press_key(key: "Enter")

Wait for map to center:

browser_wait_for(selector: ".widget-scene-canvas", timeout: 5000)

Switch to Satellite View

Click satellite button:

browser_click(selector: "button[aria-label*='Satellite']")

Wait for satellite tiles to load (2 seconds):

# 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)
with open(f"/tmp/aerial_{building.uid}.png", "wb") as f:
    f.write(screenshot_data)

Analyze Screenshot for Equipment

Claude reads the screenshot directly (multimodal capability).

Read the saved image:

Read(file_path: f"/tmp/aerial_{building.uid}.png")

Analyze for equipment markers (see references/aerial-features.md):

Look for:

1. Rooftop Units (RTUs): Rectangular boxes, silver/gray, in rows
2. Cooling Tower: Circular/square, white, with fan housing
3. Solar Panels: Dark blue/black grid pattern
4. Penthouse: Raised structure in center of roof
5. PTAC Units: Grid pattern on building facade (not roof)

Count visible equipment:

• Count RTUs (most actionable signal)
• Note cooling tower presence
• Estimate solar coverage (% of roof area)

Extract aerial findings:

aerial_findings = {
    "rtus_visible": 4,  # counted from image
    "rtus_locations": "Center and east side of roof",
    "cooling_tower_visible": false,
    "solar_panels_visible": true,
    "solar_coverage_estimate": "~30% of roof area",
    "penthouse_present": false,
    "facade_units_visible": false,
    "image_quality": "high",  # clear view
    "confidence": "high"
}

Override Draft Survey with Aerial Findings

If RTUs visible:

draft_survey.central_plant_cooling.cooling_type = "rooftop_units"
draft_survey.central_plant_cooling.cooling_count = aerial_findings.rtus_visible
draft_survey.central_plant_heating.heating_type = "rooftop_units"  # RTUs often have heating

If cooling tower visible:

draft_survey.central_plant_cooling.cooling_type = "chiller"
draft_survey.central_plant_cooling.central_plant = true

If solar panels visible:

draft_survey.pv_system.pv_exists = true
roof_area_sqft = building.gross_floor_area_sqft / building.floors_above_grade
coverage_pct = 0.30  # from estimate
draft_survey.pv_system.size_kw = roof_area_sqft * coverage_pct * 0.015

If penthouse visible:

# 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:

Required sections:
- central_plant_heating
- central_plant_cooling
- suite_level_heating
- suite_level_cooling
- dhw_heater
- air_handling_equipment
- rooftop_units
- pv_system
- elevators

For each section:

If _exists = false:

• All other fields in section must be null

If _exists = true:

• Required fields must have non-null values

Check enum values:

• 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):

submit_equipment_survey(
    building_model_uid: building.uid,
    equipment_survey: draft_survey
)

On success:

✓ 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):

submit_equipment_survey(
    building_model_uid: building.uid,
    equipment_survey: draft_survey
)

Track results:

• submitted_successfully[] - buildings submitted OK
• 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

Skill stops if:

• Audette MCP not connected
• Playwright MCP not installed
• No buildings in portfolio

Performance

Single building:

• Prerequisites: 5 sec
• Archetype loading: 30-60 sec
• Baseline matching: 5 sec
• Aerial capture: 60-120 sec
• Fuel validation: 30-60 sec
• User review: variable
• Submission: 5 sec
• Total: 5-10 minutes

Batch (10 buildings):

• Prerequisites: 5 sec
• Archetype loading: 60 sec (once)
• Per building: 3-5 min
• User review: 2-5 min
• Submission: 30 sec
• Total: 30-60 minutes