Honeybee Aperture Remover

Use This Skill When

• The user wants all windows removed
• The user wants apertures removed only from selected faces
• The user wants to clear glazing before re-adding apertures
• The user wants a facade reset without affecting the whole model

Preferred Tool

remove

Description Unified removal bus. Aperture deletion mainly uses all_apertures and face_objects.

Args

• operation: str
• identifiers: list | None = None
• options: dict | None = None

Returns

• success: bool
• message: str
• operation-specific removal fields
• auto_save: dict when relevant

Operation Selection

all_apertures

Use when every aperture in the model should be removed.

remove(operation="all_apertures")

face_objects

Use when removal should be scoped to selected faces.

Identifiers

• Face identifiers

Options

• apertures
• doors
• indoor_shades
• outdoor_shades
• sub_faces

Aperture-only example

remove(
    operation="face_objects",
    identifiers=["Face_1", "Face_2"],
    options={"apertures": True}
)

Apertures plus attached objects example

remove(
    operation="face_objects",
    identifiers=["Face_3"],
    options={"apertures": True, "outdoor_shades": True}
)

Subface reset example

remove(
    operation="face_objects",
    identifiers=["Face_4"],
    options={"sub_faces": True}
)

Reset-and-Rebuild Pattern

A common pattern is to clear existing apertures and then re-add with new parameters:

# 1. Query current state
query(target_type="face", identifiers=["Face_1"],
    fields=["identifier", "aperture_ratio", "apertures"])

# 2. Clear existing apertures
remove(operation="face_objects", identifiers=["Face_1"],
    options={"apertures": True})

# 3. Add new apertures with different parameters
add(operation="apertures_by_ratio", target_type="face",
    identifiers=["Face_1"], params={"ratio": 0.5})

# 4. Verify new state
query(target_type="face", identifiers=["Face_1"],
    fields=["identifier", "aperture_ratio", "apertures"])

Return Guidance

• Use message for quick confirmation.
• Use results when explaining what happened on each face.
• Inspect not_found if identifiers may not exist in the current model.
• Mention auto_save when the source model came from shared memory.

Workflow

1. Query faces first if the scope is uncertain.
2. Choose model-wide or face-scoped removal.
3. Re-query aperture counts or face aperture ratios after removal if verification is needed.