Room Lighting Status

[yushanwe]

Feature Description
Environmental lighting level indicator

Problem It Solves
Cannot determine room lighting level

Proposed Solution
Provide real-time environmental lighting level indication

Implementation details

Assume each generated tool implements one user-facing task. If this issue enumerates multiple stages, execute one ordered copilot_llm_call(...) per stage and explicitly pass useful structured artifacts to later calls with metadata={"previous_stage_artifact": ...}. Use the stage capability as capability. Choose only from these capabilities: general_reasoning, ocr, object_detection_localization, structured_visual_understanding, spatial_reasoning, navigation, camera_motion, or temporal_reasoning. Never use visual_reasoning. The backend may evaluate and escalate reasoning capabilities according to the execution policy. Generated tools must not choose implementations, models, providers, detector backends, fallback order, retries, or verification logic. Do not implement detection, OCR, VLM, LLM, model loading, or provider calls inside generated tool files. Generated tools must not create routers, capability registries, detector/OCR/LLM wrappers, new model-router clients, provider-specific DEFAULT_MODEL constants, COCO_CLASSES, .pt model loading/discovery logic, or direct provider calls.

Alternatives Considered

Example usage
Notify user if the current environment is dark, dim, normal, or bright, and alert if lighting changes significantly

Live Mode
no

Live Query

Additional Context
Custom GPT: No
Unless otherwise specified, in streaming mode, any verbal/text response should be limited to 15 words. No such limit applies to one-shot output.

Task Stages

Stage 1

• Goal: Determine the ambient brightness of the room.
• Capability: general_reasoning

Write the code for this tool inside the tools folder. Assume the tool implements one user-facing task. For a Task Stages section, make one ordered copilot_llm_call(...) per stage and explicitly pass useful artifacts to later calls. Use only these capabilities: general_reasoning, ocr, object_detection_localization, structured_visual_understanding, spatial_reasoning, navigation, camera_motion, temporal_reasoning. Never use visual_reasoning. Do not import litellm, call litellm.completion(), create new model-router clients, create ModelRouter classes, resolve model names, resolve API keys, import detector libraries/provider SDKs/YOLO, define COCO_CLASSES, hardcode model names, load .pt files, or call YOLO(...). Do not select implementations, providers, fallback order, retries, or verification logic in generated tools.