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Use this skill when the user asks to work with shapes in annotation-store NDJSON files or JSON stubs that reference annotation stores.
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Based on SOC occupation classification
Label video behavior segments from frame-grid images with a defined behavior list, model-ready JSON, resumable outputs, and no_behavior handling for sparse labels.
Use Annolid GUI tools for robust automation across web, PDF, video, and chat controls.
Infer the assay or paradigm from video context, task text, tracked entities, and experimental cues such as social interaction, open field, courtship, resident-intruder, and novel object recognition.
Choose assay-specific features and measurable objectives for behavior analysis, including distances, zones, speed, contact, orientation, and object interaction.
Segment behavior timelines from tracks, pose, contact, speed, and proximity signals into typed intervals with stable labels and rationales.
Keep behavior analysis aligned with assay objectives, controls, reproducibility, and measurable outputs instead of ad hoc summaries.
| name | shape-annotation-store |
| description | Use this skill when the user asks to work with shapes in annotation-store NDJSON files or JSON stubs that reference annotation stores. |
This skill is for annotation-store workflows:
*_annotations.ndjsonannotation_storegui_list_shapes_in_annotationgui_relabel_shapes_in_annotationgui_delete_shapes_in_annotationdelete_all=true).dry_run=true first for risky relabel/delete requests.