القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.
Derive assay metrics from typed artifacts and segments, including bout counts, durations, rates, occupancy, proximity, and investigation time.
| name | behavior-vlm-labeling |
| description | 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. |
| metadata | {"annolid":{"always":false}} |
Use this skill when the user asks Annolid Bot to label behavior segments from a video, frame grid, or fixed interval, especially with phrasing such as "label behavior", "defined list", "every 1s", or "frames per grid".
no_behavior.label value must be exactly one allowed behavior label or no_behavior.no_behavior when none of the listed behaviors is clearly visible.no_behavior as a skipped segment, not as a persisted behavior interval.