perceiving-next-item

perceiving-next-item is an Embedded, IoT & robotics skill for AI agents, published by graph-robots in the open-robot-skills repository on GitHub. Skillspace rates perceiving-next-item C (78/100) and it passed the safety scan; the rating is based on static analysis only.

Loop-head perception for pack-all / clean-all-items tasks. From ONE observation it localizes BOTH the destination container (basket, bin, box) AND the next remaining target item, using the pairwise VLM crop tournament with a container-excluding description so the target is never confused with the basket, then makes a clean found / none decision: an item was found (grasp it) or only the container remains (all items packed — exit the loop to done). Use when a workflow must pick up EVERY object and place each into a container in a loop (pack-all / clean-all-items) and each pass must reliably answer "is there still a graspable item, or are we done?" while also exposing a fresh container OBB for the downstream place. This subgraph is self-contained and takes NO inputs (inputs: {}); the container and item phrases are literal strings written inside the perception nodes, never subgraph parameters — do not declare item_name / container_name / item_description as subgraph inputs.

Grade

C

Quality

78 / 100

Safety

Passed

GitHub stars

22

Unproven: rated by static analysis only, not yet compared head-to-head.

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