Skip to content

From the author

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.

Install

npx skills add graph-robots/open-robot-skills --skill perceiving-next-item -a claude-code

Installs to .claude/skills/perceiving-next-item/ · Each command fetches the skill straight from GitHub with the open skills CLI.

Download .zip

Byte-identical to the scanned commit, with the repository’s licence and a SOURCE.txt.

Want your agent to suggest skills while you type? See how to use skills from your IDE.

Safety checks on SKILL.md

  • Ships scripts we have not reviewedLow

Why 76

Security85
Provenance61
Maintenance100
Licence100
Adoption31

aiqbench computes the trust score from public signals. It is not a security audit, so read a skill before you install it.

Similar skills

Closest to this one by what they do and when they are used.

Ratings & reviews

—

no reviews

Tried this skill? Rate it to help others. Sign in

    Discussion

    Join the discussion. Sign in