perceiving-next-item

perceiving-next-item is a skill for Claude Code from graph-robots/open-robot-skills. It costs 209 tokens per session (3,356 once invoked), scanned A, original, Apache-2.0.

A robot-vision loop for clearing objects into a container such as a basket or box. Each observation identifies both the destination container and the next item to move, then decides whether the loop should continue.

In plain words
What is it for?
Pack-all and clean-all robot tasks where objects are repeatedly picked up and placed into one container.
Why use it?
It removes the need to build separate checks for finding the container, finding the next object, and stopping when no objects remain.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the open-robot-skills plugin — 39 skills shipped together

Good fit Pack-all and clean-all robot tasks where objects are repeatedly picked up and placed into one container.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/graph-robots/open-robot-skills/perceiving-next-item
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add graph-robots/open-robot-skills --skill perceiving-next-item
Clone the repo
git clone --depth 1 https://github.com/graph-robots/open-robot-skills

Made for: Claude Code.

Or install open-robot-skills, the plugin that ships this one along with the rest of its 39 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for perceiving-next-item

README.md
[![agentmods](https://agentmods.dev/badge/skills/graph-robots/open-robot-skills/perceiving-next-item/github.svg)](https://agentmods.dev/skills/graph-robots/open-robot-skills/perceiving-next-item)
Your own site
<a href="https://agentmods.dev/skills/graph-robots/open-robot-skills/perceiving-next-item"><img src="https://agentmods.dev/badge/skills/graph-robots/open-robot-skills/perceiving-next-item/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for perceiving-next-item

Your own site · 80×15
<a href="https://agentmods.dev/skills/graph-robots/open-robot-skills/perceiving-next-item"><img src="https://agentmods.dev/badge/skills/graph-robots/open-robot-skills/perceiving-next-item.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 209 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,356 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00209 $0.03356
Opus 5 $0.00105 $0.01678
Sonnet 5 $0.00042 $0.00671
Haiku 4.5 $0.00021 $0.00336

Measured 10d ago against content hash 51b3e7f045f1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

perceiving-next-item scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.

The scan reads SKILL.md. This mod also ships 3 executable files (scripts/decide_next_item.py, scripts/exterior_view.py, scripts/perceive_dino_vlm.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/perceiving-next-item/SKILL.md · 253 lines

How it starts

The opening of the file, as written. The whole thing — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.

perceiving-next-item

The repeating head of a pack-all loop. One robot.get_observation feeds two perceptions — the destination container and the next remaining item — and a decide router turns the item verdict into the loop's continue/terminate signal. It is perceiving-objects (the reliable pairwise-tournament perception) composed with (a) a second perception for the container and (b) a clean none exit, so a "pick up every object and place it in the basket" workflow has one subgraph that answers "is there still an item, and where is the basket?" every pass.

Detection uses the same pairwise VLM crop tournament as perceiving-objects (~30% → 97% object-ID over a one-shot Set-of-Marks pick on the LIBERO-PosVar study). The clean loop terminator comes from the item's container-excluding object_description: once only the basket remains, the tournament + verify gate answer "no grocery item", perceive_item returns found=False, and the decide router emits none → the loop exits to done.

When to use

  • The repeating head of a pack-all / clean-all-items loop (transport routes its success edge back here), where each pass must reliably decide "grasp the next item" vs "everything is packed, stop".
  • When the downstream place needs a fresh container OBB every pass (this skill emits container_obb/container_mask/container_cloud alongside the target).

When NOT to use

  • Single pick-and-place (grab ONE named object). Use perceiving-objects (one target, no loop, no container co-perception).
  • You want the container localized once, out of the loop. If the container never moves and you prefer to perceive it a single time before the loop, use a plain perceiving-objects subgraph for the basket + perceiving-objects-oneshot for the looping target. This skill deliberately re-localizes both each pass (robust to a nudged basket, one observation, one clean none).
  • Cluttered scenes with look-alike distractors. Prefer perceiving-objects for the target identity — its pairwise-tournament plus object_description hints disambiguate look-alikes (it lacks the clean loop none, so you would add your own decide).

Read the full file on GitHub · 253 lines

Files

What ships with it

7 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 10d ago First seen · 253 lines · 209 tokens per session scan A 51b3e7f045f1

Subscribe to this mod's changes

perceiving-next-item is a skill published in the GitHub repository graph-robots/open-robot-skills (41 stars, last pushed yesterday), licensed Apache-2.0. It adds 209 tokens to every session and 3,356 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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