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Verifying Grasps

Organization
graph-robots
verifying-grasps

Three independent checks on one grasp attempt -- that the hand arrived where it was sent before the jaws close, that the jaw gap after the close is not the mechanical stop, and that the lifted object is visible above the surface and near the hand. Use when a grasp closed on a small or thin object and the graph must know, before carrying or inserting, whether anything is actually in the gripper.

Overview

Publishergraph-robots
Repositoryopen-robot-skills
Skill nameverifying-grasps
Stars
58
Forks
8
Bundled files
3
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by graph-robots on GitHub. Read the source before you install it.

Installation

Install the Verifying Grasps AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/graph-robots/open-robot-skills/tree/main/skills/verifying-grasps
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/graph-robots/open-robot-skills.git /tmp/open-robot-skills
mkdir -p .claude/skills
cp -r /tmp/open-robot-skills/skills/verifying-grasps .claude/skills/verifying-grasps
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verifying Grasps in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Verifying Grasps on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Verifying Grasps is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

verifying-grasps

Three instruments on one grasp attempt, at three different moments. A graph may use any of them alone; together they separate "the hand never got there" from "it got there and closed on nothing" from "it closed on something and dropped it", which a single check cannot.

whenscriptroutes
after the approach, before the closeverify_reachreached / missed
immediately after the closeverify_gripheld / empty / unknown
after a short liftverify_graspverified / not_held

verify_reach compares the commanded grasp pose with the observed one and routes missed when the distance exceeds the object's own smallest half-extent (clamped to floor_m..ceiling_m). The tolerance is the object's size rather than a constant because the question is whether the object can still be between the pads, and that is a question about the object. Cheap -- no camera, no motion -- and it fires before the close, when the graph can still do something about it.

verify_grip reads robot.get_gripper and converts through robot.describe_gripper's width_fit, routing empty when the gap is within empty_margin_m of the mechanical stop. Also cameraless. It reports expected_width_m alongside, which is context and not a test: a planner that grasps at a fitted line's centre may legitimately hold a wide part of the body.

verify_grasp raises the end effector by lift_m along world Z with a Cartesian move, takes a fresh observation, and looks for object_description (then marker_description, when given) first in the camera whose name contains wrist_camera_keyword and then in overhead_camera_name. The first segmentation at or above score_min is back-projected; the grasp is verified only when the cloud has at least min_points valid depth points, its median lies at least min_above_table_m above robot.describe_workspace().surface_z, and within max_hand_distance_m of the lifted hand. Every failed gate returns route: not_held with a reason.

score_min is deliberately low: a small held object occupies only a few dozen wrist pixels and true positives score in the hundredths. Confidence admits a candidate; geometry decides.

Boundaries

  • The lift in verify_grasp is the only motion any of the three makes; the object is neither released nor moved elsewhere.
  • No force or tactile reading is consulted. verify_grip uses the jaw gap, which every parallel hand reports; torque and slip belong to a connector's own grasp check when it has one.
  • Only a rig without a wrist camera raises, and only from verify_grasp. A missing input elsewhere routes the benign way -- reached, unknown -- because absent evidence is not evidence of a failed grasp.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Verifying Grasps AI skill do?

Three independent checks on one grasp attempt -- that the hand arrived where it was sent before the jaws close, that the jaw gap after the close is not the mechanical stop, and that the lifted object is visible above the surface and near the hand. Use when a grasp closed on a small or thin object and the graph must know, before carrying or inserting, whether anything is actually in the gripper.

Why use Verifying Grasps on TypingMind?

Because you install it once and use it with any model. Verifying Grasps is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Verifying Grasps in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/graph-robots/open-robot-skills/tree/main/skills/verifying-grasps. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Verifying Grasps?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Verifying Grasps?

As many as you like. As long as a model supports skills, you can use Verifying Grasps with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Verifying Grasps AI skill free?

Yes. It is published on GitHub by graph-robots under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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