Proposing Short Axis Grasps

Organization

graph-robots

proposing-short-axis-grasps

Propose top-down grasps whose jaws close ACROSS an elongated object's short axis, derived from its oriented box. Use when the object is longer than it is wide, as the first grasp-proposal rung — a handle, a spanner, a screwdriver, a bar, a laid-down bottle — where closing along the length would put the fingers down the object and meet them on each other. Declines when the short axis is wider than the hand opens, which is the signal to try `proposing-side-grasps` rather than to re-tune this one.

Overview

Publisher	graph-robots
Repository	`open-robot-skills`
Skill name	`proposing-short-axis-grasps`
Stars	58
Forks	8
Bundled files	1
License	Apache-2.0
Links	Skill source Repository

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.
1 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 Proposing Short Axis Grasps AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/graph-robots/open-robot-skills/tree/main/skills/proposing-short-axis-grasps
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/graph-robots/open-robot-skills/tree/main/skills/proposing-short-axis-grasps

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/proposing-short-axis-grasps .claude/skills/proposing-short-axis-grasps

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Proposing Short Axis 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 Proposing Short Axis 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 Proposing Short Axis 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.

proposing-short-axis-grasps

One strategy for the propose stage of a pick: a top-down grasp across the narrow dimension of an elongated body.

Why this is its own skill

A pick has stages — propose, select, approach, close — and each has more than one strategy. Treating "grasping" as one indivisible thing is what leaves a policy with a single approach and nowhere to go when it does not fit: it can only jitter the numbers of a strategy that structurally cannot work. Proposing is separated from selecting so that a refusal here ("the short axis is wider than the hand") is a routable exit to a different rung, not a failed episode.

When to use

The target is longer than it is wide: a handle, spanner, screwdriver, bar.
Top-down access is available.
Any privilege tier. The box may come from sim.get_object_obb or from geometry.compute_obb over a segmented cloud — this skill cannot tell which, which is what makes it portable to the vision body.

When NOT to use

The top is blocked (a shelf, a lid) — use proposing-side-grasps.
The body is roughly cubic; there is no meaningful short axis to close across.
The graspable part is a concave interior (the inside of a ring, a pivot between two plates). A box has no opinion about concavity, and neither has this skill.

Recommended subgraph state flow

text
describe → get_obb → propose → frame → select → END

describe — tool: robot.describe_gripper (for the jaw span).
get_obb — tool: sim.get_object_obb, inputs: {object_name: …}. At camera_only/none, substitute geometry.filter_and_compute_obb.
propose — script: scripts/<sg>/propose_short_axis.py, inputs target_obb = Ref("get_obb.obb"), gripper = Ref("describe"). Router on route: proposed → frame, declined → the subgraph's declined exit.
frame — tool: robot.grasp_frame, inputs: {close_heading_deg: Ref("propose.close_heading_deg")}.
select — hand propose.positions and frame.rotation to selecting-reachable-grasp, which walks them.

Required end states

End state	Meaning
`proposed`	A heading and stations. Continue to select.
`declined`	The hand cannot span this object's short axis. Route to another propose rung — do not retry this one with different numbers.

Bundled files

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

scripts/propose_short_axis.py6 KB

Frequently asked questions

What does the Proposing Short Axis Grasps AI skill do?

Why use Proposing Short Axis Grasps on TypingMind?

Because you install it once and use it with any model. Proposing Short Axis 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 Proposing Short Axis Grasps in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/graph-robots/open-robot-skills/tree/main/skills/proposing-short-axis-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 Proposing Short Axis 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 Proposing Short Axis Grasps?

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

Is the Proposing Short Axis 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

proposing-short-axis-grasps

Why this is its own skill

When to use

When NOT to use

Recommended subgraph state flow

Required end states

Bundled files

Frequently asked questions

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