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Grasping Concave Contour

Organization
graph-robots
grasping-concave-contour

Grasp an awkward rigid part by the waists in its own silhouette — take the alpha shape of its point cloud from above, find where that boundary turns inward, and close the jaws across a pair of those inward runs. Use when a part has no graspable long axis and no pair of parallel walls an OBB would find — pliers and scissors gripped at the joint, a spool at its neck, a gear between two teeth, a bulb socket at its skirt. Use grasping-linear-feature instead for handles, rods and shafts, and grasping-short-axis when an upright box is enough.

Overview

Publishergraph-robots
Repositoryopen-robot-skills
Skill namegrasping-concave-contour
Stars
58
Forks
8
Bundled files
1
LicenseApache-2.0
Links
Skill sourceRepository
  • 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 Grasping Concave Contour 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/grasping-concave-contour
  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/grasping-concave-contour .claude/skills/grasping-concave-contour
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Grasping Concave Contour 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 Grasping Concave Contour 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 Grasping Concave Contour 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.

Grasping by the concavities in a silhouette

A parallel jaw wants two opposing surfaces. For a handle or a rod, the pair is implied by the long axis and grasping-linear-feature finds it. For a part with no long axis — a pair of pliers, a spur gear, a wire spool, a bulb socket — the opposing surfaces are the waists: the places where the outline turns inward. This skill finds them directly.

What the script does

  1. Projects the segmented cloud to the (x, y) plane the part is seen from.
  2. Builds the alpha shape of that projection — a Delaunay triangulation filtered to triangles whose circumradius is under alpha_radius, whose boundary is the part's real outline rather than its hull.
  3. Walks that boundary and keeps the reflex vertices, where it turns inward. Consecutive reflex vertices are one run; each run's centroid is one candidate pad site.
  4. Pairs the candidates, keeps the pairs whose secant fits the gripper's span, and builds a straight-down pose centred on each pair's midpoint with the jaws closing along the secant.
  5. Ranks by confidence and returns the best, with the rest in candidates so a caller that fails one can try the next without recomputing.

Where it fits

It is a proposer that also poses: it returns grasp_pose and pregrasp_pose directly rather than handing a list to a selector. Put verifying-grasps after it — this skill exits posed with the hand open and does not close it — and route failed to whatever recovery the graph has, because a part whose silhouette has no fitting pair of waists is one this skill genuinely cannot take.

A sensible arrangement is to try grasping-linear-feature first and fall through to this on failed: most parts have an axis, and the ones that do not are exactly the ones this skill is for.

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 Grasping Concave Contour AI skill do?

Grasp an awkward rigid part by the waists in its own silhouette — take the alpha shape of its point cloud from above, find where that boundary turns inward, and close the jaws across a pair of those inward runs. Use when a part has no graspable long axis and no pair of parallel walls an OBB would find — pliers and scissors gripped at the joint, a spool at its neck, a gear between two teeth, a bulb socket at its skirt. Use grasping-linear-feature instead for handles, rods and shafts, and grasping-short-axis when an upright box is enough.

Why use Grasping Concave Contour on TypingMind?

Because you install it once and use it with any model. Grasping Concave Contour 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 Grasping Concave Contour in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/graph-robots/open-robot-skills/tree/main/skills/grasping-concave-contour. 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 Grasping Concave Contour?

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 Grasping Concave Contour?

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

Is the Grasping Concave Contour 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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