Computing Feature Mating Poses logo

Computing Feature Mating Poses

Organization
graph-robots
computing-feature-mating-poses

Computes approach, engaged, and mate TCP poses from a held feature in the hand and a fixture feature, resolving free mate symmetries from the current robot pose without executing motion. Use when a held loop must go over a shaft or hook, or a held shaft or tip must enter an aperture, and explicit approach, engaged, and mate poses are needed for planning.

Overview

Publishergraph-robots
Repositoryopen-robot-skills
Skill namecomputing-feature-mating-poses
Stars
58
Forks
8
Bundled files
1
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.

  • 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 Computing Feature Mating Poses 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/computing-feature-mating-poses
  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/computing-feature-mating-poses .claude/skills/computing-feature-mating-poses
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Computing Feature Mating Poses 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 Computing Feature Mating Poses 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 Computing Feature Mating Poses 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.

computing-feature-mating-poses

This skill computes geometry only. When a relation has a free rotational degree of freedom, it selects the equivalent mate closest to the current TCP orientation to avoid arbitrary or unreachable reorientation. Its pre-contact standoff encloses the complete attached-object sphere projection along the fixture axis, preventing long held bodies from sweeping into the fixture.

When to use

  • A held loop or ring must be placed over a shaft, peg, or hook.
  • A held shaft or tip must enter an aperture.
  • The mating pose has rotational symmetry that should be resolved from the current robot pose.

Use loop_over_shaft for a loop or ring placed over a peg or hook. Use shaft_into_aperture for a shaft entering an opening; tip_through_aperture and insert_through are accepted aliases. The skill checks that the relevant inner and outer dimensions have sufficient clearance: for loop_over_shaft the held radius_inner against the fixture radius_outer, for apertures the held radius_outer against the fixture radius_inner.

For loop_over_shaft, describe the fixture at its distal tip with an axis pointing outward from the mounting surface, radius_outer, and the observed usable_length from tip toward the mount. The skill uses these dimensions to center the shaft inside the loop during crossing, travel to a stable interior shaft position, and then seat the loop without requiring an object-specific target point. crossing_lift_m (default 0) raises the approach and engaged poses vertically by that amount after centering, so a long held body that settles in the hand between observation and crossing still clears the tip; the mate pose is unaffected. It applies when usable_length is declared.

For apertures, the fixture axis points into the opening. The approach is mirrored onto the free side of the mate, and insertion_depth (when the fixture declares it) moves the engaged and mate poses that far into the opening.

Outputs

  • approach_pose is a collision-free standoff before fixture engagement.
  • engaged_pose crosses the distal tip far enough for the loop to surround the shaft, or enters the aperture.
  • mate_pose places the loop farther along the usable shaft and seats it, or seats the shaft at the requested depth.
  • seating_distance and minimum_clearance describe the computed mate.

Boundaries

  • This skill computes poses but does not plan or execute robot motion.
  • Inputs describe functional geometry and a typed relation, not object names. Object-specific corrections (a settling offset measured for one tool) enter through crossing_lift_m, supplied by the graph.
  • Execute the result with planning-held-object-motion and the corresponding motion-execution skills.

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 Computing Feature Mating Poses AI skill do?

Computes approach, engaged, and mate TCP poses from a held feature in the hand and a fixture feature, resolving free mate symmetries from the current robot pose without executing motion. Use when a held loop must go over a shaft or hook, or a held shaft or tip must enter an aperture, and explicit approach, engaged, and mate poses are needed for planning.

Why use Computing Feature Mating Poses on TypingMind?

Because you install it once and use it with any model. Computing Feature Mating Poses 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 Computing Feature Mating Poses in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/graph-robots/open-robot-skills/tree/main/skills/computing-feature-mating-poses. 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 Computing Feature Mating Poses?

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 Computing Feature Mating Poses?

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

Is the Computing Feature Mating Poses 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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