Anchoring A Free End logo

Anchoring A Free End

Organization
graph-robots
anchoring-a-free-end

Pins one end of a cable with the support hand before any crossing is attempted — asks the support-hand planner for the anchor grasp as joint-space legs, refuses it when its worst IK error is past the limit, streams the legs, closes the jaws to the plan's own width and dwells for the fingers' travel, and carries the solved chain and grasp orientation out on the exit so later pay-outs seed from them. Use when a deformable linear object must be held at one end so a sideways pull bends it instead of dragging the whole thing.

Overview

Publishergraph-robots
Repositoryopen-robot-skills
Skill nameanchoring-a-free-end
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 Anchoring A Free End 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/anchoring-a-free-end
  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/anchoring-a-free-end .claude/skills/anchoring-a-free-end
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Anchoring A Free End 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 Anchoring A Free End 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 Anchoring A Free End 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.

anchoring-a-free-end

Take the cable's far end with the support hand, before any crossing.

Why this is its own skill

Why it matters is a property of the material, not of the plan. A cable held at one end and pulled sideways bends; a free cable translates. Without an anchor the first crossing hauls the whole cable toward one station and the next has nothing left to grasp — measured, 26 of 42 segments piled at a single station. Every later crossing then works on a cable that has already moved out from under the model that was measured.

Failure is fatal here in a way a missed look is not. A weave without an anchor is not a degraded weave, it is a different and much worse task, so the skill routes unanchored for a graph to abort on rather than carrying on and reporting the result as a policy failure.

What it needs from the connector

This skill requires a connector that registers cable.plan_support, a privileged support-hand planner that reads the true rod state and returns the anchor grasp as joint-space legs (stages), the hand that owns them (arm_id, arm_name), the close width (close), the end joint state (q_end), the grasp orientation (quat), the rod segment taken and the IK error of the worst waypoint (worst_grasp_error) — in simulation the cable-routing suite's connector provides it. It also needs robot.set_grip (a ramped jaw command in metres, addressed by arm_id) and robot.wait_steps (hold still while the scene runs on). The generic connector registers none of the three, which is why the bundle is sim-only today.

Recommended subgraph state flow

text
anchor_far_end ──anchored──▶ (the first crossing)
       └──unanchored──▶ abort
  1. Ask cable.plan_support(approach=) for the grasp.
  2. Refuse when there are no legs or worst_grasp_error exceeds 50 mm — past that the chain is in a different basin and what streams is a whip with the cable near the jaws.
  3. Stream each leg with robot.execute_trajectory on the plan's arm_id.
  4. robot.set_grip(width_m=plan.close, arm_id=plan.arm_id), then robot.wait_steps for the jaws' own travel: a finger command is a target, not a state, and the first crossing must not start while the pads are still moving.
  5. Return the solved chain (q_support) and grasp orientation (quat) as JSON lists: every later pay-out seeds its IK from them, and re-deriving them per crossing would let the support chain drift into a different basin mid-weave.

Inputs and outputs

The script takes one parameter, approach (m above the grasp the enter leg starts from; 0.055). Outputs: segment (the rod segment held; -1 when refused), worst_error_m, and q_support / quat as JSON strings ("null" when refused).

What a CPU test can and cannot exercise

The refusal gate and the call order (plan, stream each leg, close on the plan's width and hand, dwell) run against a canned plan. What a CPU test cannot exercise is the planner behind cable.plan_support — whether the legs it returns reach the rod — nor whether the pinch actually holds under the first crossing's pull.

Required end states

End stateMeaning
anchoredThe far end is held; segment, q_support, quat bound.
unanchoredRefused before anything moved. Abort the weave.

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 Anchoring A Free End AI skill do?

Pins one end of a cable with the support hand before any crossing is attempted — asks the support-hand planner for the anchor grasp as joint-space legs, refuses it when its worst IK error is past the limit, streams the legs, closes the jaws to the plan's own width and dwells for the fingers' travel, and carries the solved chain and grasp orientation out on the exit so later pay-outs seed from them. Use when a deformable linear object must be held at one end so a sideways pull bends it instead of dragging the whole thing.

Why use Anchoring A Free End on TypingMind?

Because you install it once and use it with any model. Anchoring A Free End 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 Anchoring A Free End in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/graph-robots/open-robot-skills/tree/main/skills/anchoring-a-free-end. 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 Anchoring A Free End?

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 Anchoring A Free End?

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

Is the Anchoring A Free End 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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