Seating A Cable Crossing logo

Seating A Cable Crossing

Organization
graph-robots
seating-a-cable-crossing

Streams one planned crossing leg of a cable onto a routing station, lets the rod settle, then LOOKS — segments the rod, fits its centreline and counts how much of the curve lies inside the station's seat box on the side the route requires — and reports the fraction honestly rather than assuming the crossing took. It measures and does not correct. Use when a deformable-linear-object route is laid one crossing at a time and each crossing must be verified by vision before the next is planned.

Overview

Publishergraph-robots
Repositoryopen-robot-skills
Skill nameseating-a-cable-crossing
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 Seating A Cable Crossing 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/seating-a-cable-crossing
  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/seating-a-cable-crossing .claude/skills/seating-a-cable-crossing
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Seating A Cable Crossing 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 Seating A Cable Crossing 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 Seating A Cable Crossing 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.

seating-a-cable-crossing

Seat one crossing, then look, and hand on an honest number.

Why this is its own skill

A weave that streams its legs and hopes never finds out which crossing failed. The whole difference between a full solve and a third of one on the weave bench was one crossing that did not take and was never looked at again. This skill closes that loop for a single crossing: stream the planner's place leg, wait for the rod to come to rest, then segment the rod, fit its centreline and count the densified curve inside the station's seat box.

"Did not take" is a fraction, not a touch. The task scores containment as the share of the rod inside a box a few centimetres wide beside the station, on the side the route requires (>= 0.047 on the weave bench). Measured on the failures, a losing station is almost never on the wrong side: it is on the right side with too little of it there.

What it deliberately does not do

It measures and does not correct. The obvious correction — push the hand further onto the required side when the box is short — was built and measured and made the seat worse (0.016 to 0.000 after a 14 mm nudge): the cable is not short of the box, it is crossing it at an angle, and driving the contact point further in rotates it further. A correction wants a different move, arriving along the seat rather than across it, and that belongs in the carry planner.

Routing

The script always returns route: done; the verdict is in the outputs. seated is fraction >= seat_fraction; fraction is 0.0 when the seat could not be measured at all (no camera frame, no depth, no rod mask, a fit too short to be a curve) and detail says so. A graph that wants to branch on the verdict reads seated, not the exit. blocked is the exit a subgraph declares as on_error for a tool that raises during the place or the look.

Which station

The plan's station field names the station the planner actually worked, and that — not the loop's pass index — selects the seat box. The planner does not work stations in pass order (far-side crossings first, near-side seats last, each from the anchored end outwards), so indexing the perceived list by the pass number measured a different station's box from the one just worked on every pass. index is the fallback only when the plan carries no station.

Inputs and parameters

InputShape
planJSON: {"stages": [{"stage": "place", "trajectory": Trajectory, ...}], "station": int, "side": ±1} from the crossing planner
sceneJSON: {"stations": [[x, y], ...], "sides": [±1, ...]} from the fixture survey
indexthe pass number; used only when the plan names no station
arm_idthe work arm that streams the place leg

Parameters with the weave bench's numbers as defaults: seat_fraction (0.047), seat_inner / seat_outer (0.003 / 0.045 m out from the station on the required side), seat_half_y (0.035 m along the station line), camera ("cable"), rod_query ("thin white cable"), curve_nodes (42). Read a different bench's off its own task document rather than reusing them. grip_open_m / grip_close_m are accepted for uniform binding and unused.

What a CPU test can and cannot exercise

The fraction arithmetic (densification, the side sign, the box bounds) and the plan-station indexing run against canned observations and a canned centreline fit. What a CPU test cannot exercise is the perception behind sam3.segment_text and curve.fit_centerline on a real frame, and whether a streamed place leg actually lays cable where the plan said.

Required end states

End stateMeaning
doneStreamed and measured; read seated / fraction / detail.
blockedA tool raised; the subgraph's on_error.

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 Seating A Cable Crossing AI skill do?

Streams one planned crossing leg of a cable onto a routing station, lets the rod settle, then LOOKS — segments the rod, fits its centreline and counts how much of the curve lies inside the station's seat box on the side the route requires — and reports the fraction honestly rather than assuming the crossing took. It measures and does not correct. Use when a deformable-linear-object route is laid one crossing at a time and each crossing must be verified by vision before the next is planned.

Why use Seating A Cable Crossing on TypingMind?

Because you install it once and use it with any model. Seating A Cable Crossing 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 Seating A Cable Crossing in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/graph-robots/open-robot-skills/tree/main/skills/seating-a-cable-crossing. 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 Seating A Cable Crossing?

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 Seating A Cable Crossing?

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

Is the Seating A Cable Crossing 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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