Verifying A Cable Route logo

Verifying A Cable Route

Organization
graph-robots
verifying-a-cable-route

Judge a finished cable route from a camera rather than from an evaluator — re-read the cable's centreline from the clear-view camera (tracking the carried prior when the support hand still hides one end), test that rod material lies on the owed side of every station within the seat's reach, and that the hand has left the rod. Use when a routing policy must decide for itself whether every crossing is seated and it is safe to stop, and report margins so a disagreement with a scorer is diagnosable.

Overview

Publishergraph-robots
Repositoryopen-robot-skills
Skill nameverifying-a-cable-route
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 Verifying A Cable Route 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/verifying-a-cable-route
  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/verifying-a-cable-route .claude/skills/verifying-a-cable-route
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Verifying A Cable Route 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 Verifying A Cable Route 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 Verifying A Cable Route 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.

verifying-a-cable-route

Is every station seated, and am I off the rod — answered from a frame, with margins.

Why this is its own skill

A policy that can only know it is finished by asking a simulator's evaluator is not a policy that could run in a cell. The route's success criteria are visible — rod material in a box beside each post, a hand some distance from the rod — and this skill measures exactly those, on the same stations the plan was built on, with the same centreline tools the routing loop used. It is a judgement, not a score: it does not reproduce a scorer's containment arithmetic (agreeing with a number one cannot see is how a graph ends up disagreeing with the thing the number was about) but it reports its own margins so any disagreement is diagnosable.

Measured on a three-spool bench: one disagreement with the evaluator in twelve episodes, and that one was the old pixel-counting test passing on a stray fleck of mask — the reason the seat clause now counts a fraction of the densified centreline (the goal's own 0.047) instead.

When to use

  • The last crossing has been released and the work hand has retreated; the cable camera has a clear view of the route.
  • The plan's scene (stations along +y, the side each owes) is available — the stations are not re-detected, because a second independent detection is a second chance to mis-order the posts.

When NOT to use

  • Mid-route: with a hand on the rod the release clause cannot pass and a short fit is likely. Verify once, at the end.
  • A route whose success is not "rod on a side of each post" (a wrap count, a plug in a port) — measure that instead.

State flow

text
scene ──► stations + sides (the plan's)
frame ──► rod mask ──► cold fit
              │ whole rod (≥ whole_rod × len(prior), or no prior) ──► use it
              │ short and a prior exists ──► track the prior onto the frame
              ▼
resample every sample_step ──► per station: rod abreast (|Δy| ≤ seat_band),
      offset onto the owed side in [seat_inner, seat_reach], ≥ seat_fraction of the rod?
hand: min distance from the rod > clear_m?
all seated and clear ──► woven, else short

The support hand is usually still holding the far end when this runs, and a cold fit truncated there does not say it is truncated — it reports a station with no rod. Measured: the station nearest the support grip came back no-rod in every episode of one layout, including ones the evaluator scored 3 of 4. Tracking the carried prior onto the frame carries that end through.

Per station the outcome is one of seated, wrong-side (the best point is on the far side of the post; the signed margin is negative), outside-box (right side, beyond the seat's reach) or no-rod (nothing abreast; margin is NaN). A previous revision read a wrong-side margin as a near-miss and a build was spent pressing harder on a cable on the far side of its post.

Inputs

  • scene — the survey JSON from perceiving-routing-fixtures; stations and sides are read.
  • prior — the centreline the loop carries (JSON text: a list of [x, y, z] points, or a Centerline object with points); empty when there is none, in which case the cold fit is used as is.
  • camera ("cable", 0.50 mm/px against the overhead's 1.75), rod_query ("thin white cable"), rod_score (0.20), curve_nodes (42), whole_rod (0.80), sample_step (0.002 m).
  • Goal-clause constants, defaults from the goal this was written against: seat_reach (0.045, the box's outer edge), seat_inner (0.003, its inner edge — rod pressed against the post is not in the box), seat_band (0.035, the box's half-extent along the run), seat_fraction (0.047 of the rod), clear_m (0.05, the tcp-distance clause).

Outputs

  • exit — woven | short. The router field is exit, not route; wire the subgraph's exit on it.
  • success — the same verdict as a bool.
  • seated — one bool per station, in scene order (stated here rather than under produces_outputs because the type registry names no bare list of bools).
  • worst_margin_m — the smallest finite signed margin across stations (negative: wrong side); 0.0 when none is finite.
  • tcp_clear_m — the hand's distance from the nearest rod sample.
  • detail — "k/n seated (need of N nodes each): seated, wrong-side, …; hand D mm off the rod".

Required end states

End stateMeaning
wovenEvery station seated on its owed side and the hand is clear. Stop.
shortSomething is not: read detail and seated for which station and why.

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 Verifying A Cable Route AI skill do?

Judge a finished cable route from a camera rather than from an evaluator — re-read the cable's centreline from the clear-view camera (tracking the carried prior when the support hand still hides one end), test that rod material lies on the owed side of every station within the seat's reach, and that the hand has left the rod. Use when a routing policy must decide for itself whether every crossing is seated and it is safe to stop, and report margins so a disagreement with a scorer is diagnosable.

Why use Verifying A Cable Route on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/graph-robots/open-robot-skills/tree/main/skills/verifying-a-cable-route. 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 Verifying A Cable Route?

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 Verifying A Cable Route?

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

Is the Verifying A Cable Route 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇