robot-diferencial-y-sensores

robot-diferencial-y-sensores is a skill for Claude Code from KevinInoCol/coppeliasim-mcp. It costs 117 tokens per session (1,397 once invoked), scanned A, original, MIT.

A Spanish-language guide to building a differential-drive robot in CoppeliaSim, a robotics simulator, with working wheels and proximity sensors that detect nearby objects.

In plain words
What is it for?
Use it when assembling a mobile robot, configuring wheel joints and physics, setting collision groups, or adding and debugging proximity sensors.
Why use it?
It explains setup details that can otherwise leave the robot motionless, slipping, colliding incorrectly, or sensing the floor instead of obstacles.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the coppeliasim plugin — 9 skills, 1 MCP server shipped together

Good fit Use it when assembling a mobile robot, configuring wheel joints and physics, setting collision groups, or adding and debugging proximity sensors.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/kevininocol/coppeliasim-mcp/robot-diferencial-y-sensores
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add KevinInoCol/coppeliasim-mcp --skill robot-diferencial-y-sensores
Clone the repo
git clone --depth 1 https://github.com/KevinInoCol/coppeliasim-mcp

Made for: Claude Code.

Or install coppeliasim, the plugin that ships this one along with the rest of its 9 skills, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for robot-diferencial-y-sensores

README.md
[![agentmods](https://agentmods.dev/badge/skills/kevininocol/coppeliasim-mcp/robot-diferencial-y-sensores/github.svg)](https://agentmods.dev/skills/kevininocol/coppeliasim-mcp/robot-diferencial-y-sensores)
Your own site
<a href="https://agentmods.dev/skills/kevininocol/coppeliasim-mcp/robot-diferencial-y-sensores"><img src="https://agentmods.dev/badge/skills/kevininocol/coppeliasim-mcp/robot-diferencial-y-sensores/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for robot-diferencial-y-sensores

Your own site · 80×15
<a href="https://agentmods.dev/skills/kevininocol/coppeliasim-mcp/robot-diferencial-y-sensores"><img src="https://agentmods.dev/badge/skills/kevininocol/coppeliasim-mcp/robot-diferencial-y-sensores.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 117 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,397 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00117 $0.01397
Opus 5 $0.00059 $0.00698
Sonnet 5 $0.00023 $0.00279
Haiku 4.5 $0.00012 $0.00140

Measured 11d ago against content hash 338bffd4faff, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

robot-diferencial-y-sensores scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/robot-diferencial-y-sensores/SKILL.md · 118 lines

How it starts

The opening of the file, as written. The whole thing — 118 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Robot diferencial y sensores

Las trampas de esta página cuestan horas cada una. Todas están medidas sobre un robot que funciona.

Juntas: el eje y el modo de control

Una junta actúa a lo largo de su propio +Z. Para una rueda que impulsa hacia +X, el eje tiene que quedar sobre Y, lo que significa rotar la junta entera (-pi/2 alrededor de X). No es un parámetro de la llamada: es la orientación del objeto.

Una junta sin modo de control es sorda. Aunque le mandes velocidad, no se mueve hasta que se le pone dynCtrlMode, que es una propiedad, no un argumento de la creación. Con el modo de velocidad, el valor es 4 (no 2, que es lo que uno supondría):

sim.setInt32Property(junta, "dynCtrlMode", sim.jointdynctrl_velocity)
sim.setJointTargetVelocity(junta, 3.0)

Si usas la tool crear_junta del MCP, hace las dos cosas por ti.

Fricción: Bullet lee frictionOld

Bullet 2.7, que es el motor por defecto, obedece bullet.frictionOld, no bullet.friction. CoppeliaSim expone las dos y cuál manda depende de la versión de Bullet elegida en la escena. Poner solo bullet.friction no hace absolutamente nada.

Medido sobre un diferencial real: una rueda loca con la fricción vieja en 1 arrastró al robot al 87% de su distancia en recta y al 51% de su giro, patinando en vez de pivotar sobre el eje motriz. Escribe siempre las dos.

Rueda loca: fricción muy baja. Ruedas motrices: fricción alta.

Masa e inercia

computeMassAndInertia solo funciona con formas convexas. No fusiones el chasis con la carga y luego lo llames: deja cada pieza convexa por separado y calcula la masa de cada una.

Sin masa razonable, un robot ligero con ruedas finas tiembla o sale despedido.

Ruedas finas

Las ruedas de 1 cm de espesor son poquísimo para Bullet: el contacto es casi una línea y el robot tiembla. Se compensa con fricción alta y masa suficiente. Si el robot vibra parado, mide la altura del chasis a lo largo del tiempo — si oscila, es esto.

Read the full file on GitHub · 118 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 118 lines · 117 tokens per session scan A 338bffd4faff

Subscribe to this mod's changes

robot-diferencial-y-sensores is a skill published in the GitHub repository KevinInoCol/coppeliasim-mcp (0 stars, last pushed 16d ago), licensed MIT. It adds 117 tokens to every session and 1,397 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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