robo-diferencial-e-sensores

robo-diferencial-e-sensores is a skill for Claude Code from KevinInoCol/coppeliasim-mcp. It costs 118 tokens per session (1,376 once invoked), scanned A, original, MIT.

A 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/robo-diferencial-e-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 robo-diferencial-e-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 robo-diferencial-e-sensores

README.md
[![agentmods](https://agentmods.dev/badge/skills/kevininocol/coppeliasim-mcp/robo-diferencial-e-sensores/github.svg)](https://agentmods.dev/skills/kevininocol/coppeliasim-mcp/robo-diferencial-e-sensores)
Your own site
<a href="https://agentmods.dev/skills/kevininocol/coppeliasim-mcp/robo-diferencial-e-sensores"><img src="https://agentmods.dev/badge/skills/kevininocol/coppeliasim-mcp/robo-diferencial-e-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 robo-diferencial-e-sensores

Your own site · 80×15
<a href="https://agentmods.dev/skills/kevininocol/coppeliasim-mcp/robo-diferencial-e-sensores"><img src="https://agentmods.dev/badge/skills/kevininocol/coppeliasim-mcp/robo-diferencial-e-sensores.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 118 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,376 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 $0.00118 $0.01376
Opus 5 $0.00059 $0.00688
Sonnet 5 $0.00024 $0.00275
Haiku 4.5 $0.00012 $0.00138

Measured 11d ago against content hash 8881897b04f4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

robo-diferencial-e-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/robo-diferencial-e-sensores/SKILL.md · 116 lines

How it starts

The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Robô diferencial e sensores

Cada armadilha desta página custa horas. Todas foram medidas num robô que funciona.

Juntas: o eixo e o modo de controle

Uma junta atua ao longo do próprio +Z. Para uma roda que impulsiona para +X, o eixo precisa ficar sobre Y, o que significa girar a junta inteira (-pi/2 em torno de X). Não é parâmetro da chamada: é a orientação do objeto.

Uma junta sem modo de controle é surda. Você manda velocidade e nada acontece até definir dynCtrlMode, que é uma propriedade, não um argumento da criação. No modo de velocidade o valor é 4 (não 2, que é o que se supõe):

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

A tool crear_junta do MCP faz as duas coisas por você.

Atrito: o Bullet lê frictionOld

O Bullet 2.7, que é o motor padrão, obedece bullet.frictionOld, não bullet.friction. O CoppeliaSim expõe os dois e qual manda depende da versão do Bullet escolhida na cena. Definir só bullet.friction não faz absolutamente nada.

Medido num diferencial real: uma roda boba com o atrito velho em 1 arrastou o robô para 87% da distância em reta e 51% do giro, patinando em vez de pivotar sobre o eixo motriz. Escreva sempre os dois.

Roda boba: atrito bem baixo. Rodas motrizes: atrito alto.

Massa e inércia

computeMassAndInertia só funciona com formas convexas. Não funda o chassi com a carga e depois chame: deixe cada peça convexa separada e calcule a massa de cada uma.

Sem massa razoável, um robô leve com rodas finas treme ou sai voando.

Rodas finas

Rodas de 1 cm de espessura são pouquíssimo para o Bullet: o contato é quase uma linha e o robô treme. Compense com atrito alto e massa suficiente. Se o robô vibra parado, trace a altura do chassi ao longo do tempo — se oscila, é isso.

Grupos de colisão

As peças do robô não devem colidir entre si, mas devem colidir com o mundo. A máscara de respondable resolve as duas coisas: os 8 bits baixos dizem com quem colide dentro da mesma árvore e os 8 altos com o resto. Com 0xFF00 as peças do robô se ignoram — acabaram os tremores — e continuam batendo no chão e nos obstáculos.

Read the full file on GitHub · 116 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 · 116 lines · 118 tokens per session scan A 8881897b04f4

Subscribe to this mod's changes

robo-diferencial-e-sensores is a skill published in the GitHub repository KevinInoCol/coppeliasim-mcp (0 stars, last pushed 16d ago), licensed MIT. It adds 118 tokens to every session and 1,376 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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