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.
npx skills add KevinInoCol/coppeliasim-mcp --skill differential-robot-and-sensorsgit clone --depth 1 https://github.com/KevinInoCol/coppeliasim-mcpWrote 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.
[](https://agentmods.dev/skills/kevininocol/coppeliasim-mcp/differential-robot-and-sensors)<a href="https://agentmods.dev/skills/kevininocol/coppeliasim-mcp/differential-robot-and-sensors"><img src="https://agentmods.dev/badge/skills/kevininocol/coppeliasim-mcp/differential-robot-and-sensors/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.
<a href="https://agentmods.dev/skills/kevininocol/coppeliasim-mcp/differential-robot-and-sensors"><img src="https://agentmods.dev/badge/skills/kevininocol/coppeliasim-mcp/differential-robot-and-sensors.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00095 | $0.01205 |
| Opus 5 | $0.00048 | $0.00602 |
| Sonnet 5 | $0.00019 | $0.00241 |
| Haiku 4.5 | $0.00010 | $0.00120 |
Grade A, and why
differential-robot-and-sensors 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.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Differential drive robot and sensors
Every trap on this page costs hours. All of them were measured on a robot that works.
Joints: axis and control mode
A joint acts along its own +Z. For a wheel driving toward +X, the axis must
lie along Y, which means rotating the whole joint (-pi/2 about X). It is not a
call parameter: it is the object's orientation.
A joint with no control mode is deaf. Send it a velocity and nothing
happens until dynCtrlMode is set — a property, not an argument of the
creation call. For velocity control the value is 4 (not 2, which is what you
would guess):
sim.setInt32Property(joint, "dynCtrlMode", sim.jointdynctrl_velocity)
sim.setJointTargetVelocity(joint, 3.0)
The MCP's crear_junta tool does both for you.
Friction: Bullet reads frictionOld
Bullet 2.7, the default engine, obeys bullet.frictionOld, not
bullet.friction. CoppeliaSim exposes both and which one wins depends on the
Bullet version selected in the scene. Setting only bullet.friction does
nothing at all.
Measured on a real differential drive: a caster left at old-friction 1 dragged the robot down to 87% of its straight-line distance and 51% of its turn rate, skid-steering instead of pivoting on its drive axle. Always write both.
Caster: very low friction. Drive wheels: high friction.
Mass and inertia
computeMassAndInertia only works on convex shapes. Do not merge the
chassis with the payload and then call it: keep each piece convex on its own and
compute each mass separately.
Without sensible mass, a light robot on thin wheels jitters or gets flung.
Thin wheels
Wheels 1 cm thick are very little for Bullet: the contact is almost a line and the robot shakes. Compensate with high friction and enough mass. If the robot vibrates while standing still, trace the chassis height over time — if it oscillates, this is why.
Collision groups
The robot's parts must not collide with each other, but must collide with the
world. The respondable mask does both at once: the low 8 bits say who it hits
within the same tree, the high 8 bits who it hits outside. With 0xFF00 the
robot's parts ignore each other — no more jitter — and still hit the floor and
the obstacles.
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.
- 11d ago First seen · 117 lines · 95 tokens per session scan A bee0e597f45d
differential-robot-and-sensors is a skill published in the GitHub repository KevinInoCol/coppeliasim-mcp (0 stars, last pushed 16d ago), licensed MIT. It adds 95 tokens to every session and 1,205 once invoked, about $0.0005 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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