motor-model-dynamics

motor-model-dynamics is a skill for Claude Code, Codex from benchflow-ai/skillsbench. It costs 59 tokens per session (1,037 once invoked), scanned A, original, Apache-2.0.

A physics model for a quadrotor that converts total thrust and turning forces into individual motor speeds, models motor delay, and simulates the drone’s movement over time.

In plain words
What is it for?
Use it to simulate quadrotor motors and nonlinear flight dynamics with numerical integration.
Why use it?
It accounts for the fact that motors do not change speed instantly and that their forces affect both movement and rotation.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to simulate quadrotor motors and nonlinear flight dynamics with numerical integration.

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Install with agentmods
npx agentmods add skills/benchflow-ai/skillsbench/motor-model-dynamics
About the project

SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.

benchflow-ai/skillsbench · 1,760 stars · on GitHub · skillsbench.ai

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 benchflow-ai/skillsbench --skill motor-model-dynamics
Clone the repo
git clone --depth 1 https://github.com/benchflow-ai/skillsbench

Made for: Claude Code, Codex.

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 motor-model-dynamics

README.md
[![agentmods](https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/motor-model-dynamics/github.svg)](https://agentmods.dev/skills/benchflow-ai/skillsbench/motor-model-dynamics)
Your own site
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/motor-model-dynamics"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/motor-model-dynamics/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 motor-model-dynamics

Your own site · 80×15
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/motor-model-dynamics"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/motor-model-dynamics.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 59 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,037 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. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00059 $0.01037
Opus 5 $0.00030 $0.00518
Sonnet 5 $0.00012 $0.00207
Haiku 4.5 $0.00006 $0.00104

Measured 7d ago against content hash f4e773904e97, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

motor-model-dynamics 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 7d 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.

tasks/drone-planning-control/environment/skills/motor-model-dynamics/SKILL.md · 79 lines

How it starts

The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Motor Model and Dynamics Simulation

Overview

Two modules form the physics layer of the simulator:

  1. Motor model — maps desired [F, Mx, My, Mz] → desired motor RPMs → applies first-order lag → returns actual thrust and moments
  2. Dynamics — nonlinear equations of motion; given forces and moments, returns the 16-element state derivative for ODE integration

Motor Model

Propeller Allocation Matrix (X-frame quadrotor)

The 4×4 allocation matrix maps [F, Mx, My, Mz] to squared motor RPMs. For an X-frame with arm length d, thrust coefficient cT, and torque coefficient cQ:

         motor:  1      2      3      4
Thrust:         +cT    +cT    +cT    +cT
Roll  (Mx):      0    +d·cT    0    -d·cT
Pitch (My):    -d·cT    0    +d·cT    0
Yaw   (Mz):    -cQ    +cQ    -cQ    +cQ

Implementation Logic

  1. Build the 4×4 prop_matrix from cT, cQ, and d.
  2. Solve prop_matrix @ rpm_sq = [F, Mx, My, Mz] for rpm_sq (desired squared RPMs).
  3. Take sqrt of each element (clamp negatives to 0 first), then clip to [rpm_min, rpm_max].
  4. Apply first-order motor lag: rpm_dot = km * (rpm_desired - rpm_current).
  5. Compute actual force/moment using prop_matrix @ (motor_rpm ** 2) and return (F_actual, M_actual, rpm_dot).

Dynamics (Equations of Motion)

State vector (16,)

Indices Meaning
0:3 Position [x, y, z]
3:6 Velocity [vẋ, vẏ, vż]
6:9 Euler angles [φ, θ, ψ]
9:12 Angular velocity [p, q, r]
12:16 Motor RPM [ω₁, ω₂, ω₃, ω₄]

Implementation Logic

Compute state_dot (16,) from current state and applied forces/moments:

  1. Position derivative = current velocity (state[3:6]).
  2. Velocity derivative = gravity + thrust projected into world frame via ZYX Euler rotation. The x/y accelerations depend on F/m, sin/cos of all three Euler angles. The z acceleration is −g + (F/m)·cos(φ)·cos(θ).
  3. Euler angle derivative = current angular velocity (state[9:12]).
  4. Angular velocity derivative = I⁻¹ M (solve inertia matrix against moment vector).
  5. Motor RPM derivative = rpm_motor_dot from the motor model.

Read the full file on GitHub · 79 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. 7d ago First seen · 79 lines · 59 tokens per session scan A f4e773904e97

Subscribe to this mod's changes

motor-model-dynamics is a skill published in the GitHub repository benchflow-ai/skillsbench (1,760 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 59 tokens to every session and 1,037 once invoked, about $0.0003 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 2026-09-03.

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