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.
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 benchflow-ai/skillsbench --skill motor-model-dynamicsgit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/motor-model-dynamics)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00059 | $0.01037 |
| Opus 5 | $0.00030 | $0.00518 |
| Sonnet 5 | $0.00012 | $0.00207 |
| Haiku 4.5 | $0.00006 | $0.00104 |
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.
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:
- Motor model — maps desired
[F, Mx, My, Mz]→ desired motor RPMs → applies first-order lag → returns actual thrust and moments - 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
- Build the 4×4
prop_matrixfromcT,cQ, andd. - Solve
prop_matrix @ rpm_sq = [F, Mx, My, Mz]forrpm_sq(desired squared RPMs). - Take
sqrtof each element (clamp negatives to 0 first), then clip to[rpm_min, rpm_max]. - Apply first-order motor lag:
rpm_dot = km * (rpm_desired - rpm_current). - 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:
- Position derivative = current velocity (
state[3:6]). - Velocity derivative = gravity + thrust projected into world frame via ZYX Euler rotation. The x/y accelerations depend on
F/m,sin/cosof all three Euler angles. The z acceleration is−g + (F/m)·cos(φ)·cos(θ). - Euler angle derivative = current angular velocity (
state[9:12]). - Angular velocity derivative =
I⁻¹ M(solve inertia matrix against moment vector). - Motor RPM derivative =
rpm_motor_dotfrom the motor model.
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.
- 7d ago First seen · 79 lines · 59 tokens per session scan A f4e773904e97
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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