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 plot-quadrotorgit 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/plot-quadrotor)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/plot-quadrotor"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/plot-quadrotor/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/plot-quadrotor"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/plot-quadrotor.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 | $0.00062 | $0.00708 |
| Opus 5 | $0.00031 | $0.00354 |
| Sonnet 5 | $0.00012 | $0.00142 |
| Haiku 4.5 | $0.00006 | $0.00071 |
Grade A, and why
plot-quadrotor 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 9d 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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Quadrotor Simulation Plotter
Overview
Given actual and desired state matrices from a simulation run, generates three figures and saves them as PNG files.
Input Format
state : (15 x n) numpy array — actual drone state over time
state_des : (15 x n) numpy array — desired drone state over time
time_vec : (n,) numpy array — time axis in seconds
State matrix row layout:
| Rows | Content |
|---|---|
| 0:3 | Position [x, y, z] |
| 3:6 | Velocity [vx, vy, vz] |
| 6:9 | Orientation [φ, θ, ψ] |
| 9:12 | Angular velocity [p, q, r] |
| 12:15 | Acceleration [ax, ay, az] |
Three Figures Produced
| Figure | File | Content |
|---|---|---|
| 1 | {save_dir}/desired_vs_actual.png |
Blue (desired) vs red (actual) overlay for all 5 groups |
| 2 | {save_dir}/errors.png |
Instantaneous error = actual − desired |
| 3 | {save_dir}/cumulative_errors.png |
`time_step × cumsum( |
Plots are written to the save_dir argument passed by the caller (e.g. /root/results/001/plots). The function must not hardcode any path.
Implementation Logic
- Read
sample_ratefrom/root/system_params.yamland derivetime_step = 1 / sample_rate. - Slice
stateandstate_desinto 5 groups (pos, vel, orientation, angular velocity, acceleration) of 3 rows each. - For each group, compute
error = actual − desiredandcumulative = time_step * cumsum(|error|). - Create three figures, each with a 5×3 subplot grid (5 groups × 3 axes):
- Figure 1: overlay desired (blue) and actual (red) signals per axis.
- Figure 2: plot instantaneous error per axis.
- Figure 3: plot cumulative absolute error per axis.
- Call
os.makedirs(save_dir, exist_ok=True), then save each figure withfig.savefig(...)and close it withplt.close(fig).
Key Details
time_stepis not hardcoded — always readsample_ratefromsystem_params.yamland derivetime_step = 1 / sample_rate.- Cumulative error uses
time_step * np.cumsum(np.abs(error))to give units of[unit × seconds]. - Use
figsize=(16, 20)for 5×3 subplot grids to prevent label overlap. - LaTeX strings for orientation labels:
r'$\phi$',r'$\theta$',r'$\psi$'.
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.
- 9d ago First seen · 57 lines · 62 tokens per session scan A d02c76fb625a
plot-quadrotor is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 62 tokens to every session and 708 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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