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 mpc-horizon-tuninggit 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/mpc-horizon-tuning)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/mpc-horizon-tuning"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/mpc-horizon-tuning/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/mpc-horizon-tuning"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/mpc-horizon-tuning.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 | $0.00018 | $0.00326 |
| Opus 5 | $0.00009 | $0.00163 |
| Sonnet 5 | $0.00004 | $0.00065 |
| Haiku 4.5 | $0.00002 | $0.00033 |
Grade A, and why
mpc-horizon-tuning 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- mpc-horizon-tuning — 100% identical, 0 lines differ
What it actually says
MPC Tuning for Tension Control
Prediction Horizon Selection
Horizon N affects performance and computation:
- Too short (N < 5): Poor disturbance rejection
- Too long (N > 20): Excessive computation
- Rule of thumb: N ≈ 2-3× settling time / dt
For R2R systems with dt=0.01s: N = 5-15 typical
Cost Matrix Design
State cost Q: Emphasize tension tracking
Q_tension = 100 / T_ref² # High weight on tensions
Q_velocity = 0.1 / v_ref² # Lower weight on velocities
Q = diag([Q_tension × 6, Q_velocity × 6])
Control cost R: Penalize actuator effort
R = 0.01-0.1 × eye(n_u) # Smaller = more aggressive
Trade-offs
| Higher Q | Effect |
|---|---|
| Faster tracking | More control effort |
| Lower steady-state error | More aggressive transients |
| Higher R | Effect |
|---|---|
| Smoother control | Slower response |
| Less actuator wear | Higher tracking error |
Terminal Cost
Use LQR solution for terminal cost to guarantee stability:
P = solve_continuous_are(A, B, Q, R)
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 · 49 lines · 18 tokens per session scan A fa85a6c22e72
mpc-horizon-tuning is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 18 tokens to every session and 326 once invoked, about $0.0001 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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