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 OpenLAIR/OpenSkill --skill evo-r2r-mpc-controllergit clone --depth 1 https://github.com/OpenLAIR/OpenSkillWrote 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/openlair/openskill/evo-r2r-mpc-controller)<a href="https://agentmods.dev/skills/openlair/openskill/evo-r2r-mpc-controller"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-r2r-mpc-controller/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/openlair/openskill/evo-r2r-mpc-controller"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-r2r-mpc-controller.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 | $0.00043 | $0.00373 |
| Opus 5 | $0.00022 | $0.00187 |
| Sonnet 5 | $0.00009 | $0.00075 |
| Haiku 4.5 | $0.00004 | $0.00037 |
Grade A, and why
evo-r2r-mpc-controller 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 yesterday.
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.
What it actually says
evo-r2r-mpc-controller
MPC controller for 6-section R2R system with reference step tracking.
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-r2r-mpc-controller/scripts')
from utils import (
build_prediction_matrices,
build_qp_matrices,
solve_mpc_step,
run_simulation_loop,
compute_performance_metrics,
save_output_files
)
Key Functions
build_prediction_matrices(Ad, Bd, N)- Build Phi and Gamma matricesbuild_qp_matrices(Phi, Gamma, Q, R_mat, P, N)- Build QP Hessian and gradientsolve_mpc_step(H, F, dx, N, nu)- Solve one MPC step (unconstrained)run_simulation_loop(sim, linearize_fn, get_ref_fn, Q, R_mat, N, num_steps)- Full sim loopcompute_performance_metrics(log_data, T_ref_final)- Compute SSE, settling time, etc.save_output_files(controller_params, log_data, metrics)- Save all 3 JSON files
Output Files
- controller_params.json: A_matrix (continuous), B_matrix (continuous), K_lqr, Q_diag, R_diag, horizon_N
- control_log.json: phase="control", data array with time/tensions/velocities/control_inputs/references
- metrics.json: steady_state_error, settling_time, max_tension, min_tension
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- yesterday First seen · 36 lines · 43 tokens per session scan A 572c1825aec4
evo-r2r-mpc-controller is a skill published in the GitHub repository OpenLAIR/OpenSkill (90 stars, last pushed 2d ago), licensed Apache-2.0. It adds 43 tokens to every session and 373 once invoked, about $0.0002 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 2026-09-11.
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