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-adaptive-cruise-controlgit 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-adaptive-cruise-control)<a href="https://agentmods.dev/skills/openlair/openskill/evo-adaptive-cruise-control"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-adaptive-cruise-control/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-adaptive-cruise-control"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-adaptive-cruise-control.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 | $0.00046 | $0.01075 |
| Opus 5 | $0.00023 | $0.00537 |
| Sonnet 5 | $0.00009 | $0.00215 |
| Haiku 4.5 | $0.00005 | $0.00108 |
Grade A, and why
evo-adaptive-cruise-control 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 today.
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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
evo-adaptive-cruise-control
Overview
A unified ACC simulation skill combining:
- PID Controller — Discrete-time PID with integral anti-windup and robust first-timestep handling.
- ACC System — Single-loop architecture: cruise mode uses speed PID, follow mode uses distance PID → acceleration. Three modes: cruise, follow, emergency. TTC-based safety override.
- Simulation Runner — 1501-step Euler integration loop, sensor_data.csv / vehicle_params.yaml ingestion, tuning_results.yaml loading, simulation_results.csv output (with proper NaN→empty handling), and acc_report.md generation.
- Tuning — Pre-tuned PID gains for speed and distance control.
Quick Start
# 1. Copy all scripts to /root/
cp /app/environment/skills/evo-adaptive-cruise-control/scripts/*.py /root/
# 2. Generate tuning_results.yaml with pre-tuned gains
cd /root && python3 tuning.py
# 3. Run simulation (reads tuning_results.yaml, sensor_data.csv, vehicle_params.yaml)
python3 simulation.py
This produces: tuning_results.yaml, simulation_results.csv, acc_report.md.
File Descriptions
pid_controller.py
PIDController(kp, ki, kd, output_limits=(None, None))— Constructorreset()— Resets integral, prev_errorcompute(error, dt, measurement=None)— Returns float control output
acc_system.py
AdaptiveCruiseControl(config)— config dict from vehicle_params.yaml. PID controllers are created inside__init__with default gains. No separate setup step needed.compute(ego_speed, lead_speed, distance, dt)→(accel_cmd, mode, distance_error)modeis one of:'cruise','follow','emergency'distance_errorisNonewhen in cruise mode (no lead vehicle)
calculate_ttc(ego_speed, lead_speed, distance)→ floatcalculate_desired_distance(ego_speed, time_headway, min_distance)→ float
simulation.py
- Reads PID gains from
tuning_results.yamlat runtime (no embedded auto-tuning). - Uses
sensor_data.csvfor lead vehicle data. - Outputs
simulation_results.csv(1501 rows) andacc_report.md. - Can be run directly:
python3 simulation.py
What ships with it
4 files 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.
- today First seen · 89 lines · 46 tokens per session scan A 1412b486061d
evo-adaptive-cruise-control is a skill published in the GitHub repository OpenLAIR/OpenSkill (88 stars, last pushed yesterday), licensed Apache-2.0. It adds 46 tokens to every session and 1,075 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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