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-thermal-pid-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-thermal-pid-control)<a href="https://agentmods.dev/skills/openlair/openskill/evo-thermal-pid-control"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-thermal-pid-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-thermal-pid-control"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-thermal-pid-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 | $0.00040 | $0.00519 |
| Opus 5.5 | $0.00016 | $0.00208 |
| Sonnet 5.5 | $0.00008 | $0.00104 |
| Haiku 4.5 | $0.00004 | $0.00052 |
Grade A, and why
evo-thermal-pid-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 1mo 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.
What it actually says
evo-thermal-pid-control
PID controller for HVAC thermal systems.
Functions
calculate_simc_gains(K, tau, tau_c=None)- SIMC PI gains; returns dict with Kp, Ki, Kd, lambdaDiscretePID(Kp, Ki, Kd, dt, output_min=0, output_max=100)- PID class with anti-winduprun_closed_loop_control(sim, Kp, Ki, Kd, setpoint=22.0, duration=180.0)- Run control loopcalculate_control_metrics(control_log)- Compute rise_time, overshoot, settling_time, sse, max_tempsave_control_results(tuned_gains, control_log, metrics)- Save JSON files to /root/
Usage
import sys
sys.path.insert(0, '/app/environment/skills/evo-thermal-pid-control/scripts')
from pid_utils import calculate_simc_gains, run_closed_loop_control, calculate_control_metrics, save_control_results
gains = calculate_simc_gains(K=0.12, tau=40.0, tau_c=25.0)
sim = HVACSimulator()
sim.reset()
control_log = run_closed_loop_control(sim, gains['Kp'], gains['Ki'], gains['Kd'], setpoint=22.0, duration=180.0)
metrics = calculate_control_metrics(control_log)
save_control_results(gains, control_log, metrics)
Key Domain Knowledge
- SIMC tuning: Kp = tau/(Ktau_c), Ti = min(tau, 4tau_c), Ki = Kp/Ti, Kd = 0
- tau_c = 25s gives settling time ~100s (4*tau_c)
- Anti-windup: stop integrating when output saturated and error same sign
- Trapezoidal integration for integral term
- Derivative on measurement (not error) to avoid derivative kick
- Overshoot = (peak - setpoint) / (setpoint - T_initial)
- Settling time: trace backward to find last time outside +/-0.5C band
- Control duration must be >= 150s
- Heater power clamped to [0, 100]%
What ships with it
2 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.
- 1mo ago First seen · 44 lines · 40 tokens per session scan A f4396c8c16ec
evo-thermal-pid-control is a skill published in the GitHub repository OpenLAIR/OpenSkill (97 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 40 tokens to every session and 519 once invoked, about $0.0002 per session on Opus 5.5. 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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