What is evo-hvac-controller?

A temperature-control workflow for heating, ventilation, and air-conditioning systems. It tests the system, models how its temperature responds, sets controller settings, and runs temperature control.

Use it to calibrate a thermal system, estimate its response, tune PID settings, run closed-loop control, and calculate rise time, overshoot, settling time, and steady-state error.

It replaces manual tuning of a PID controller, which adjusts heating or cooling based on current and past temperature error. It also measures whether the control behaves as expected.

evo-hvac-controller — Skill for Claude Code and Codex by Zhang-Henry

Install

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.

Any agent

npx skills add Zhang-Henry/CoEvoSkills --skill evo-hvac-controller

Clone the repo

git clone --depth 1 https://github.com/Zhang-Henry/CoEvoSkills

Made for: Claude Code, Codex.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/zhang-henry/coevoskills/evo-hvac-controller/github.svg)](https://agentmods.dev/skills/zhang-henry/coevoskills/evo-hvac-controller)

Your own site

<a href="https://agentmods.dev/skills/zhang-henry/coevoskills/evo-hvac-controller"><img src="https://agentmods.dev/badge/skills/zhang-henry/coevoskills/evo-hvac-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.

Your own site · 80×15

<a href="https://agentmods.dev/skills/zhang-henry/coevoskills/evo-hvac-controller"><img src="https://agentmods.dev/badge/skills/zhang-henry/coevoskills/evo-hvac-controller.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 50 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 607 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits

NVIDIA SkillSpector pass 7 Sept 2026

How audits are shown

Origin original No closer match found in the catalogue.

Token cost

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	$0.00050	$0.00607
Opus 5	$0.00025	$0.00303
Sonnet 5	$0.00010	$0.00121
Haiku 4.5	$0.00005	$0.00061

Measured 12d ago against content hash abf5e7589784, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

evo-hvac-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 12d ago.

The scan reads SKILL.md. This mod also ships 7 executable files (scripts/__init__.py, scripts/calibration.py, scripts/controller.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

artifacts/skills/hvac-control/evo-hvac-controller/SKILL.md · 68 lines

How it starts

The opening of the file, as written. The whole thing — 68 lines — stays where its author put it; the contents beside it link to each section on GitHub.

HVAC Temperature Controller Skill

Overview

This skill implements a complete HVAC temperature control pipeline:

Calibration: Open-loop step test to characterize the thermal system
Estimation: Fit first-order model parameters (K, tau) via curve fitting
Tuning: Compute PID gains using IMC (Internal Model Control) method
Control: Run closed-loop PID control with anti-windup
Metrics: Compute rise time, overshoot, settling time, steady-state error, max temp

Quick Start

import sys
sys.path.insert(0, '/app/environment/skills/evo-hvac-controller/scripts')
from orchestrator import run_full_pipeline, validate_outputs

# Run the complete pipeline
results = run_full_pipeline(
    output_dir="/root",
    calibration_power=50.0,
    calibration_duration=60.0,
    control_duration=180.0
)

# Validate all outputs
validate_outputs("/root")

Scripts

scripts/calibration.py

run_calibration(sim, heater_power, duration) - Run open-loop calibration
save_calibration_log(log, path) - Save calibration data to JSON

scripts/estimation.py

estimate_params(calibration_log, ambient_temp) - Fit K and tau from data
save_estimated_params(params, path) - Save parameters to JSON

scripts/tuning.py

compute_gains(K, tau, lambda_factor) - Compute PID gains via IMC
save_tuned_gains(gains, path) - Save gains to JSON

scripts/controller.py

PIDController class with anti-windup
run_closed_loop(sim, gains, duration) - Run PID control loop
save_control_log(log, path) - Save control log to JSON

scripts/metrics.py

compute_metrics(control_log) - Compute performance metrics
save_metrics(metrics, path) - Save metrics to JSON

scripts/orchestrator.py

run_full_pipeline(output_dir, ...) - End-to-end entry point
validate_outputs(output_dir) - Validate all output files

Design Decisions

IMC tuning with lambda = max(tau/2, 5) for balanced speed/stability
Anti-windup via back-calculation in PID controller
Settling band of +/- 0.5C (matching the steady-state error target)
Overshoot computed as (max_temp - setpoint) / (setpoint - T0)
Steady-state error averaged over last 20% of data

Read the full file on GitHub · 68 lines

Files

What ships with it

7 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.

Changes

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.

12d ago First seen · 68 lines · 50 tokens per session scan A abf5e7589784

Subscribe to this mod's changes

evo-hvac-controller is a skill published in the GitHub repository Zhang-Henry/CoEvoSkills (66 stars, last pushed 22d ago), licensed Apache-2.0. It adds 50 tokens to every session and 607 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 2026-08-30.

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