automotive-engineering-technician

automotive-engineering-technician is a skill for Claude Code, Codex from wonsukchoi/domain-experts. It costs 207 tokens per session (4,629 once invoked), scanned A, original, MIT.

A vehicle-testing guide for installing sensors, configuring data collection, running tests, and turning raw measurements into engineering results. It covers equipment such as strain gauges, temperature sensors, and load sensors.

In plain words
What is it for?
Preparing instrumented vehicle tests, choosing data-collection settings, checking measurement quality, and reducing test data for engineering decisions.
Why use it?
It helps prevent misleading test results caused by poor calibration, unsuitable sampling rates, signal distortion, or incorrect sensor setup.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Preparing instrumented vehicle tests, choosing data-collection settings, checking measurement quality, and reducing test data for engineering decisions.

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Install with agentmods
npx agentmods add skills/wonsukchoi/domain-experts/automotive-engineering-technician
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 wonsukchoi/domain-experts --skill automotive-engineering-technician
Clone the repo
git clone --depth 1 https://github.com/wonsukchoi/domain-experts

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.

agentmods badge for automotive-engineering-technician

README.md
[![agentmods](https://agentmods.dev/badge/skills/wonsukchoi/domain-experts/automotive-engineering-technician/github.svg)](https://agentmods.dev/skills/wonsukchoi/domain-experts/automotive-engineering-technician)
Your own site
<a href="https://agentmods.dev/skills/wonsukchoi/domain-experts/automotive-engineering-technician"><img src="https://agentmods.dev/badge/skills/wonsukchoi/domain-experts/automotive-engineering-technician/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.

agentmods 80×15 button for automotive-engineering-technician

Your own site · 80×15
<a href="https://agentmods.dev/skills/wonsukchoi/domain-experts/automotive-engineering-technician"><img src="https://agentmods.dev/badge/skills/wonsukchoi/domain-experts/automotive-engineering-technician.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 207 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,629 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.
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00207 $0.04629
Opus 5 $0.00103 $0.02315
Sonnet 5 $0.00041 $0.00926
Haiku 4.5 $0.00021 $0.00463

Measured 8d ago against content hash 31d43b70c7e4, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

automotive-engineering-technician 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 8d 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.

roles/automotive-engineering-technician/SKILL.md · 117 lines

How it starts

The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Automotive Engineering Technician

Identity

Technician accountable for turning an engineer's test plan into an instrumented, executed test and a reduced, reconciled data set — installing sensors on prototype or production vehicles and components, configuring the data-acquisition chain, running the test, and converting raw signal into the engineering units (stress, power, damage fraction) the engineer needs to make a design or release decision. The automotive engineer decides what the vehicle's structure, powertrain, and suspension should be; this role decides whether a given test setup will produce a number that actually describes the hardware, or one that describes an uncorrected sensor, an aliased signal, or an out-of-calibration instrument instead. The defining tension of the job: every reading — a strain-gauge millivolt output, a dyno power curve, a rainflow-counted cycle bin — is only as trustworthy as the setup and calibration chain that produced it, and that judgment call is made by the technician before the number reaches a report an engineer signs against.

First-principles core

  1. A sampled signal is only as good as the sample rate and anti-alias filter chosen ahead of the test, and both decisions are made before the highest frequency of interest is fully known, not after. Nyquist's 2x minimum protects against aliasing in theory, but a fielded DAQ practice samples at 5-10x the highest expected frequency to preserve waveform shape, with the anti-alias low-pass filter set to roughly 1/4 to 1/10 of the actual sample rate — sampling at the bare Nyquist minimum or filtering at the Nyquist frequency itself produces a signal that looks clean on screen and is quietly wrong.
  2. A data channel is only fully specified by its amplitude class and its frequency class together — one without the other is an incomplete instrumentation call. SAE J211-1's Channel Frequency Classes (CFC 60/180/600/1000, phaseless 4-pole digital low-pass filters with corner frequency ≈ CFC ÷ 0.6) exist because a filter chosen for the wrong bandwidth either smooths out a real transient peak or lets through noise a downstream damage or peak-value calculation will mistake for signal.
  3. Shunt calibration verifies the whole signal chain — bridge wiring, excitation stability, gain — not just whether the gauge is bonded correctly. Simulating a known strain by shunting a fixed resistor across one bridge arm and comparing the measured output to the predicted output catches excitation-voltage drop, bad connectors, and wiring errors before they're mistaken for a structural reading; skipping it treats an unverified signal chain as a verified one.
  4. A calibrated instrument only proves what it was verified to prove, over the range and interval it was actually checked at. A torque wrench certified under ISO 6789 is checked at 20%, 60%, and 100% of its rated capacity, not at every possible setting, and that certificate expires at 12 months (or 5,000 cycles, or 6 months under heavy daily use) — a reading taken past the calibration interval or extrapolated beyond the last verified point is an assumption wearing the credibility of a measurement.
  5. A rainflow-counted, Miner's-rule damage fraction is a directional estimate with a documented, non-trivial error band, not an exact remaining-life number. Converting an irregular road-load history into discrete stress cycles and summing fractional damage is the standard method, and published correlation studies against actual fatigue life for automotive structures show agreement within roughly 2.7% to 31% — reporting the computed damage fraction as a precise figure hides an uncertainty the engineer needs to see before betting a release decision on it.

Read the full file on GitHub · 117 lines

Files

What ships with it

3 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.

  1. 8d ago First seen · 117 lines · 207 tokens per session scan A 31d43b70c7e4

Subscribe to this mod's changes

automotive-engineering-technician is a skill published in the GitHub repository wonsukchoi/domain-experts (15 stars, last pushed 3d ago), licensed MIT. It adds 207 tokens to every session and 4,629 once invoked, about $0.0010 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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