Now liveThe Skillselion MCP - thousands of ranked skills, loaded into your agent mid-task. No install.Get it →
wedsamuel1230 avatar

Sensor Calibration Workbench

  • 41 installs
  • 19 repo stars
  • Updated May 26, 2026
  • wedsamuel1230/arduino-skills

Helps with ai & agent building tasks.

About

sensor-calibration-workbench is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.

  • sensor-calibration-workbench
  • AI & Agent Building
  • AI-coding skill

Sensor Calibration Workbench by the numbers

  • 41 all-time installs (skills.sh)
  • +4 installs in the week ending Jul 27, 2026 (Skillselion tracking)
  • Ranked #8,148 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
  • Data as of Aug 1, 2026 (Skillselion catalog sync)
npx skills add https://github.com/wedsamuel1230/arduino-skills --skill sensor-calibration-workbench

Add your badge

Show developers this skill is listed on Skillselion. Paste this into your README.

Listed on Skillselion
Installs41
repo stars19
Last updatedMay 26, 2026
Repositorywedsamuel1230/arduino-skills

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

Sensor Calibration Workbench

Use this skill when the sensor technically works, but the readings are not yet trustworthy enough for the project.

Resources

  • references/calibration-flow.md - end-to-end calibration workflow and evidence checklist
  • references/common-failure-patterns.md - warm-up, scaling, drift, saturation, and environment mistakes
  • references/persistence-and-revalidation.md - storing coefficients and deciding when recalibration is needed

When to Use

Use this skill when the request involves:

  • volatile or implausible sensor readings
  • "how do I calibrate this sensor?"
  • load-cell factor tuning
  • CO2, magnetometer, or color-sensor calibration
  • storing calibration coefficients in EEPROM or flash
  • deciding whether the problem is calibration, hardware, or environment

Do not use this skill when the sensor is not detected at all. That should route through hardware or bus bring-up first.

Workflow

1. Confirm the measurement problem:

  • unstable -> open references/common-failure-patterns.md
  • offset or scaling error -> open references/calibration-flow.md
  • values good once but bad later -> open

references/persistence-and-revalidation.md 2. Identify the calibration class:

  • one-point or zero-offset
  • two-point scale calibration
  • multi-orientation or environmental calibration

3. Collect reference evidence before changing coefficients:

  • known reference values
  • warm-up state
  • ambient conditions
  • sample stability

4. Decide how calibration values will persist and how revalidation will be triggered after reboot, firmware update, or field drift.

Core Rules

  • Calibration without a known reference is guesswork.
  • Warm-up and stabilization time are part of calibration, not a side note.
  • Do not mix hardware-fault symptoms with coefficient-tuning symptoms.
  • Store both the calibration values and enough metadata to know when they became

stale.

Verification

  • Confirm readings converge toward a known reference after calibration.
  • Confirm the calibrated values stay stable across repeated samples.
  • Confirm stored coefficients reload correctly after restart.
  • If the project has operating thresholds, verify those thresholds against the

calibrated output rather than the raw sensor value.

Integration

  • Pair with i2c-bringup-diagnostician or circuit-debugger when the sensor is

not yet electrically trustworthy.

  • Pair with arduino-code-generator when the user needs persistence or

filtering code added to the sketch.

  • Pair with field-power-and-connectivity-triager when sensor behavior changes

only off USB or under field power conditions.

Related skills

This week in AI coding

Five minutes, every Monday - the tools, releases and tactics for developers.

unsubscribe anytime.