kaizen:plan-do-check-act

Guide iterative process improvement through Plan-Do-Check-Act experiment cycles.

Updated Apr 29, 2026

Search Tags:#root cause analysis #metrics #continuous improvement #process improvement #experimentation #pdca

Ready to run

npx skills add https://github.com/pgm1980/stryker-netx --skill kaizen-plan-do-check-act-pgm1980

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: kaizen:plan-do-check-act
Source: https://github.com/pgm1980/stryker-netx/tree/main/.claude/skills/plan-do-check-act
Command: npx skills add https://github.com/pgm1980/stryker-netx --skill kaizen-plan-do-check-act-pgm1980

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

PDCA helps teams move from vague improvement goals to measurable, iterative change—by planning actions, executing small experiments, checking results with data, and standardizing what works.

Core Features & Use Cases

Structured iterative experimentation across Plan, Do, Check, and Act to reduce guesswork.
Measurable success criteria so outcomes can be compared against a baseline.
Continuous improvement loop that turns failures into learning and triggers a new cycle when needed.
Use Case: A software team wants to reduce CI build time by running focused experiments, tracking before/after metrics, and applying the winning changes across the workflow.

Quick Start

Use the skill by asking: "Start PDCA for reducing our CI build time, with clear success metrics and a plan for Cycle 1."

Frequently Asked Questions about kaizen:plan-do-check-act

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is the PDCA cycle for continuous improvement?▼

The PDCA cycle is an iterative experiment loop using Plan, Do, Check, and Act phases to achieve measurable process improvement through evidence-based decisions and small-scope changes.

How do I run a PDCA experiment to reduce CI build time?▼

To run a PDCA experiment for CI build time, define an improvement goal, execute small-scope changes, check results against baseline metrics, and standardize the winning changes or plan a refined cycle.

When should I use a PDCA loop for process improvement?▼

Use a PDCA loop for process improvement when engineering or operational scenarios require root-cause analysis, measurable outcomes, and iterative experimentation to reduce guesswork across multiple cycles.

Can I use PDCA for root cause analysis in software engineering?▼

Yes, PDCA supports root cause analysis in software engineering by recording deviations and observations during experiments, then evaluating them against measurable success criteria to drive evidence-based decisions.

How does the PDCA Act phase handle failed experiments?▼

The Act phase handles failed experiments by evaluating results against success criteria, turning failures into learning, and selecting a refined next-cycle plan instead of standardizing the changes.