MCPInstall

plan-do-check-act

Iterative PDCA cycle for systematic experimentation and continuous improvement

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/neolabhq/context-engineering-kit --skill plan-do-check-act
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads plan-do-check-act automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Plan-Do-Check-Act (PDCA)

Apply PDCA cycle for continuous improvement through iterative problem-solving and process optimization.

Description

Four-phase iterative cycle: Plan (identify and analyze), Do (implement changes), Check (measure results), Act (standardize or adjust). Enables systematic experimentation and improvement.

Usage

/plan-do-check-act [improvement_goal]

Variables

  • GOAL: Improvement target or problem to address (default: prompt for input)
  • CYCLE_NUMBER: Which PDCA iteration (default: 1)

Steps

Phase 1: PLAN

  1. Define the problem or improvement goal
  2. Analyze current state (baseline metrics)
  3. Identify root causes (use /why or /cause-and-effect)
  4. Develop hypothesis: "If we change X, Y will improve"
  5. Design experiment: what to change, how to measure success
  6. Set success criteria (measurable targets)

Phase 2: DO

  1. Implement the planned change (small scale first)
  2. Document what was actually done
  3. Record any deviations from plan
  4. Collect data throughout implementation
  5. Note unexpected observations

Phase 3: CHECK

  1. Measure results against success criteria
  2. Compare to baseline (before vs. after)
  3. Analyze data: did hypothesis hold?
  4. Identify what worked and what didn't
  5. Document learnings and insights

Phase 4: ACT

  1. If successful: Standardize the change
    • Update documentation
    • Train team
    • Create checklist/automation
    • Monitor for regression
  2. If unsuccessful: Learn and adjust
    • Understand why it failed
    • Refine hypothesis
    • Start new PDCA cycle with adjusted plan
  3. If partially successful:
    • Standardize what worked
    • Plan next cycle for remaining issues

Examples

Example 1: Reducing Build Time

CYCLE 1
───────
PLAN:
  Problem: Docker build takes 45 minutes
  Current State: Full rebuild every time, no layer caching
  Root Cause: Package manager cache not preserved between builds
  Hypothesis: Caching dependencies will reduce build to <10 minutes
  Change: Add layer caching for package.json + node_modules
  Success Criteria: Build time <10 minutes on unchanged dependencies

DO:
  - Restructured Dockerfile: COPY package*.json before src files
  - Added .dockerignore for node_modules
  - Configured CI cache for Docker layers
  - Tested on 3 builds

CHECK:
  Results:
    - Unchanged dependencies: 8 minutes ✓ (was 45)
    - Changed dependencies: 12 minutes (was 45)
    - Fresh builds: 45 minutes (same, expected)
  Analysis: 82% reduction on cached builds, hypothesis confirmed

ACT:
  Standardize:
    ✓ Merged Dockerfile changes
    ✓ Updated CI pipeline config
    ✓ Documented in README
    ✓ Added build time monitoring
  
  New Problem: 12 minutes still slow when deps change
  → Start CYCLE 2


CYCLE 2
───────
PLAN:
  Problem: Build still 12 min when dependencies change
  Current State: npm install rebuilds all packages
  Root Cause: Some packages compile from source
  Hypothesis: Pre-built binaries will reduce to <5 minutes
  Change: Use npm ci instead of install, configure binary mirrors
  Success Criteria: Build <5 minutes on dependency changes

DO:
  - Changed to npm ci (uses package-lock.json)
  - Added .npmrc with binary mirror configs
  - Tested across 5 dependency updates

CHECK:
  Results:
    - Dependency changes: 4.5 minutes ✓ (was 12)
    - Compilation errors reduced to 0 (was 3)
  Analysis: npm ci faster + more reliable, hypothesis confirmed

ACT:
  Standardize:
    ✓ Use npm ci everywhere (local + CI)
    ✓ Committed .npmrc
    ✓ Updated developer onboarding docs
  
  Total improvement: 45min → 4.5min (90% reduction)
  ✓ PDCA complete, monitor for 2 weeks

Example 2: Reducing Production Bugs

CYCLE 1
───────
PLAN:
  Problem: 8 production bugs per month
  Current State: Manual testing only, no automated tests
  Root Cause: Regressions not caught before release
  Hypothesis: Adding integration tests will reduce bugs by 50%
  Change: Implement integration test suite for critical paths
  Success Criteria: <4 bugs per month after 1 month

DO:
  Week 1-2: Wrote integration tests for:
    - User authentication flow
    - Payment processing
    - Data export
  Week 3: Set up CI to run tests
  Week 4: Team training on test writing
  Coverage: 3 critical paths (was 0)

CHECK:
  Results after 1 month:
    - Production bugs: 6 (was 8)
    - Bugs caught in CI: 4
    - Test failures (false positives): 2
  Analysis: 25% reduction, not 50% target
  Insight: Bugs are in areas without tests yet

ACT:
  Partially successful:
    ✓ Keep existing tests (prevented 4 bugs)
    ✓ Fix flaky tests
  
  Adjust for CYCLE 2:
    - Expand test coverage to all user flows
    - Add tests for bug-prone areas
    → Start CYCLE 2


CYCLE 2
───────
PLAN:
  Problem: Still 6 bugs/month, need <4
  Current State: 3 critical paths tested, 12 paths total
  Root Cause: UI interaction bugs not covered by integration tests
  Hypothesis: E2E tests for all user flows will reach <4 bugs
  Change: Add E2E tests for remaining 9 flows
  Success Criteria: <4 bugs per month, 80% coverage

DO:
  Week 1-3: Added E2E tests for all user flows
  Week 4: Set up visual regression testing
  Coverage: 12/12 user flows (was 3/12)

CHECK:
  Results after 1 month:
    - Production bugs: 3 ✓ (was 6)
    - Bugs caught in CI: 8 (was 4)
    - Test maintenance time: 3 hours/week
  Analysis: Target achieved! 62% reduction from baseline

ACT:
  Standardize:
    ✓ Made tests required for all PRs
    ✓ Added test checklist to PR template
    ✓ Scheduled weekly test review
    ✓ Created runbook for test maintenance
  
  Monitor: Track bug rate and test effectiveness monthly
  ✓ PDCA complete

Example 3: Improving Code Review Speed

PLAN:
  Problem: PRs take 3 days average to merge
  Current State: Manual review, no automation
  Root Cause: Reviewers wait to see if CI passes before reviewing
  Hypothesis: Auto-review + faster CI will reduce to <1 day
  Change: Add automated checks + split long CI jobs
  Success Criteria: Average time to merge <1 day (8 hours)

DO:
  - Set up automated linter checks (fail fast)
  - Split test suite into parallel jobs
  - Added PR template with self-review checklist
  - CI time: 45min → 15min
  - Tracked PR merge time for 2 weeks

CHECK:
  Results:
    - Average time to merge: 1.5 days (was 3)
    - Time waiting for CI: 15min (was 45min)
    - Time waiting for review: 1.3 days (was 2+ days)
  Analysis: CI faster, but review still bottleneck

ACT:
  Partially successful:
    ✓ Keep fast CI improvements
  
  Insight: Real bottleneck is reviewer availability, not CI
  Adjust for new PDCA:
    - Focus on reviewer availability/notification
    - Consider rotating review assignments
  → Start new PDCA cycle with different hypothesis

Notes

  • Start with small, measurable changes (not big overhauls)
  • PDCA is iterative—multiple cycles normal
  • Failed experiments are learning opportunities
  • Document everything: easier to see patterns across cycles
  • Success criteria must be measurable (not subjective)
  • Phase 4 "Act" determines next cycle or completion
  • If stuck after 3 cycles, revisit root cause analysis
  • PDCA works for technical and process improvements
  • Use /analyse-problem (A3) for comprehensive documentation

FAQ

What does the plan-do-check-act skill do?

Iterative PDCA cycle for systematic experimentation and continuous improvement

How do I install plan-do-check-act?

Run npx skills add https://github.com/neolabhq/context-engineering-kit --skill plan-do-check-act in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/plan-do-check-act/ (personal) or .claude/skills/plan-do-check-act/ (project).

Which AI agents can use plan-do-check-act?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is plan-do-check-act safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains plan-do-check-act?

It is published by neolabhq in neolabhq/context-engineering-kit. It has 1,202 recorded installs.

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