kaizen:why

Apply Five Whys root cause analysis to investigate issues by iteratively asking "why" to drill from symptoms to root causes.

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/neolabhq/context-engineering-kit --skill kaizen:why

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this week

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Installation Guide

How to use kaizen:why on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add kaizen:why
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/neolabhq/context-engineering-kit --skill kaizen:why

Fetches kaizen:why from neolabhq/context-engineering-kit and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/kaizen:why

Restart Cursor to activate kaizen:why. Access via /kaizen:why in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

Five Whys Analysis

Apply Five Whys root cause analysis to investigate issues by iteratively asking "why" to drill from symptoms to root causes.

Description

Iteratively ask "why" to move from surface symptoms to fundamental causes. Identifies systemic issues rather than quick fixes.

Usage

/why [issue_description]

Variables

  • ISSUE: Problem or symptom to analyze (default: prompt for input)
  • DEPTH: Number of "why" iterations (default: 5, adjust as needed)

Steps

  1. State the problem clearly
  2. Ask "Why did this happen?" and document the answer
  3. For that answer, ask "Why?" again
  4. Continue until reaching root cause (usually 5 iterations)
  5. Validate by working backwards: root cause → symptom
  6. Explore branches if multiple causes emerge
  7. Propose solutions addressing root causes, not symptoms

Examples

Example 1: Production Bug

Problem: Users see 500 error on checkout
Why 1: Payment service throws exception
Why 2: Request timeout after 30 seconds
Why 3: Database query takes 45 seconds
Why 4: Missing index on transactions table
Why 5: Index creation wasn't in migration scripts
Root Cause: Migration review process doesn't check query performance

Solution: Add query performance checks to migration PR template

Example 2: CI/CD Pipeline Failures

Problem: E2E tests fail intermittently
Why 1: Race condition in async test setup
Why 2: Test doesn't wait for database seed completion
Why 3: Seed function doesn't return promise
Why 4: TypeScript didn't catch missing return type
Why 5: strict mode not enabled in test config
Root Cause: Inconsistent TypeScript config between src and tests

Solution: Unify TypeScript config, enable strict mode everywhere

Example 3: Multi-Branch Analysis

Problem: Feature deployment takes 2 hours

Branch A (Build):
Why 1: Docker build takes 90 minutes
Why 2: No layer caching
Why 3: Dependencies reinstalled every time
Why 4: Cache invalidated by timestamp in Dockerfile
Root Cause A: Dockerfile uses current timestamp for versioning

Branch B (Tests):
Why 1: Test suite takes 30 minutes
Why 2: Integration tests run sequentially
Why 3: Test runner config has maxWorkers: 1
Why 4: Previous developer disabled parallelism due to flaky tests
Root Cause B: Flaky tests masked by disabling parallelism

Solutions: 
A) Remove timestamp from Dockerfile, use git SHA
B) Fix flaky tests, re-enable parallel test execution

Notes

  • Don't stop at symptoms; keep digging for systemic issues
  • Multiple root causes may exist - explore different branches
  • Document each "why" for future reference
  • Consider both technical and process-related causes
  • The magic isn't in exactly 5 whys - stop when you reach the true root cause
  • Stop when you hit systemic/process issues, not just technical details
  • Multiple root causes are common—explore branches separately
  • If "human error" appears, keep digging: why was error possible?
  • Document every "why" for future reference
  • Root cause usually involves: missing validation, missing docs, unclear process, or missing automation
  • Test solutions: implement → verify symptom resolved → monitor for recurrence

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Steps

  1. 1Install skill using provided installation command
  2. 2Test with simple use case relevant to your work
  3. 3Evaluate output quality and relevance
  4. 4Iterate on prompts to improve results
  5. 5Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use when

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid when

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Related Skills

Reviews

4.659 reviews
  • M
    Mateo RaoDec 28, 2024

    Keeps context tight: kaizen:why is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • D
    Dev VermaDec 20, 2024

    Keeps context tight: kaizen:why is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • L
    Luis RaoDec 20, 2024

    kaizen:why is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • F
    Fatima SharmaDec 16, 2024

    kaizen:why fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • S
    Sofia TandonDec 8, 2024

    Solid pick for teams standardizing on skills: kaizen:why is focused, and the summary matches what you get after install.

  • L
    Luis MartinezDec 4, 2024

    I recommend kaizen:why for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • C
    Charlotte VermaDec 4, 2024

    kaizen:why fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • C
    Charlotte TandonNov 27, 2024

    kaizen:why has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • C
    Charlotte AbbasNov 23, 2024

    kaizen:why is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • R
    Rahul SantraNov 11, 2024

    Keeps context tight: kaizen:why is the kind of skill you can hand to a new teammate without a long onboarding doc.

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