plugins/kaizen/skills/why/SKILL.md
Iterative Five Whys root cause analysis drilling from symptoms to fundamentals
npx skillsauth add NeoLabHQ/context-engineering-kit whyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Apply Five Whys root cause analysis to investigate issues by iteratively asking "why" to drill from symptoms to root causes.
Iteratively ask "why" to move from surface symptoms to fundamental causes. Identifies systemic issues rather than quick fixes.
/why [issue_description]
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
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
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
development
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testing
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testing
Refine, parallelize, and verify a draft task specification into a fully planned implementation-ready task
data-ai
Implement a task with automated LLM-as-Judge verification per step