Smart Analysis
Intelligently select and apply the most appropriate Kaizen analysis technique based on what you're analyzing.
Description
Analyzes context and chooses best method: Gemba Walk (code exploration), Value Stream Mapping (workflow/process), or Muda Analysis (waste identification). Guides you through the selected technique.
Usage
/analyse [target_description]
Examples:
/analyse authentication implementation/analyse deployment workflow/analyse codebase for inefficiencies
Variables
- TARGET: What to analyze (default: prompt for input)
- METHOD: Override auto-selection (gemba, vsm, muda)
Method Selection Logic
Gemba Walk → When analyzing:
- Code implementation (how feature actually works)
- Gap between documentation and reality
- Understanding unfamiliar codebase areas
- Actual vs. assumed architecture
Value Stream Mapping → When analyzing:
- Workflows and processes (CI/CD, deployment, development)
- Bottlenecks in multi-stage pipelines
- Handoffs between teams/systems
- Time spent in each process stage
Muda (Waste Analysis) → When analyzing:
- Code quality and efficiency
- Technical debt
- Over-engineering or duplication
- Resource utilization
Steps
- Understand what's being analyzed
- Determine best method (or use specified method)
- Explain why this method fits
- Guide through the analysis
- Present findings with actionable insights
Method 1: Gemba Walk
"Go and see" the actual code to understand reality vs. assumptions.
When to Use
- Understanding how feature actually works
- Code archaeology (legacy systems)
- Finding gaps between docs and implementation
- Exploring unfamiliar areas before changes
Process
- Define scope: What code area to explore
- State assumptions: What you think it does
- Observe reality: Read actual code
- Document findings:
- Entry points
- Actual data flow
- Surprises (differs from assumptions)
- Hidden dependencies
- Undocumented behavior
- Identify gaps: Documentation vs. reality
- Recommend: Update docs, refactor, or accept
Example: Authentication System Gemba Walk
SCOPE: User authentication flow
ASSUMPTIONS (Before):
• JWT tokens stored in localStorage
• Single sign-on via OAuth only
• Session expires after 1 hour
• Password reset via email link
GEMBA OBSERVATIONS (Actual Code):
Entry Point: /api/auth/login (routes/auth.ts:45)
├─> AuthService.authenticate() (services/auth.ts:120)
├─> UserRepository.findByEmail() (db/users.ts:67)
├─> bcrypt.compare() (services/auth.ts:145)
└─> TokenService.generate() (services/token.ts:34)
Actual Flow:
1. Login credentials → POST /api/auth/login
2. Password hashed with bcrypt (10 rounds)
3. JWT generated with 24hr expiry (NOT 1 hour!)
4. Token stored in httpOnly cookie (NOT localStorage)
5. Refresh token in separate cookie (15 days)
6. Session data in Redis (30 days TTL)
SURPRISES:
✗ OAuth not implemented (commented out code found)
✗ Password reset is manual (admin intervention)
✗ Three different session storage mechanisms:
- Redis for session data
- Database for "remember me"
- Cookies for tokens
✗ Legacy endpoint /auth/legacy still active (no auth!)
✗ Admin users bypass rate limiting (security issue)
GAPS:
• Documentation says OAuth, code doesn't have it
• Session expiry inconsistent (docs: 1hr, code: 24hr)
• Legacy endpoint not documented (security risk)
• No mention of "remember me" in docs
RECOMMENDATIONS:
1. HIGH: Secure or remove /auth/legacy endpoint
2. HIGH: Document actual session expiry (24hr)
3. MEDIUM: Clean up or implement OAuth
4. MEDIUM: Consolidate session storage (choose one)
5. LOW: Add rate limiting for admin users
Example: CI/CD Pipeline Gemba Walk
SCOPE: Build and deployment pipeline
ASSUMPTIONS:
• Automated tests run on every commit
• Deploy to staging automatic
• Production deploy requires approval
GEMBA OBSERVATIONS:
Actual Pipeline (.github/workflows/main.yml):
1. On push to main:
├─> Lint (2 min)
├─> Unit tests (5 min) [SKIPPED if "[skip-tests]" in commit]
├─> Build Docker image (15 min)
└─> Deploy to staging (3 min)
2. Manual trigger for production:
├─> Run integration tests (20 min) [ONLY for production!]
├─> Security scan (10 min)
└─> Deploy to production (5 min)
SURPRISES:
✗ Unit tests can be skipped with commit message flag
✗ Integration tests ONLY run for production deploy
✗ Staging deployed without integration tests
✗ No rollback mechanism (manual kubectl commands)
✗ Secrets loaded from .env file (not secrets manager)
✗ Old "hotfix" branch bypasses all checks
GAPS:
• Staging and production have different test coverage
• Documentation doesn't mention test skip flag
• Rollback process not documented or automated
• Security scan results not enforced (warning only)
RECOMMENDATIONS:
1. CRITICAL: Remove test skip flag capability
2. CRITICAL: Migrate secrets to secrets manager
3. HIGH: Run integration tests on staging too
4. HIGH: Delete or secure hotfix branch
5. MEDIUM: Add automated rollback capability
6. MEDIUM: Make security scan blocking
Method 2: Value Stream Mapping
Map workflow stages, measure time/waste, identify bottlenecks.
When to Use
- Process optimization (CI/CD, deployment, code review)
- Understanding multi-stage workflows
- Finding delays and handoffs
- Improving cycle time
Process
- Identify start and end: Where process begins and ends
- Map all steps: Including waiting/handoff time
- Measure each step:
- Processing time (work happening)
- Waiting time (idle, blocked)
- Who/what performs step
- Calculate metrics:
- Total lead time
- Value-add time vs. waste time
- % efficiency (value-add / total time)
- Identify bottlenecks: Longest steps, most waiting
- Design future state: Optimized flow
- Plan improvements: How to achieve future state
Example: Feature Development Value Stream Map
CURRENT STATE: Feature request → Production
Step 1: Requirements Gathering
├─ Processing: 2 days (meetings, writing spec)
├─ Waiting: 3 days (stakeholder review)
└─ Owner: Product Manager
Step 2: Design
├─ Processing: 1 day (mockups, architecture)
├─ Waiting: 2 days (design review, feedback)
└─ Owner: Designer + Architect
Step 3: Development
├─ Processing: 5 days (coding)
├─ Waiting: 2 days (PR review queue)
└─ Owner: Developer
Step 4: Code Review
├─ Processing: 0.5 days (review)
├─ Waiting: 1 day (back-and-forth changes)
└─ Owner: Senior Developer
Step 5: QA Testing
├─ Processing: 2 days (manual testing)
├─ Waiting: 1 day (bug fixes, retest)
└─ Owner: QA Engineer
Step 6: Staging Deployment
├─ Processing: 0.5 days (deploy, smoke test)
├─ Waiting: 2 days (stakeholder UAT)
└─ Owner: DevOps
Step 7: Production Deployment
├─ Processing: 0.5 days (deploy, monitor)
├─ Waiting: 0 days
└─ Owner: DevOps
───────────────────────────────────────
METRICS:
Total Lead Time: 22.5 days
Value-Add Time: 11.5 days (work)
Waste Time: 11 days (waiting)
Efficiency: 51%
BOTTLENECKS:
1. Requirements review wait (3 days)
2. Development time (5 days)
3. Stakeholder UAT wait (2 days)
4. PR review queue (2 days)
WASTE ANALYSIS:
• Waiting for reviews/approvals: 9 days (82% of waste)
• Rework due to unclear requirements: ~1 day
• Manual testing time: 2 days
FUTURE STATE DESIGN:
Changes:
1. Async requirements approval (stakeholders have 24hr SLA)
2. Split large features into smaller increments
3. Automated testing replaces manual QA
4. PR review SLA: 4 hours max
5. Continuous deployment to staging (no approval)
6. Feature flags for production rollout (no wait)
Projected Future State:
Total Lead Time: 9 days (60% reduction)
Value-Add Time: 8 days
Waste Time: 1 day
Efficiency: 89%
IMPLEMENTATION PLAN:
Week 1: Set review SLAs, add feature flags
Week 2: Automate test suite
Week 3: Enable continuous staging deployment
Week 4: Train team on incremental delivery