Kaizen Skill -- Continuous Improvement Tracking
Purpose
Log daily improvement observations and track them for systematic learning. Kaizen (continuous improvement) is a core principle: every day, EM captures one improvement idea, lesson learned, or process optimization.
Categories
- •PROCESS: Workflow improvements, automation opportunities, handoff optimizations
- •QUALITY: Error prevention, data validation, accuracy improvements
- •SPEED: Performance optimizations, bottleneck reduction, cycle time improvements
- •COST: Cost savings, resource optimization, efficiency gains
- •RISK: Risk mitigation, safety improvements, failure prevention
Daily logging
Every day at 17:45, EM logs one Kaizen observation to kaizen-journal.md with:
- •Date (YYYY-MM-DD)
- •Category (PROCESS, QUALITY, SPEED, COST, RISK)
- •Observation (what did we notice today?)
- •Root cause (why does it matter?)
- •Proposed action (what could we try?)
- •Evidence (what data supports this?)
- •Status (proposed / approved / implemented / rejected)
Weekly digest
Every Friday 17:30, EM compiles weekly Kaizen log for the retrospective:
- •All observations from the week
- •Categorized by type
- •Approved items highlighted
- •Implemented improvements shown with results
Human interaction
Observations start as "proposed". Human can:
- •Approve: Move to "approved" status. EM implements next cycle.
- •Reject: Move to "rejected" status with reason.
- •Defer: Keep as "proposed" for later decision.
Approved improvements are tracked for implementation and validation.
Examples
PROCESS: "Z's Hot List publication moved from 06:45 to 06:55 avg. Added extra validation step last week. Marginal impact. Proposal: revert validation step or parallelize it."
QUALITY: "Profile ban incident this week. Proposal: add new alert when profile approaches rate limit (85% of daily cap), allow reactive rotation."
SPEED: "Rick's matching cycle hit 09:12 on Wednesday due to sequential trifecta checking. Sequential can handle 135 apps but struggles with >150. Proposal: implement parallel matching."
COST: "Analyzed end-client deduction accuracy. Jay's deductions 100% accurate. Cost saved by preventing bad-fit submissions: ~$2K/week in avoided recruitment fee disputes."
RISK: "Two duplicate submission near-misses this week (caught by Z). Proposal: add ML model to predict likelihood of 90-day duplicate before submission even reaches Z."