gh-yaleh-meta-cc-claude/skills/knowledge-transfer/reference/overview.md

Knowledge Transfer Methodology - Reference

This reference documentation provides comprehensive details on the progressive learning methodology developed in bootstrap-011.

Core Documentation

Progressive Learning Path Pattern: Time-boxed learning paths with validation checkpoints

• Day-1: 4-8 hours (setup → understanding → first contribution)
• Week-1: 20-40 hours (architecture → module mastery → meaningful contribution)
• Month-1: 40-160 hours (expertise → significant feature → mentoring)

Learning Theory Principles Applied:

1. Progressive Disclosure: Gradual complexity increase
2. Scaffolding: Decreasing support over time
3. Validation Checkpoints: Self-assessment without external dependency
4. Time-Boxing: Realistic time estimates

Knowledge Artifacts

All knowledge artifacts from bootstrap-011 are documented in: experiments/bootstrap-011-knowledge-transfer/knowledge/

Templates (3):

• Day-1 Learning Path Template
• Week-1 Learning Path Template
• Month-1 Learning Path Template

Patterns (1):

• Progressive Learning Path Pattern

Principles (1):

• Validation Checkpoint Principle

Best Practices (1):

• Module Mastery Onboarding

Transferability

Overall: 95%+ transferable to any software project

Cross-Language Validation:

• Go → Rust: 90-95% (6-8h adaptation)
• Go → Python: 85-90% (8-10h adaptation)
• Go → TypeScript: 80-85% (10-12h adaptation)

What Transfers Easily:

• Progressive learning pattern (time-boxed sections, validation checkpoints)
• Validation checkpoint principle (self-assessment criteria)
• Domain specialization framework (choose → deep dive → feature → ownership → mentoring)
• Learning theory principles (progressive disclosure, scaffolding)

What Needs Adaptation:

• Module names (project-specific)
• Technology stack (Go → other languages)
• Project structure (internal/cmd → project layout)
• Domain areas (5 areas → adapt to project size)

Experiment Results

See full results: experiments/bootstrap-011-knowledge-transfer/results.md

Key Metrics:

• V_meta = 0.877 (CONVERGED)
• 3-8x onboarding speedup validated
• 4 iterations, ~8 hours total
• 95%+ transferability