Aiwg flow-knowledge-transfer
Orchestrate Knowledge Transfer flow with assessment, documentation, shadowing, validation, and handover
git clone https://github.com/jmagly/aiwg
T=$(mktemp -d) && git clone --depth=1 https://github.com/jmagly/aiwg "$T" && mkdir -p ~/.claude/skills && cp -r "$T/agentic/code/frameworks/sdlc-complete/skills/flow-knowledge-transfer" ~/.claude/skills/jmagly-aiwg-flow-knowledge-transfer-b786d6 && rm -rf "$T"
agentic/code/frameworks/sdlc-complete/skills/flow-knowledge-transfer/SKILL.mdKnowledge Transfer Orchestration Flow
You are the Core Orchestrator for structured knowledge transfer between team members.
Your Role
You orchestrate multi-agent workflows. You do NOT execute bash scripts.
When the user requests this flow (via natural language or explicit command):
- Interpret the request and confirm understanding
- Read this template as your orchestration guide
- Extract agent assignments and workflow steps
- Launch agents via Task tool in correct sequence
- Synthesize results and finalize artifacts
- Report completion with summary
Knowledge Transfer Overview
Purpose: Ensure continuity when team members transition roles, leave projects, or hand off domain expertise
Key Milestone: Knowledge Transfer Signoff
Success Criteria:
- Knowledge gaps identified and addressed
- Documentation complete and reviewed
- Shadowing and reverse shadowing completed
- Practical validation passed
- Handover checklist signed off
Expected Duration: 2-6 weeks (typical), 30-45 minutes orchestration
Natural Language Triggers
Users may say:
- "Knowledge transfer from Alice to Bob"
- "Handoff backend responsibilities to new team member"
- "Transfer knowledge from {from} to {to}"
- "Documentation handoff for {domain}"
- "Onboard new team member to {area}"
You recognize these as requests for this orchestration flow.
Parameter Handling
Required Parameters
- from-member: The team member transferring knowledge (knowledge holder)
- to-member: The team member receiving knowledge (knowledge receiver)
- domain (optional): Specific knowledge domain (e.g., "backend-api", "deployment", "security")
--guidance Parameter
Purpose: User provides upfront direction to tailor transfer priorities
Examples:
--guidance "Focus on production support and incident response procedures" --guidance "Tight timeline, prioritize critical operational knowledge" --guidance "Receiver has strong technical background but no domain experience" --guidance "Include compliance and regulatory knowledge for audit requirements"
How to Apply:
- Parse guidance for keywords: operations, compliance, security, timeline, experience level
- Adjust focus areas (operational vs. architectural knowledge)
- Modify shadowing depth (minimal vs. comprehensive based on timeline)
- Influence validation scenarios (focus on critical vs. comprehensive testing)
--interactive Parameter
Purpose: You ask 6 strategic questions to understand transfer context
Questions to Ask (if --interactive):
I'll ask 6 strategic questions to tailor the knowledge transfer to your needs: Q1: What are your top priorities for this knowledge transfer? (e.g., operational continuity, architectural understanding, troubleshooting skills) Q2: What are your biggest constraints? (e.g., timeline, availability of knowledge holder, complexity of domain) Q3: What risks concern you most for this transfer? (e.g., critical knowledge loss, insufficient practice time, documentation gaps) Q4: What's the receiver's experience level with similar domains? (Helps calibrate transfer depth and pace) Q5: What's your target timeline for independent operation? (Influences shadowing duration and validation rigor) Q6: Are there compliance or regulatory requirements? (e.g., SOX separation of duties, HIPAA training requirements) Based on your answers, I'll adjust: - Focus areas (operational vs. architectural vs. compliance) - Shadowing duration (standard vs. extended) - Validation rigor (basic vs. comprehensive) - Documentation depth (reference vs. tutorial)
Synthesize Guidance: Combine answers into structured guidance string for execution
Artifacts to Generate
Primary Deliverables:
- Knowledge Map: Domain expertise assessment →
.aiwg/knowledge/knowledge-map-{domain}.md - Transfer Plan: Structured handoff schedule →
.aiwg/knowledge/transfer-plan-{from}-to-{to}.md - Documentation Package: Updated/created docs →
.aiwg/knowledge/docs/ - Shadowing Logs: Observation records →
.aiwg/knowledge/shadowing/ - Validation Results: Test scenarios and outcomes →
.aiwg/knowledge/validation/ - Handover Checklist: Final signoff document →
.aiwg/knowledge/handover-checklist-{domain}.md - Transfer Report: Completion summary →
.aiwg/reports/knowledge-transfer-report-{domain}.md
Supporting Artifacts:
- Knowledge gap analysis
- Runbook updates
- Training materials
- Follow-up plans
Multi-Agent Orchestration Workflow
Step 1: Knowledge Assessment and Transfer Scope
Purpose: Identify knowledge domain(s) and define transfer scope
Your Actions:
-
Validate Team Members Exist:
Read .aiwg/team/team-profile.yaml (if exists) Verify from-member and to-member are valid team members If not found, proceed with provided names but note in report -
Launch Knowledge Assessment Agents (parallel):
# Agent 1: Knowledge Manager (lead) Task( subagent_type="knowledge-manager", description="Assess knowledge domain and create transfer scope", prompt=""" Create knowledge assessment for transfer: - From: {from-member} - To: {to-member} - Domain: {domain if specified, else "all responsibilities"} Define Knowledge Map: 1. Knowledge Areas (list all relevant areas) 2. Criticality Assessment (Critical, High, Medium, Low) 3. Current State Assessment: - Holder expertise level (Expert, Advanced, Intermediate) - Receiver current level (None, Novice, Beginner, Intermediate) 4. Knowledge Gaps (delta between holder and receiver) 5. Transfer Priority (HIGH, MEDIUM, LOW for each area) Define Transfer Scope: - In Scope: Areas requiring active transfer - Out of Scope: Already documented or low priority - Success Criteria: What defines successful transfer Estimate Timeline: - Based on scope and gaps - Typical: 2-6 weeks Use template if available: $AIWG_ROOT/templates/knowledge/knowledge-map-template.md Output: .aiwg/knowledge/knowledge-map-{domain}.md """ ) # Agent 2: Training Coordinator Task( subagent_type="training-coordinator", description="Create structured transfer plan", prompt=""" Based on knowledge assessment, create transfer plan: Structure: 1. Documentation Phase (Week 1) - Review existing docs - Identify and fill gaps - Create runbooks 2. Shadowing Phase (Week 2-3) - 4-8 observation sessions - Knowledge holder leads, receiver observes - Q&A and note-taking 3. Reverse Shadowing (Week 3-4) - 4-8 practice sessions - Receiver leads, holder observes - Feedback and correction 4. Validation Phase (Week 4-5) - Practical scenarios - Independent operation test - Knowledge verification 5. Handover Phase (Week 5-6) - Final checklist - Signoffs - Follow-up plan Adjust timeline based on: - Scope complexity - Availability constraints - {guidance if provided} Use template if available: $AIWG_ROOT/templates/knowledge/transfer-plan-template.md Output: .aiwg/knowledge/transfer-plan-{from}-to-{to}.md """ ) -
Review and Confirm Scope:
Task( subagent_type="project-manager", description="Review and validate transfer scope", prompt=""" Read: - .aiwg/knowledge/knowledge-map-{domain}.md - .aiwg/knowledge/transfer-plan-{from}-to-{to}.md Validate: - Scope is realistic for timeline - Critical knowledge areas covered - Success criteria are measurable - Plan accounts for constraints Create gate decision: - GO: Proceed with transfer - ADJUST: Modify scope or timeline - ESCALATE: Needs management decision Output validation summary to transfer plan """ )
Communicate Progress:
✓ Knowledge assessment complete ✓ Transfer scope defined: {X} knowledge areas, {Y} weeks estimated ✓ Transfer plan created: .aiwg/knowledge/transfer-plan-{from}-to-{to}.md
Step 2: Documentation Review and Knowledge Artifacts
Purpose: Compile and enhance documentation for knowledge transfer
Your Actions:
-
Inventory Existing Documentation:
# Use Glob to find relevant docs Glob("**/*.md") Glob("**/*.txt") Filter for domain-relevant documentation Create inventory list -
Launch Documentation Agents (parallel):
# Agent 1: Documentation Archivist Task( subagent_type="documentation-archivist", description="Organize and review existing documentation", prompt=""" Domain: {domain} Review existing documentation: 1. Architecture documents 2. Runbooks and procedures 3. Configuration guides 4. Troubleshooting guides 5. Historical incident reports Assess each document: - Currency (up-to-date?) - Completeness (gaps?) - Clarity (understandable?) - Relevance (needed for transfer?) Create Documentation Inventory: - Core Docs (must review) - Reference Docs (good to know) - Archive Docs (historical context) - Missing Docs (gaps to fill) Organize in logical learning sequence Output: .aiwg/knowledge/docs/documentation-inventory.md """ ) # Agent 2: Subject Matter Expert (knowledge holder role) Task( subagent_type="subject-matter-expert", description="Identify and create missing documentation", prompt=""" Acting as {from-member} (knowledge holder perspective) Based on documentation inventory, create missing critical docs: 1. Runbooks for common operations: - Daily/weekly tasks - Deployment procedures - Rollback procedures - Monitoring and alerting 2. Troubleshooting guides: - Common issues and solutions - Debugging techniques - Log analysis patterns - Performance tuning 3. Architecture notes: - Design decisions and rationale - System boundaries and interfaces - Data flows and dependencies - Security considerations 4. Tribal knowledge: - Undocumented gotchas - Historical context ("why it's this way") - Stakeholder relationships - Political/organizational context Focus on practical, hands-on knowledge needed for independent operation Output to: .aiwg/knowledge/docs/{category}/ """ ) # Agent 3: Technical Writer Task( subagent_type="technical-writer", description="Enhance documentation clarity and completeness", prompt=""" Review and enhance documentation for knowledge transfer: Improvements: 1. Add missing context for newcomers 2. Clarify technical jargon 3. Add examples and scenarios 4. Create quick reference guides 5. Add diagrams where helpful Ensure documentation is: - Self-contained (minimal external references) - Progressive (basic → advanced) - Actionable (clear steps) - Verifiable (testable outcomes) Create consolidated reading list in order Output enhanced docs to: .aiwg/knowledge/docs/enhanced/ """ )
Communicate Progress:
⏳ Documentation review in progress... ✓ {X} existing documents inventoried ✓ {Y} documentation gaps identified ✓ {Z} new documents created ✓ Documentation package complete: .aiwg/knowledge/docs/
Step 3: Shadowing Phase (Receiver Observes)
Purpose: Knowledge receiver observes holder performing actual work
Your Actions:
-
Initialize Shadowing Sessions:
# Create session structure mkdir -p .aiwg/knowledge/shadowing/sessions # Define 4-8 sessions based on knowledge areas For each critical knowledge area, allocate 1-2 sessions -
Launch Shadowing Simulation (for each session):
# For each shadowing session (4-8 total) Task( subagent_type="training-coordinator", description="Simulate shadowing session {N}", prompt=""" Shadowing Session {N} Knowledge Area: {area from knowledge map} Duration: 1-2 hours (simulated) Simulate session where {from-member} demonstrates: 1. Task execution (step-by-step) 2. Decision points (what and why) 3. Tool usage (specific commands/interfaces) 4. Common issues (what to watch for) 5. Best practices (efficiency tips) {to-member} perspective: - Observations noted - Questions asked - Concepts clarified - Confidence assessment (1-5) Create session log including: - Tasks demonstrated - Key decisions explained - Questions and answers - Key learnings captured - Follow-up items identified - Confidence rating Output: .aiwg/knowledge/shadowing/sessions/session-{N}-{area}.md """ ) -
Synthesize Shadowing Learnings:
Task( subagent_type="knowledge-manager", description="Synthesize shadowing phase learnings", prompt=""" Read all shadowing session logs Create synthesis: 1. Knowledge areas covered 2. Key learnings consolidated 3. Remaining questions 4. Confidence progression (trend over sessions) 5. Areas needing more practice Identify patterns: - Concepts requiring repetition - Complex areas needing breakdown - Tools requiring hands-on practice Recommend focus for reverse shadowing Output: .aiwg/knowledge/shadowing/shadowing-synthesis.md """ )
Communicate Progress:
⏳ Shadowing phase in progress... ✓ Session 1: Database operations (confidence: 3/5) ✓ Session 2: Deployment procedures (confidence: 2/5) ✓ Session 3: Incident response (confidence: 4/5) ✓ Session 4: Performance tuning (confidence: 2/5) ✓ Shadowing complete: {X} sessions, average confidence: {Y}/5
Step 4: Reverse Shadowing Phase (Receiver Leads)
Purpose: Knowledge receiver performs tasks with holder observing
Your Actions:
-
Plan Reverse Shadowing Sessions:
Based on shadowing synthesis, prioritize: - Low confidence areas (2/5 or below) - Critical operations - Complex procedures -
Launch Reverse Shadowing (for each session):
# For each reverse shadowing session (4-8 total) Task( subagent_type="learner", description="Simulate reverse shadowing session {N}", prompt=""" Reverse Shadowing Session {N} Knowledge Area: {area} Receiver Leading: {to-member} Holder Observing: {from-member} Simulate {to-member} performing tasks: 1. Task approach (how they tackle it) 2. Decision making (choices and reasoning) 3. Challenges faced (what's difficult) 4. Holder interventions (when and why) 5. Corrections made (learning moments) Holder feedback: - What went well - Areas for improvement - Specific corrections - Confidence assessment Success indicators: - Task completed correctly - Minimal interventions needed - Sound reasoning demonstrated Create session log: - Tasks performed - Interventions required - Feedback provided - Outcome (SUCCESS, PARTIAL, NEEDS_PRACTICE) - Confidence growth Output: .aiwg/knowledge/shadowing/reverse/session-{N}-{area}.md """ ) -
Assess Progress and Readiness:
Task( subagent_type="training-coordinator", description="Assess reverse shadowing progress", prompt=""" Read all reverse shadowing sessions Assess readiness: 1. Tasks completed successfully (%) 2. Intervention frequency (trending down?) 3. Confidence ratings (trending up?) 4. Decision quality (sound reasoning?) For each knowledge area: - Status: READY | NEEDS_PRACTICE | NOT_READY - Remaining gaps - Recommended actions Overall assessment: - Ready for validation: YES/NO - Areas needing more practice - Estimated additional time needed Output: .aiwg/knowledge/shadowing/reverse/readiness-assessment.md """ )
Communicate Progress:
⏳ Reverse shadowing in progress... ✓ Session 1: Database operations (SUCCESS, minimal intervention) ✓ Session 2: Deployment procedures (PARTIAL, 2 interventions) ✓ Session 3: Incident response (SUCCESS, no intervention) ⚠️ Session 4: Performance tuning (NEEDS_PRACTICE, multiple interventions) ✓ Reverse shadowing complete: 75% success rate
Step 5: Knowledge Validation and Practical Testing
Purpose: Validate knowledge acquisition through realistic scenarios
Your Actions:
-
Create Validation Scenarios:
Task( subagent_type="test-architect", description="Design validation scenarios", prompt=""" Based on knowledge domain {domain}, create 4 validation scenarios: Scenario 1: Routine Operation - Common daily/weekly task - Expected to complete independently - Time limit: reasonable for task Scenario 2: Troubleshooting - Realistic problem to diagnose and fix - Tests analytical skills - Multiple solution paths acceptable Scenario 3: Teach-Back - Explain concept to simulated junior member - Tests depth of understanding - Must be accurate and clear Scenario 4: Novel Situation - New problem not explicitly covered - Tests knowledge application - Reasonable extrapolation expected Each scenario includes: - Context and setup - Success criteria - Evaluation rubric - Time expectations Output: .aiwg/knowledge/validation/validation-scenarios.md """ ) -
Execute Validation Tests (parallel where possible):
# For each validation scenario Task( subagent_type="learner", description="Execute validation scenario {N}", prompt=""" As {to-member}, complete validation scenario {N} Demonstrate: 1. Understanding of the problem 2. Systematic approach 3. Correct solution or diagnosis 4. Appropriate tool usage 5. Documentation of actions For teach-back scenario: - Explain clearly - Use examples - Check understanding For novel situation: - Show problem-solving process - Use available resources - Apply learned principles Document: - Approach taken - Solution provided - Time taken - Confidence level - Resources consulted Output: .aiwg/knowledge/validation/scenario-{N}-results.md """ ) # Parallel evaluation by holder Task( subagent_type="subject-matter-expert", description="Evaluate validation scenarios", prompt=""" As {from-member}, evaluate {to-member}'s performance For each scenario: - Accuracy (correct solution?) - Approach (systematic and logical?) - Efficiency (reasonable time?) - Independence (minimal help needed?) - Documentation (clear and complete?) Rating scale: - EXCELLENT: Exceeds expectations - PASS: Meets requirements - CONDITIONAL: Mostly correct, minor gaps - FAIL: Significant gaps, more practice needed Provide specific feedback: - What was done well - Areas for improvement - Recommendations Overall readiness assessment: - READY for independent operation - READY with support period - NOT READY, need more practice Output: .aiwg/knowledge/validation/evaluation-results.md """ )
Communicate Progress:
⏳ Validation testing in progress... ✓ Scenario 1 (Routine): PASS ✓ Scenario 2 (Troubleshooting): PASS ✓ Scenario 3 (Teach-Back): EXCELLENT ⚠️ Scenario 4 (Novel): CONDITIONAL (minor gaps noted) ✓ Validation complete: 3/4 PASS or better
Step 6: Handover Checklist and Signoff
Purpose: Complete formal handover with all parties signing off
Your Actions:
-
Generate Handover Checklist:
Task( subagent_type="project-manager", description="Create comprehensive handover checklist", prompt=""" Create handover checklist for: - Domain: {domain} - From: {from-member} - To: {to-member} - Duration: {weeks from start to now} Checklist sections: 1. Documentation - All docs reviewed: YES/NO - Gaps addressed: YES/NO - Bookmarks/access: YES/NO 2. Practical Skills - Routine tasks: {validation results} - Troubleshooting: {validation results} - Emergency procedures: UNDERSTOOD/PRACTICED 3. Knowledge Validation - Scenarios passed: {X}/4 - Teach-back successful: YES/NO - Holder confidence: {rating} 4. Access and Permissions - System access: GRANTED/PENDING - Tool access: GRANTED/PENDING - Communication channels: ADDED/PENDING 5. Operational Handoff - On-call rotation: UPDATED/PENDING - Responsibility matrix: UPDATED/PENDING - Stakeholder notification: SENT/PENDING 6. Follow-Up Plan - 1-week check-in: {date} - 1-month check-in: {date} - Support period: {duration} 7. Residual Gaps (if any) - List with severity and remediation plan Use template if available: $AIWG_ROOT/templates/knowledge/handover-checklist-template.md Output: .aiwg/knowledge/handover-checklist-{domain}.md """ ) -
Collect Signoffs:
Task( subagent_type="project-manager", description="Collect handover signoffs", prompt=""" Document signoffs for handover: Required signatures: 1. Knowledge Receiver ({to-member}): "I am confident in my ability to perform {domain} responsibilities independently" Confidence level: {1-5} Concerns (if any): {list} 2. Knowledge Holder ({from-member}): "I am confident the receiver has the knowledge to succeed independently" Confidence level: {1-5} Recommendations: {list} 3. Project Manager: "Knowledge transfer is complete and receiver is ready for independent operation" Decision: APPROVED / CONDITIONAL / NOT_APPROVED Conditional requirements (if CONDITIONAL): - What must be completed - Timeline for completion - Re-validation plan Add signatures to handover checklist """ ) -
Generate Final Report:
Task( subagent_type="knowledge-manager", description="Generate knowledge transfer completion report", prompt=""" Create comprehensive transfer report including: 1. Executive Summary - Transfer status: COMPLETE/PARTIAL/INCOMPLETE - Readiness: READY/CONDITIONAL/NOT_READY - Key outcomes 2. Transfer Summary - Scope (knowledge areas covered) - Timeline (planned vs actual) - Methods (shadowing, documentation, validation) 3. Knowledge Acquisition Metrics - Shadowing sessions: {count} - Reverse shadowing: {count} - Validation scenarios: {passed}/{total} - Confidence progression: {start} → {end} 4. Documentation Improvements - Docs created: {count} - Docs enhanced: {count} - Remaining gaps: {list} 5. Validation Results - Detailed scenario outcomes - Evaluator feedback - Areas of strength - Areas for improvement 6. Lessons Learned - What worked well - What could improve - Recommendations for future transfers 7. Follow-Up Plan - Check-in schedule - Support arrangements - Escalation path 8. Risk Assessment - Operational risks - Mitigation strategies - Contingency plans Output: .aiwg/reports/knowledge-transfer-report-{domain}.md """ )
Communicate Progress:
✓ Handover checklist complete: .aiwg/knowledge/handover-checklist-{domain}.md ✓ All parties signed off ✓ Transfer report generated: .aiwg/reports/knowledge-transfer-report-{domain}.md
Quality Gates
Before marking workflow complete, verify:
- Knowledge assessment documented
- Transfer plan created and followed
- Documentation gaps addressed
- Shadowing sessions completed (minimum 4)
- Reverse shadowing completed (minimum 4)
- Validation scenarios passed (≥75%)
- Handover checklist complete
- All required signoffs obtained
- Follow-up plan established
User Communication
At start: Confirm understanding and outline process
Understood. I'll orchestrate the knowledge transfer from {from-member} to {to-member} for {domain}. This will include: - Knowledge assessment and gap analysis - Documentation review and enhancement - Shadowing sessions (observation) - Reverse shadowing (practice) - Validation testing - Formal handover and signoff Expected duration: 30-45 minutes orchestration. Real-world timeline: 2-6 weeks for actual transfer. Starting orchestration...
During: Update progress with clear indicators
✓ = Complete ⏳ = In progress ⚠️ = Attention needed ❌ = Failed/blocked
At end: Summary report with status and next steps
───────────────────────────────────────────── Knowledge Transfer Complete ───────────────────────────────────────────── **Transfer**: {from-member} → {to-member} **Domain**: {domain} **Status**: COMPLETE **Readiness**: READY FOR INDEPENDENT OPERATION **Summary**: ✓ Knowledge gaps identified and addressed ✓ Documentation: {X} docs created/updated ✓ Shadowing: {Y} sessions completed ✓ Validation: {Z}/4 scenarios passed ✓ Handover: All parties signed off **Confidence Assessment**: - Receiver confidence: 4/5 - Holder confidence: 4/5 - Manager approval: APPROVED **Follow-Up Plan**: - 1-week check-in: {date} - 1-month review: {date} - Support period: {from-member} available for {duration} **Artifacts Generated**: - Knowledge Map: .aiwg/knowledge/knowledge-map-{domain}.md - Transfer Plan: .aiwg/knowledge/transfer-plan-{from}-to-{to}.md - Documentation: .aiwg/knowledge/docs/ - Validation Results: .aiwg/knowledge/validation/ - Handover Checklist: .aiwg/knowledge/handover-checklist-{domain}.md - Final Report: .aiwg/reports/knowledge-transfer-report-{domain}.md **Next Steps**: - Update team roster and responsibilities - Schedule follow-up check-ins - Monitor initial independent operation - Address any residual gaps per remediation plan ─────────────────────────────────────────────
Error Handling
Team Member Not Found:
⚠️ Team member not found in roster Proceeding with provided names: {from-member} → {to-member} Note: Consider updating .aiwg/team/team-profile.yaml
Knowledge Domain Unclear:
⚠️ Knowledge domain not specified Defaulting to: "all responsibilities" This may extend timeline and scope. Recommendation: Specify domain for focused transfer Example: "backend-api", "deployment", "security"
Validation Failure:
❌ Validation scenario failed: {scenario} Result: {failure-reason} Impact: Receiver not ready for independent operation Recommendations: 1. Additional practice in {area} 2. Review relevant documentation 3. Schedule extra reverse shadowing session 4. Re-attempt validation after practice
Insufficient Confidence:
⚠️ Low confidence detected Receiver confidence: {X}/5 (target: ≥3) Holder confidence: {Y}/5 (target: ≥3) Actions: 1. Identify specific concern areas 2. Provide additional shadowing/practice 3. Consider extended support period 4. Document contingency plans
Timeline Overrun:
⚠️ Transfer taking longer than planned Original estimate: {X} weeks Current duration: {Y} weeks Factors: - Complexity underestimated - Availability constraints - Additional gaps discovered Recommendation: Adjust timeline and expectations
Success Criteria
This orchestration succeeds when:
- Knowledge gaps identified and documented
- Transfer scope agreed by all parties
- Documentation complete and accessible
- Minimum 4 shadowing sessions completed
- Minimum 4 reverse shadowing sessions completed
- ≥75% validation scenarios passed
- Receiver confidence ≥3/5
- Holder confidence ≥3/5
- Handover checklist signed off
- Follow-up plan established
Metrics to Track
During orchestration, track:
- Documentation coverage: % of knowledge areas documented
- Shadowing completion: # sessions completed vs planned
- Confidence progression: Rating trend over time
- Validation pass rate: % scenarios passed first attempt
- Time to competency: Weeks from start to signoff
References
Templates (via $AIWG_ROOT):
- Knowledge Map:
templates/knowledge/knowledge-map-template.md - Transfer Plan:
templates/knowledge/transfer-plan-template.md - Shadowing Log:
templates/knowledge/shadowing-log-template.md - Validation Checklist:
templates/knowledge/knowledge-validation-checklist.md - Handover Checklist:
templates/knowledge/handover-checklist-template.md
Related Commands:
- Update team responsibilities/team-roster
- Modify on-call schedules/update-oncall
- Full team member onboarding/flow-onboarding
Best Practices:
docs/knowledge-transfer-best-practices.mddocs/shadowing-techniques.mddocs/validation-scenario-design.md