[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6C2RWDF2GG0B42FBWZRB44Z":3,"skill-same:01M6C2RWDF2GG0B42FBWZRB44Z":85},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":62,"commitSha":63,"skillDirectory":64,"forksCount":65,"repositoryHtmlUrl":66,"repositoryHomepageUrl":67,"ownerHtmlUrl":68,"ownerBlogUrl":69,"ownerDescription":70,"updatedAt":71,"install":72,"resources":84},"01M6C2RWDF2GG0B42FBWZRB44Z","flow-knowledge-transfer",null,"Orchestrate Knowledge Transfer flow with assessment, documentation, shadowing, validation, and handover","jmagly\u002Faiwg","jmagly","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F1159087?v=4","01M6C10AK8N2WCSQN11FYGTJWN","Joseph Magly","01M6C10AQ8A2RMED1Y1BSVY7NF",229,"MIT","https:\u002F\u002Fgithub.com\u002Fjmagly\u002Faiwg\u002Fblob\u002Fmain\u002Fagentic\u002Fcode\u002Fplugins\u002Fcodex-sdlc\u002Fskills\u002Fflow-knowledge-transfer\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"transfer","flow","knowledge","knowledge transfer","flow knowledge transfer","read","standard","true","sonnet","domain","task","sdlc","interactive","write","orchestration","coding","text","handover","orchestrate","validation","assessment","documentation","2026-11-10 10:12:30",false,"---\nnamespace: aiwg\nname: flow-knowledge-transfer\nplatforms: [all]\ndescription: Orchestrate Knowledge Transfer flow with assessment, documentation, shadowing, validation, and handover\ncommandHint:\n  argumentHint: \u003Cfrom-member> \u003Cto-member> [domain] [--guidance \"text\"] [--interactive]\n  allowedTools: Task, Read, Write, Glob, TodoWrite\n  model: sonnet\n  category: sdlc-orchestration\n  orchestration: true\n  modelRole: coding\n  modelTier: standard\n---\n\n> **Declarative Flow (#1539):** This flow's orchestration is now also expressed as a\n> declarative YAML Flow at `flows\u002Fflow-knowledge-transfer.playbook.yaml` (source of truth for the step\n> sequence + gates). This SKILL.md remains the discoverable trigger surface and prose\n> reference. See epic #1534.\n\n\u003C!-- AIWG-SKILL-CALLOUT -->\n> **Skill access pattern (post-kernel-pivot, 2026.5+)**\n>\n> Skill names referenced in this document are AIWG skills, **not slash commands**. Most are not kernel-listed and cannot be invoked as `\u002Fskill-name` by the platform. Reach them via:\n>\n> ```bash\n> aiwg discover \"\u003Ccapability>\"\n> aiwg show skill \u003Cname> --body\n> ```\n>\n> Only kernel-listed skills (`aiwg-doctor`, `aiwg-refresh`, `aiwg-status`, `aiwg-help`, `use`, `steward`) are directly invokable as slash commands. See [skill-discovery rule](https:\u002F\u002Fgithub.com\u002Fjmagly\u002Faiwg\u002Fblob\u002Fmain\u002Fagentic\u002Fcode\u002Faddons\u002Faiwg-utils\u002Frules\u002Fskill-discovery.md).\n\n\n# Knowledge Transfer Orchestration Flow\n\n**You are the Core Orchestrator** for structured knowledge transfer between team members.\n\n## Your Role\n\n**You orchestrate multi-agent workflows. You do NOT execute bash scripts.**\n\nWhen the user requests this flow (via natural language or explicit command):\n\n1. **Interpret the request** and confirm understanding\n2. **Read this template** as your orchestration guide\n3. **Extract agent assignments** and workflow steps\n4. **Delegate agents with the current provider-native orchestration mechanism** in the correct sequence\n5. **Synthesize results** and finalize artifacts\n6. **Report completion** with summary\n\n## Knowledge Transfer Overview\n\n**Purpose**: Ensure continuity when team members transition roles, leave projects, or hand off domain expertise\n\n**Key Milestone**: Knowledge Transfer Signoff\n\n**Success Criteria**:\n- Knowledge gaps identified and addressed\n- Documentation complete and reviewed\n- Shadowing and reverse shadowing completed\n- Practical validation passed\n- Handover checklist signed off\n\n**Expected Duration**: 2-6 weeks (typical), 30-45 minutes orchestration\n\n## Natural Language Triggers\n\nUsers may say:\n- \"Knowledge transfer from Alice to Bob\"\n- \"Handoff backend responsibilities to new team member\"\n- \"Transfer knowledge from {from} to {to}\"\n- \"Documentation handoff for {domain}\"\n- \"Onboard new team member to {area}\"\n\nYou recognize these as requests for this orchestration flow.\n\n## Parameter Handling\n\n### Required Parameters\n\n- **from-member**: The team member transferring knowledge (knowledge holder)\n- **to-member**: The team member receiving knowledge (knowledge receiver)\n- **domain** (optional): Specific knowledge domain (e.g., \"backend-api\", \"deployment\", \"security\")\n\n### --guidance Parameter\n\n**Purpose**: User provides upfront direction to tailor transfer priorities\n\n**Examples**:\n```\n--guidance \"Focus on production support and incident response procedures\"\n--guidance \"Tight timeline, prioritize critical operational knowledge\"\n--guidance \"Receiver has strong technical background but no domain experience\"\n--guidance \"Include compliance and regulatory knowledge for audit requirements\"\n```\n\n**How to Apply**:\n- Parse guidance for keywords: operations, compliance, security, timeline, experience level\n- Adjust focus areas (operational vs. architectural knowledge)\n- Modify shadowing depth (minimal vs. comprehensive based on timeline)\n- Influence validation scenarios (focus on critical vs. comprehensive testing)\n\n### --interactive Parameter\n\n**Purpose**: You ask 6 strategic questions to understand transfer context\n\n**Questions to Ask** (if --interactive):\n\n```\nI'll ask 6 strategic questions to tailor the knowledge transfer to your needs:\n\nQ1: What are your top priorities for this knowledge transfer?\n    (e.g., operational continuity, architectural understanding, troubleshooting skills)\n\nQ2: What are your biggest constraints?\n    (e.g., timeline, availability of knowledge holder, complexity of domain)\n\nQ3: What risks concern you most for this transfer?\n    (e.g., critical knowledge loss, insufficient practice time, documentation gaps)\n\nQ4: What's the receiver's experience level with similar domains?\n    (Helps calibrate transfer depth and pace)\n\nQ5: What's your target timeline for independent operation?\n    (Influences shadowing duration and validation rigor)\n\nQ6: Are there compliance or regulatory requirements?\n    (e.g., SOX separation of duties, HIPAA training requirements)\n\nBased on your answers, I'll adjust:\n- Focus areas (operational vs. architectural vs. compliance)\n- Shadowing duration (standard vs. extended)\n- Validation rigor (basic vs. comprehensive)\n- Documentation depth (reference vs. tutorial)\n```\n\n**Synthesize Guidance**: Combine answers into structured guidance string for execution\n\n## Artifacts to Generate\n\n**Primary Deliverables**:\n- **Knowledge Map**: Domain expertise assessment → `.aiwg\u002Fknowledge\u002Fknowledge-map-{domain}.md`\n- **Transfer Plan**: Structured handoff schedule → `.aiwg\u002Fknowledge\u002Ftransfer-plan-{from}-to-{to}.md`\n- **Documentation Package**: Updated\u002Fcreated docs → `.aiwg\u002Fknowledge\u002Fdocs\u002F`\n- **Shadowing Logs**: Observation records → `.aiwg\u002Fknowledge\u002Fshadowing\u002F`\n- **Validation Results**: Test scenarios and outcomes → `.aiwg\u002Fknowledge\u002Fvalidation\u002F`\n- **Handover Checklist**: Final signoff document → `.aiwg\u002Fknowledge\u002Fhandover-checklist-{domain}.md`\n- **Transfer Report**: Completion summary → `.aiwg\u002Freports\u002Fknowledge-transfer-report-{domain}.md`\n\n**Supporting Artifacts**:\n- Knowledge gap analysis\n- Runbook updates\n- Training materials\n- Follow-up plans\n\n## Multi-Agent Orchestration Workflow\n\n### Step 1: Knowledge Assessment and Transfer Scope\n\n**Purpose**: Identify knowledge domain(s) and define transfer scope\n\n**Your Actions**:\n\n1. **Validate Team Members Exist**:\n   ```\n   Read .aiwg\u002Fteam\u002Fteam-profile.yaml (if exists)\n   Verify from-member and to-member are valid team members\n   If not found, proceed with provided names but note in report\n   ```\n\n2. **Launch Knowledge Assessment Agents** (parallel):\n   ```\n   # Agent 1: Knowledge Manager (lead)\n   Task(\n       subagent_type=\"knowledge-manager\",\n       description=\"Assess knowledge domain and create transfer scope\",\n       prompt=\"\"\"\n       Create knowledge assessment for transfer:\n       - From: {from-member}\n       - To: {to-member}\n       - Domain: {domain if specified, else \"all responsibilities\"}\n\n       Define Knowledge Map:\n       1. Knowledge Areas (list all relevant areas)\n       2. Criticality Assessment (Critical, High, Medium, Low)\n       3. Current State Assessment:\n          - Holder expertise level (Expert, Advanced, Intermediate)\n          - Receiver current level (None, Novice, Beginner, Intermediate)\n       4. Knowledge Gaps (delta between holder and receiver)\n       5. Transfer Priority (HIGH, MEDIUM, LOW for each area)\n\n       Define Transfer Scope:\n       - In Scope: Areas requiring active transfer\n       - Out of Scope: Already documented or low priority\n       - Success Criteria: What defines successful transfer\n\n       Estimate Timeline:\n       - Based on scope and gaps\n       - Typical: 2-6 weeks\n\n       Use template if available: $AIWG_ROOT\u002Ftemplates\u002Fknowledge\u002Fknowledge-map-template.md\n\n       Output: .aiwg\u002Fknowledge\u002Fknowledge-map-{domain}.md\n       \"\"\"\n   )\n\n   # Agent 2: Training Coordinator\n   Task(\n       subagent_type=\"training-coordinator\",\n       description=\"Create structured transfer plan\",\n       prompt=\"\"\"\n       Based on knowledge assessment, create transfer plan:\n\n       Structure:\n       1. Documentation Phase (Week 1)\n          - Review existing docs\n          - Identify and fill gaps\n          - Create runbooks\n\n       2. Shadowing Phase (Week 2-3)\n          - 4-8 observation sessions\n          - Knowledge holder leads, receiver observes\n          - Q&A and note-taking\n\n       3. Reverse Shadowing (Week 3-4)\n          - 4-8 practice sessions\n          - Receiver leads, holder observes\n          - Feedback and correction\n\n       4. Validation Phase (Week 4-5)\n          - Practical scenarios\n          - Independent operation test\n          - Knowledge verification\n\n       5. Handover Phase (Week 5-6)\n          - Final checklist\n          - Signoffs\n          - Follow-up plan\n\n       Adjust timeline based on:\n       - Scope complexity\n       - Availability constraints\n       - {guidance if provided}\n\n       Use template if available: $AIWG_ROOT\u002Ftemplates\u002Fknowledge\u002Ftransfer-plan-template.md\n\n       Output: .aiwg\u002Fknowledge\u002Ftransfer-plan-{from}-to-{to}.md\n       \"\"\"\n   )\n   ```\n\n3. **Review and Confirm Scope**:\n   ```\n   Task(\n       subagent_type=\"project-manager\",\n       description=\"Review and validate transfer scope\",\n       prompt=\"\"\"\n       Read:\n       - .aiwg\u002Fknowledge\u002Fknowledge-map-{domain}.md\n       - .aiwg\u002Fknowledge\u002Ftransfer-plan-{from}-to-{to}.md\n\n       Validate:\n       - Scope is realistic for timeline\n       - Critical knowledge areas covered\n       - Success criteria are measurable\n       - Plan accounts for constraints\n\n       Create gate decision:\n       - GO: Proceed with transfer\n       - ADJUST: Modify scope or timeline\n       - ESCALATE: Needs management decision\n\n       Output validation summary to transfer plan\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n✓ Knowledge assessment complete\n✓ Transfer scope defined: {X} knowledge areas, {Y} weeks estimated\n✓ Transfer plan created: .aiwg\u002Fknowledge\u002Ftransfer-plan-{from}-to-{to}.md\n```\n\n### Step 2: Documentation Review and Knowledge Artifacts\n\n**Purpose**: Compile and enhance documentation for knowledge transfer\n\n**Your Actions**:\n\n1. **Inventory Existing Documentation**:\n   ```\n   # Use Glob to find relevant docs\n   Glob(\"**\u002F*.md\")\n   Glob(\"**\u002F*.txt\")\n\n   Filter for domain-relevant documentation\n   Create inventory list\n   ```\n\n2. **Launch Documentation Agents** (parallel):\n   ```\n   # Agent 1: Documentation Archivist\n   Task(\n       subagent_type=\"documentation-archivist\",\n       description=\"Organize and review existing documentation\",\n       prompt=\"\"\"\n       Domain: {domain}\n\n       Review existing documentation:\n       1. Architecture documents\n       2. Runbooks and procedures\n       3. Configuration guides\n       4. Troubleshooting guides\n       5. Historical incident reports\n\n       Assess each document:\n       - Currency (up-to-date?)\n       - Completeness (gaps?)\n       - Clarity (understandable?)\n       - Relevance (needed for transfer?)\n\n       Create Documentation Inventory:\n       - Core Docs (must review)\n       - Reference Docs (good to know)\n       - Archive Docs (historical context)\n       - Missing Docs (gaps to fill)\n\n       Organize in logical learning sequence\n\n       Output: .aiwg\u002Fknowledge\u002Fdocs\u002Fdocumentation-inventory.md\n       \"\"\"\n   )\n\n   # Agent 2: Subject Matter Expert (knowledge holder role)\n   Task(\n       subagent_type=\"subject-matter-expert\",\n       description=\"Identify and create missing documentation\",\n       prompt=\"\"\"\n       Acting as {from-member} (knowledge holder perspective)\n\n       Based on documentation inventory, create missing critical docs:\n\n       1. Runbooks for common operations:\n          - Daily\u002Fweekly tasks\n          - Deployment procedures\n          - Rollback procedures\n          - Monitoring and alerting\n\n       2. Troubleshooting guides:\n          - Common issues and solutions\n          - Debugging techniques\n          - Log analysis patterns\n          - Performance tuning\n\n       3. Architecture notes:\n          - Design decisions and rationale\n          - System boundaries and interfaces\n          - Data flows and dependencies\n          - Security considerations\n\n       4. Tribal knowledge:\n          - Undocumented gotchas\n          - Historical context (\"why it's this way\")\n          - Stakeholder relationships\n          - Political\u002Forganizational context\n\n       Focus on practical, hands-on knowledge needed for independent operation\n\n       Output to: .aiwg\u002Fknowledge\u002Fdocs\u002F{category}\u002F\n       \"\"\"\n   )\n\n   # Agent 3: Technical Writer\n   Task(\n       subagent_type=\"technical-writer\",\n       description=\"Enhance documentation clarity and completeness\",\n       prompt=\"\"\"\n       Review and enhance documentation for knowledge transfer:\n\n       Improvements:\n       1. Add missing context for newcomers\n       2. Clarify technical jargon\n       3. Add examples and scenarios\n       4. Create quick reference guides\n       5. Add diagrams where helpful\n\n       Ensure documentation is:\n       - Self-contained (minimal external references)\n       - Progressive (basic → advanced)\n       - Actionable (clear steps)\n       - Verifiable (testable outcomes)\n\n       Create consolidated reading list in order\n\n       Output enhanced docs to: .aiwg\u002Fknowledge\u002Fdocs\u002Fenhanced\u002F\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Documentation review in progress...\n  ✓ {X} existing documents inventoried\n  ✓ {Y} documentation gaps identified\n  ✓ {Z} new documents created\n✓ Documentation package complete: .aiwg\u002Fknowledge\u002Fdocs\u002F\n```\n\n### Step 3: Shadowing Phase (Receiver Observes)\n\n**Purpose**: Knowledge receiver observes holder performing actual work\n\n**Your Actions**:\n\n1. **Initialize Shadowing Sessions**:\n   ```\n   # Create session structure\n   mkdir -p .aiwg\u002Fknowledge\u002Fshadowing\u002Fsessions\n\n   # Define 4-8 sessions based on knowledge areas\n   For each critical knowledge area, allocate 1-2 sessions\n   ```\n\n2. **Launch Shadowing Simulation** (for each session):\n   ```\n   # For each shadowing session (4-8 total)\n   Task(\n       subagent_type=\"training-coordinator\",\n       description=\"Simulate shadowing session {N}\",\n       prompt=\"\"\"\n       Shadowing Session {N}\n       Knowledge Area: {area from knowledge map}\n       Duration: 1-2 hours (simulated)\n\n       Simulate session where {from-member} demonstrates:\n       1. Task execution (step-by-step)\n       2. Decision points (what and why)\n       3. Tool usage (specific commands\u002Finterfaces)\n       4. Common issues (what to watch for)\n       5. Best practices (efficiency tips)\n\n       {to-member} perspective:\n       - Observations noted\n       - Questions asked\n       - Concepts clarified\n       - Confidence assessment (1-5)\n\n       Create session log including:\n       - Tasks demonstrated\n       - Key decisions explained\n       - Questions and answers\n       - Key learnings captured\n       - Follow-up items identified\n       - Confidence rating\n\n       Output: .aiwg\u002Fknowledge\u002Fshadowing\u002Fsessions\u002Fsession-{N}-{area}.md\n       \"\"\"\n   )\n   ```\n\n3. **Synthesize Shadowing Learnings**:\n   ```\n   Task(\n       subagent_type=\"knowledge-manager\",\n       description=\"Synthesize shadowing phase learnings\",\n       prompt=\"\"\"\n       Read all shadowing session logs\n\n       Create synthesis:\n       1. Knowledge areas covered\n       2. Key learnings consolidated\n       3. Remaining questions\n       4. Confidence progression (trend over sessions)\n       5. Areas needing more practice\n\n       Identify patterns:\n       - Concepts requiring repetition\n       - Complex areas needing breakdown\n       - Tools requiring hands-on practice\n\n       Recommend focus for reverse shadowing\n\n       Output: .aiwg\u002Fknowledge\u002Fshadowing\u002Fshadowing-synthesis.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Shadowing phase in progress...\n  ✓ Session 1: Database operations (confidence: 3\u002F5)\n  ✓ Session 2: Deployment procedures (confidence: 2\u002F5)\n  ✓ Session 3: Incident response (confidence: 4\u002F5)\n  ✓ Session 4: Performance tuning (confidence: 2\u002F5)\n✓ Shadowing complete: {X} sessions, average confidence: {Y}\u002F5\n```\n\n### Step 4: Reverse Shadowing Phase (Receiver Leads)\n\n**Purpose**: Knowledge receiver performs tasks with holder observing\n\n**Your Actions**:\n\n1. **Plan Reverse Shadowing Sessions**:\n   ```\n   Based on shadowing synthesis, prioritize:\n   - Low confidence areas (2\u002F5 or below)\n   - Critical operations\n   - Complex procedures\n   ```\n\n2. **Launch Reverse Shadowing** (for each session):\n   ```\n   # For each reverse shadowing session (4-8 total)\n   Task(\n       subagent_type=\"learner\",\n       description=\"Simulate reverse shadowing session {N}\",\n       prompt=\"\"\"\n       Reverse Shadowing Session {N}\n       Knowledge Area: {area}\n       Receiver Leading: {to-member}\n       Holder Observing: {from-member}\n\n       Simulate {to-member} performing tasks:\n       1. Task approach (how they tackle it)\n       2. Decision making (choices and reasoning)\n       3. Challenges faced (what's difficult)\n       4. Holder interventions (when and why)\n       5. Corrections made (learning moments)\n\n       Holder feedback:\n       - What went well\n       - Areas for improvement\n       - Specific corrections\n       - Confidence assessment\n\n       Success indicators:\n       - Task completed correctly\n       - Minimal interventions needed\n       - Sound reasoning demonstrated\n\n       Create session log:\n       - Tasks performed\n       - Interventions required\n       - Feedback provided\n       - Outcome (SUCCESS, PARTIAL, NEEDS_PRACTICE)\n       - Confidence growth\n\n       Output: .aiwg\u002Fknowledge\u002Fshadowing\u002Freverse\u002Fsession-{N}-{area}.md\n       \"\"\"\n   )\n   ```\n\n3. **Assess Progress and Readiness**:\n   ```\n   Task(\n       subagent_type=\"training-coordinator\",\n       description=\"Assess reverse shadowing progress\",\n       prompt=\"\"\"\n       Read all reverse shadowing sessions\n\n       Assess readiness:\n       1. Tasks completed successfully (%)\n       2. Intervention frequency (trending down?)\n       3. Confidence ratings (trending up?)\n       4. Decision quality (sound reasoning?)\n\n       For each knowledge area:\n       - Status: READY | NEEDS_PRACTICE | NOT_READY\n       - Remaining gaps\n       - Recommended actions\n\n       Overall assessment:\n       - Ready for validation: YES\u002FNO\n       - Areas needing more practice\n       - Estimated additional time needed\n\n       Output: .aiwg\u002Fknowledge\u002Fshadowing\u002Freverse\u002Freadiness-assessment.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Reverse shadowing in progress...\n  ✓ Session 1: Database operations (SUCCESS, minimal intervention)\n  ✓ Session 2: Deployment procedures (PARTIAL, 2 interventions)\n  ✓ Session 3: Incident response (SUCCESS, no intervention)\n  ⚠️ Session 4: Performance tuning (NEEDS_PRACTICE, multiple interventions)\n✓ Reverse shadowing complete: 75% success rate\n```\n\n### Step 5: Knowledge Validation and Practical Testing\n\n**Purpose**: Validate knowledge acquisition through realistic scenarios\n\n**Your Actions**:\n\n1. **Create Validation Scenarios**:\n   ```\n   Task(\n       subagent_type=\"test-architect\",\n       description=\"Design validation scenarios\",\n       prompt=\"\"\"\n       Based on knowledge domain {domain}, create 4 validation scenarios:\n\n       Scenario 1: Routine Operation\n       - Common daily\u002Fweekly task\n       - Expected to complete independently\n       - Time limit: reasonable for task\n\n       Scenario 2: Troubleshooting\n       - Realistic problem to diagnose and fix\n       - Tests analytical skills\n       - Multiple solution paths acceptable\n\n       Scenario 3: Teach-Back\n       - Explain concept to simulated junior member\n       - Tests depth of understanding\n       - Must be accurate and clear\n\n       Scenario 4: Novel Situation\n       - New problem not explicitly covered\n       - Tests knowledge application\n       - Reasonable extrapolation expected\n\n       Each scenario includes:\n       - Context and setup\n       - Success criteria\n       - Evaluation rubric\n       - Time expectations\n\n       Output: .aiwg\u002Fknowledge\u002Fvalidation\u002Fvalidation-scenarios.md\n       \"\"\"\n   )\n   ```\n\n2. **Execute Validation Tests** (parallel where possible):\n   ```\n   # For each validation scenario\n   Task(\n       subagent_type=\"learner\",\n       description=\"Execute validation scenario {N}\",\n       prompt=\"\"\"\n       As {to-member}, complete validation scenario {N}\n\n       Demonstrate:\n       1. Understanding of the problem\n       2. Systematic approach\n       3. Correct solution or diagnosis\n       4. Appropriate tool usage\n       5. Documentation of actions\n\n       For teach-back scenario:\n       - Explain clearly\n       - Use examples\n       - Check understanding\n\n       For novel situation:\n       - Show problem-solving process\n       - Use available resources\n       - Apply learned principles\n\n       Document:\n       - Approach taken\n       - Solution provided\n       - Time taken\n       - Confidence level\n       - Resources consulted\n\n       Output: .aiwg\u002Fknowledge\u002Fvalidation\u002Fscenario-{N}-results.md\n       \"\"\"\n   )\n\n   # Parallel evaluation by holder\n   Task(\n       subagent_type=\"subject-matter-expert\",\n       description=\"Evaluate validation scenarios\",\n       prompt=\"\"\"\n       As {from-member}, evaluate {to-member}'s performance\n\n       For each scenario:\n       - Accuracy (correct solution?)\n       - Approach (systematic and logical?)\n       - Efficiency (reasonable time?)\n       - Independence (minimal help needed?)\n       - Documentation (clear and complete?)\n\n       Rating scale:\n       - EXCELLENT: Exceeds expectations\n       - PASS: Meets requirements\n       - CONDITIONAL: Mostly correct, minor gaps\n       - FAIL: Significant gaps, more practice needed\n\n       Provide specific feedback:\n       - What was done well\n       - Areas for improvement\n       - Recommendations\n\n       Overall readiness assessment:\n       - READY for independent operation\n       - READY with support period\n       - NOT READY, need more practice\n\n       Output: .aiwg\u002Fknowledge\u002Fvalidation\u002Fevaluation-results.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Validation testing in progress...\n  ✓ Scenario 1 (Routine): PASS\n  ✓ Scenario 2 (Troubleshooting): PASS\n  ✓ Scenario 3 (Teach-Back): EXCELLENT\n  ⚠️ Scenario 4 (Novel): CONDITIONAL (minor gaps noted)\n✓ Validation complete: 3\u002F4 PASS or better\n```\n\n### Step 6: Handover Checklist and Signoff\n\n**Purpose**: Complete formal handover with all parties signing off\n\n**Your Actions**:\n\n1. **Generate Handover Checklist**:\n   ```\n   Task(\n       subagent_type=\"project-manager\",\n       description=\"Create comprehensive handover checklist\",\n       prompt=\"\"\"\n       Create handover checklist for:\n       - Domain: {domain}\n       - From: {from-member}\n       - To: {to-member}\n       - Duration: {weeks from start to now}\n\n       Checklist sections:\n\n       1. Documentation\n          - All docs reviewed: YES\u002FNO\n          - Gaps addressed: YES\u002FNO\n          - Bookmarks\u002Faccess: YES\u002FNO\n\n       2. Practical Skills\n          - Routine tasks: {validation results}\n          - Troubleshooting: {validation results}\n          - Emergency procedures: UNDERSTOOD\u002FPRACTICED\n\n       3. Knowledge Validation\n          - Scenarios passed: {X}\u002F4\n          - Teach-back successful: YES\u002FNO\n          - Holder confidence: {rating}\n\n       4. Access and Permissions\n          - System access: GRANTED\u002FPENDING\n          - Tool access: GRANTED\u002FPENDING\n          - Communication channels: ADDED\u002FPENDING\n\n       5. Operational Handoff\n          - On-call rotation: UPDATED\u002FPENDING\n          - Responsibility matrix: UPDATED\u002FPENDING\n          - Stakeholder notification: SENT\u002FPENDING\n\n       6. Follow-Up Plan\n          - 1-week check-in: {date}\n          - 1-month check-in: {date}\n          - Support period: {duration}\n\n       7. Residual Gaps (if any)\n          - List with severity and remediation plan\n\n       Use template if available: $AIWG_ROOT\u002Ftemplates\u002Fknowledge\u002Fhandover-checklist-template.md\n\n       Output: .aiwg\u002Fknowledge\u002Fhandover-checklist-{domain}.md\n       \"\"\"\n   )\n   ```\n\n2. **Collect Signoffs**:\n   ```\n   Task(\n       subagent_type=\"project-manager\",\n       description=\"Collect handover signoffs\",\n       prompt=\"\"\"\n       Document signoffs for handover:\n\n       Required signatures:\n       1. Knowledge Receiver ({to-member}):\n          \"I am confident in my ability to perform {domain} responsibilities independently\"\n          Confidence level: {1-5}\n          Concerns (if any): {list}\n\n       2. Knowledge Holder ({from-member}):\n          \"I am confident the receiver has the knowledge to succeed independently\"\n          Confidence level: {1-5}\n          Recommendations: {list}\n\n       3. Project Manager:\n          \"Knowledge transfer is complete and receiver is ready for independent operation\"\n          Decision: APPROVED \u002F CONDITIONAL \u002F NOT_APPROVED\n\n       Conditional requirements (if CONDITIONAL):\n       - What must be completed\n       - Timeline for completion\n       - Re-validation plan\n\n       Add signatures to handover checklist\n       \"\"\"\n   )\n   ```\n\n3. **Generate Final Report**:\n   ```\n   Task(\n       subagent_type=\"knowledge-manager\",\n       description=\"Generate knowledge transfer completion report\",\n       prompt=\"\"\"\n       Create comprehensive transfer report including:\n\n       1. Executive Summary\n          - Transfer status: COMPLETE\u002FPARTIAL\u002FINCOMPLETE\n          - Readiness: READY\u002FCONDITIONAL\u002FNOT_READY\n          - Key outcomes\n\n       2. Transfer Summary\n          - Scope (knowledge areas covered)\n          - Timeline (planned vs actual)\n          - Methods (shadowing, documentation, validation)\n\n       3. Knowledge Acquisition Metrics\n          - Shadowing sessions: {count}\n          - Reverse shadowing: {count}\n          - Validation scenarios: {passed}\u002F{total}\n          - Confidence progression: {start} → {end}\n\n       4. Documentation Improvements\n          - Docs created: {count}\n          - Docs enhanced: {count}\n          - Remaining gaps: {list}\n\n       5. Validation Results\n          - Detailed scenario outcomes\n          - Evaluator feedback\n          - Areas of strength\n          - Areas for improvement\n\n       6. Lessons Learned\n          - What worked well\n          - What could improve\n          - Recommendations for future transfers\n\n       7. Follow-Up Plan\n          - Check-in schedule\n          - Support arrangements\n          - Escalation path\n\n       8. Risk Assessment\n          - Operational risks\n          - Mitigation strategies\n          - Contingency plans\n\n       Output: .aiwg\u002Freports\u002Fknowledge-transfer-report-{domain}.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n✓ Handover checklist complete: .aiwg\u002Fknowledge\u002Fhandover-checklist-{domain}.md\n✓ All parties signed off\n✓ Transfer report generated: .aiwg\u002Freports\u002Fknowledge-transfer-report-{domain}.md\n```\n\n## Quality Gates\n\nBefore marking workflow complete, verify:\n- [ ] Knowledge assessment documented\n- [ ] Transfer plan created and followed\n- [ ] Documentation gaps addressed\n- [ ] Shadowing sessions completed (minimum 4)\n- [ ] Reverse shadowing completed (minimum 4)\n- [ ] Validation scenarios passed (≥75%)\n- [ ] Handover checklist complete\n- [ ] All required signoffs obtained\n- [ ] Follow-up plan established\n\n## User Communication\n\n**At start**: Confirm understanding and outline process\n\n```\nUnderstood. I'll orchestrate the knowledge transfer from {from-member} to {to-member} for {domain}.\n\nThis will include:\n- Knowledge assessment and gap analysis\n- Documentation review and enhancement\n- Shadowing sessions (observation)\n- Reverse shadowing (practice)\n- Validation testing\n- Formal handover and signoff\n\nExpected duration: 30-45 minutes orchestration.\nReal-world timeline: 2-6 weeks for actual transfer.\n\nStarting orchestration...\n```\n\n**During**: Update progress with clear indicators\n\n```\n✓ = Complete\n⏳ = In progress\n⚠️ = Attention needed\n❌ = Failed\u002Fblocked\n```\n\n**At end**: Summary report with status and next steps\n\n```\n─────────────────────────────────────────────\nKnowledge Transfer Complete\n─────────────────────────────────────────────\n\n**Transfer**: {from-member} → {to-member}\n**Domain**: {domain}\n**Status**: COMPLETE\n**Readiness**: READY FOR INDEPENDENT OPERATION\n\n**Summary**:\n✓ Knowledge gaps identified and addressed\n✓ Documentation: {X} docs created\u002Fupdated\n✓ Shadowing: {Y} sessions completed\n✓ Validation: {Z}\u002F4 scenarios passed\n✓ Handover: All parties signed off\n\n**Confidence Assessment**:\n- Receiver confidence: 4\u002F5\n- Holder confidence: 4\u002F5\n- Manager approval: APPROVED\n\n**Follow-Up Plan**:\n- 1-week check-in: {date}\n- 1-month review: {date}\n- Support period: {from-member} available for {duration}\n\n**Artifacts Generated**:\n- Knowledge Map: .aiwg\u002Fknowledge\u002Fknowledge-map-{domain}.md\n- Transfer Plan: .aiwg\u002Fknowledge\u002Ftransfer-plan-{from}-to-{to}.md\n- Documentation: .aiwg\u002Fknowledge\u002Fdocs\u002F\n- Validation Results: .aiwg\u002Fknowledge\u002Fvalidation\u002F\n- Handover Checklist: .aiwg\u002Fknowledge\u002Fhandover-checklist-{domain}.md\n- Final Report: .aiwg\u002Freports\u002Fknowledge-transfer-report-{domain}.md\n\n**Next Steps**:\n- Update team roster and responsibilities\n- Schedule follow-up check-ins\n- Monitor initial independent operation\n- Address any residual gaps per remediation plan\n\n─────────────────────────────────────────────\n```\n\n## Error Handling\n\n**Team Member Not Found**:\n```\n⚠️ Team member not found in roster\nProceeding with provided names: {from-member} → {to-member}\n\nNote: Consider updating .aiwg\u002Fteam\u002Fteam-profile.yaml\n```\n\n**Knowledge Domain Unclear**:\n```\n⚠️ Knowledge domain not specified\n\nDefaulting to: \"all responsibilities\"\nThis may extend timeline and scope.\n\nRecommendation: Specify domain for focused transfer\nExample: \"backend-api\", \"deployment\", \"security\"\n```\n\n**Validation Failure**:\n```\n❌ Validation scenario failed: {scenario}\n\nResult: {failure-reason}\nImpact: Receiver not ready for independent operation\n\nRecommendations:\n1. Additional practice in {area}\n2. Review relevant documentation\n3. Schedule extra reverse shadowing session\n4. Re-attempt validation after practice\n```\n\n**Insufficient Confidence**:\n```\n⚠️ Low confidence detected\n\nReceiver confidence: {X}\u002F5 (target: ≥3)\nHolder confidence: {Y}\u002F5 (target: ≥3)\n\nActions:\n1. Identify specific concern areas\n2. Provide additional shadowing\u002Fpractice\n3. Consider extended support period\n4. Document contingency plans\n```\n\n**Timeline Overrun**:\n```\n⚠️ Transfer taking longer than planned\n\nOriginal estimate: {X} weeks\nCurrent duration: {Y} weeks\n\nFactors:\n- Complexity underestimated\n- Availability constraints\n- Additional gaps discovered\n\nRecommendation: Adjust timeline and expectations\n```\n\n## Success Criteria\n\nThis orchestration succeeds when:\n- [ ] Knowledge gaps identified and documented\n- [ ] Transfer scope agreed by all parties\n- [ ] Documentation complete and accessible\n- [ ] Minimum 4 shadowing sessions completed\n- [ ] Minimum 4 reverse shadowing sessions completed\n- [ ] ≥75% validation scenarios passed\n- [ ] Receiver confidence ≥3\u002F5\n- [ ] Holder confidence ≥3\u002F5\n- [ ] Handover checklist signed off\n- [ ] Follow-up plan established\n\n## Metrics to Track\n\n**During orchestration, track**:\n- Documentation coverage: % of knowledge areas documented\n- Shadowing completion: # sessions completed vs planned\n- Confidence progression: Rating trend over time\n- Validation pass rate: % scenarios passed first attempt\n- Time to competency: Weeks from start to signoff\n\n## References\n\n**Templates** (via $AIWG_ROOT):\n- Knowledge Map: `templates\u002Fknowledge\u002Fknowledge-map-template.md`\n- Transfer Plan: `templates\u002Fknowledge\u002Ftransfer-plan-template.md`\n- Shadowing Log: `templates\u002Fknowledge\u002Fshadowing-log-template.md`\n- Validation Checklist: `templates\u002Fknowledge\u002Fknowledge-validation-checklist.md`\n- Handover Checklist: `templates\u002Fknowledge\u002Fhandover-checklist-template.md`\n\n**Related Commands**:\n- `team-roster` - Update team responsibilities\n- `update-oncall` - Modify on-call schedules\n- `flow-onboarding` - Full team member onboarding\n\n**Best Practices**:\n- `docs\u002Fknowledge-transfer-best-practices.md`\n- `docs\u002Fshadowing-techniques.md`\n- `docs\u002Fvalidation-scenario-design.md`","> **Declarative Flow (#1539):** This flow's orchestration is now also expressed as a\n> declarative YAML Flow at `flows\u002Fflow-knowledge-transfer.playbook.yaml` (source of truth for the step\n> sequence + gates). This SKILL.md remains the discoverable trigger surface and prose\n> reference. See epic #1534.\n\n\u003C!-- AIWG-SKILL-CALLOUT -->\n> **Skill access pattern (post-kernel-pivot, 2026.5+)**\n>\n> Skill names referenced in this document are AIWG skills, **not slash commands**. Most are not kernel-listed and cannot be invoked as `\u002Fskill-name` by the platform. Reach them via:\n>\n> ```bash\n> aiwg discover \"\u003Ccapability>\"\n> aiwg show skill \u003Cname> --body\n> ```\n>\n> Only kernel-listed skills (`aiwg-doctor`, `aiwg-refresh`, `aiwg-status`, `aiwg-help`, `use`, `steward`) are directly invokable as slash commands. See [skill-discovery rule](https:\u002F\u002Fgithub.com\u002Fjmagly\u002Faiwg\u002Fblob\u002Fmain\u002Fagentic\u002Fcode\u002Faddons\u002Faiwg-utils\u002Frules\u002Fskill-discovery.md).\n\n\n# Knowledge Transfer Orchestration Flow\n\n**You are the Core Orchestrator** for structured knowledge transfer between team members.\n\n## Your Role\n\n**You orchestrate multi-agent workflows. You do NOT execute bash scripts.**\n\nWhen the user requests this flow (via natural language or explicit command):\n\n1. **Interpret the request** and confirm understanding\n2. **Read this template** as your orchestration guide\n3. **Extract agent assignments** and workflow steps\n4. **Delegate agents with the current provider-native orchestration mechanism** in the correct sequence\n5. **Synthesize results** and finalize artifacts\n6. **Report completion** with summary\n\n## Knowledge Transfer Overview\n\n**Purpose**: Ensure continuity when team members transition roles, leave projects, or hand off domain expertise\n\n**Key Milestone**: Knowledge Transfer Signoff\n\n**Success Criteria**:\n- Knowledge gaps identified and addressed\n- Documentation complete and reviewed\n- Shadowing and reverse shadowing completed\n- Practical validation passed\n- Handover checklist signed off\n\n**Expected Duration**: 2-6 weeks (typical), 30-45 minutes orchestration\n\n## Natural Language Triggers\n\nUsers may say:\n- \"Knowledge transfer from Alice to Bob\"\n- \"Handoff backend responsibilities to new team member\"\n- \"Transfer knowledge from {from} to {to}\"\n- \"Documentation handoff for {domain}\"\n- \"Onboard new team member to {area}\"\n\nYou recognize these as requests for this orchestration flow.\n\n## Parameter Handling\n\n### Required Parameters\n\n- **from-member**: The team member transferring knowledge (knowledge holder)\n- **to-member**: The team member receiving knowledge (knowledge receiver)\n- **domain** (optional): Specific knowledge domain (e.g., \"backend-api\", \"deployment\", \"security\")\n\n### --guidance Parameter\n\n**Purpose**: User provides upfront direction to tailor transfer priorities\n\n**Examples**:\n```\n--guidance \"Focus on production support and incident response procedures\"\n--guidance \"Tight timeline, prioritize critical operational knowledge\"\n--guidance \"Receiver has strong technical background but no domain experience\"\n--guidance \"Include compliance and regulatory knowledge for audit requirements\"\n```\n\n**How to Apply**:\n- Parse guidance for keywords: operations, compliance, security, timeline, experience level\n- Adjust focus areas (operational vs. architectural knowledge)\n- Modify shadowing depth (minimal vs. comprehensive based on timeline)\n- Influence validation scenarios (focus on critical vs. comprehensive testing)\n\n### --interactive Parameter\n\n**Purpose**: You ask 6 strategic questions to understand transfer context\n\n**Questions to Ask** (if --interactive):\n\n```\nI'll ask 6 strategic questions to tailor the knowledge transfer to your needs:\n\nQ1: What are your top priorities for this knowledge transfer?\n    (e.g., operational continuity, architectural understanding, troubleshooting skills)\n\nQ2: What are your biggest constraints?\n    (e.g., timeline, availability of knowledge holder, complexity of domain)\n\nQ3: What risks concern you most for this transfer?\n    (e.g., critical knowledge loss, insufficient practice time, documentation gaps)\n\nQ4: What's the receiver's experience level with similar domains?\n    (Helps calibrate transfer depth and pace)\n\nQ5: What's your target timeline for independent operation?\n    (Influences shadowing duration and validation rigor)\n\nQ6: Are there compliance or regulatory requirements?\n    (e.g., SOX separation of duties, HIPAA training requirements)\n\nBased on your answers, I'll adjust:\n- Focus areas (operational vs. architectural vs. compliance)\n- Shadowing duration (standard vs. extended)\n- Validation rigor (basic vs. comprehensive)\n- Documentation depth (reference vs. tutorial)\n```\n\n**Synthesize Guidance**: Combine answers into structured guidance string for execution\n\n## Artifacts to Generate\n\n**Primary Deliverables**:\n- **Knowledge Map**: Domain expertise assessment → `.aiwg\u002Fknowledge\u002Fknowledge-map-{domain}.md`\n- **Transfer Plan**: Structured handoff schedule → `.aiwg\u002Fknowledge\u002Ftransfer-plan-{from}-to-{to}.md`\n- **Documentation Package**: Updated\u002Fcreated docs → `.aiwg\u002Fknowledge\u002Fdocs\u002F`\n- **Shadowing Logs**: Observation records → `.aiwg\u002Fknowledge\u002Fshadowing\u002F`\n- **Validation Results**: Test scenarios and outcomes → `.aiwg\u002Fknowledge\u002Fvalidation\u002F`\n- **Handover Checklist**: Final signoff document → `.aiwg\u002Fknowledge\u002Fhandover-checklist-{domain}.md`\n- **Transfer Report**: Completion summary → `.aiwg\u002Freports\u002Fknowledge-transfer-report-{domain}.md`\n\n**Supporting Artifacts**:\n- Knowledge gap analysis\n- Runbook updates\n- Training materials\n- Follow-up plans\n\n## Multi-Agent Orchestration Workflow\n\n### Step 1: Knowledge Assessment and Transfer Scope\n\n**Purpose**: Identify knowledge domain(s) and define transfer scope\n\n**Your Actions**:\n\n1. **Validate Team Members Exist**:\n   ```\n   Read .aiwg\u002Fteam\u002Fteam-profile.yaml (if exists)\n   Verify from-member and to-member are valid team members\n   If not found, proceed with provided names but note in report\n   ```\n\n2. **Launch Knowledge Assessment Agents** (parallel):\n   ```\n   # Agent 1: Knowledge Manager (lead)\n   Task(\n       subagent_type=\"knowledge-manager\",\n       description=\"Assess knowledge domain and create transfer scope\",\n       prompt=\"\"\"\n       Create knowledge assessment for transfer:\n       - From: {from-member}\n       - To: {to-member}\n       - Domain: {domain if specified, else \"all responsibilities\"}\n\n       Define Knowledge Map:\n       1. Knowledge Areas (list all relevant areas)\n       2. Criticality Assessment (Critical, High, Medium, Low)\n       3. Current State Assessment:\n          - Holder expertise level (Expert, Advanced, Intermediate)\n          - Receiver current level (None, Novice, Beginner, Intermediate)\n       4. Knowledge Gaps (delta between holder and receiver)\n       5. Transfer Priority (HIGH, MEDIUM, LOW for each area)\n\n       Define Transfer Scope:\n       - In Scope: Areas requiring active transfer\n       - Out of Scope: Already documented or low priority\n       - Success Criteria: What defines successful transfer\n\n       Estimate Timeline:\n       - Based on scope and gaps\n       - Typical: 2-6 weeks\n\n       Use template if available: $AIWG_ROOT\u002Ftemplates\u002Fknowledge\u002Fknowledge-map-template.md\n\n       Output: .aiwg\u002Fknowledge\u002Fknowledge-map-{domain}.md\n       \"\"\"\n   )\n\n   # Agent 2: Training Coordinator\n   Task(\n       subagent_type=\"training-coordinator\",\n       description=\"Create structured transfer plan\",\n       prompt=\"\"\"\n       Based on knowledge assessment, create transfer plan:\n\n       Structure:\n       1. Documentation Phase (Week 1)\n          - Review existing docs\n          - Identify and fill gaps\n          - Create runbooks\n\n       2. Shadowing Phase (Week 2-3)\n          - 4-8 observation sessions\n          - Knowledge holder leads, receiver observes\n          - Q&A and note-taking\n\n       3. Reverse Shadowing (Week 3-4)\n          - 4-8 practice sessions\n          - Receiver leads, holder observes\n          - Feedback and correction\n\n       4. Validation Phase (Week 4-5)\n          - Practical scenarios\n          - Independent operation test\n          - Knowledge verification\n\n       5. Handover Phase (Week 5-6)\n          - Final checklist\n          - Signoffs\n          - Follow-up plan\n\n       Adjust timeline based on:\n       - Scope complexity\n       - Availability constraints\n       - {guidance if provided}\n\n       Use template if available: $AIWG_ROOT\u002Ftemplates\u002Fknowledge\u002Ftransfer-plan-template.md\n\n       Output: .aiwg\u002Fknowledge\u002Ftransfer-plan-{from}-to-{to}.md\n       \"\"\"\n   )\n   ```\n\n3. **Review and Confirm Scope**:\n   ```\n   Task(\n       subagent_type=\"project-manager\",\n       description=\"Review and validate transfer scope\",\n       prompt=\"\"\"\n       Read:\n       - .aiwg\u002Fknowledge\u002Fknowledge-map-{domain}.md\n       - .aiwg\u002Fknowledge\u002Ftransfer-plan-{from}-to-{to}.md\n\n       Validate:\n       - Scope is realistic for timeline\n       - Critical knowledge areas covered\n       - Success criteria are measurable\n       - Plan accounts for constraints\n\n       Create gate decision:\n       - GO: Proceed with transfer\n       - ADJUST: Modify scope or timeline\n       - ESCALATE: Needs management decision\n\n       Output validation summary to transfer plan\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n✓ Knowledge assessment complete\n✓ Transfer scope defined: {X} knowledge areas, {Y} weeks estimated\n✓ Transfer plan created: .aiwg\u002Fknowledge\u002Ftransfer-plan-{from}-to-{to}.md\n```\n\n### Step 2: Documentation Review and Knowledge Artifacts\n\n**Purpose**: Compile and enhance documentation for knowledge transfer\n\n**Your Actions**:\n\n1. **Inventory Existing Documentation**:\n   ```\n   # Use Glob to find relevant docs\n   Glob(\"**\u002F*.md\")\n   Glob(\"**\u002F*.txt\")\n\n   Filter for domain-relevant documentation\n   Create inventory list\n   ```\n\n2. **Launch Documentation Agents** (parallel):\n   ```\n   # Agent 1: Documentation Archivist\n   Task(\n       subagent_type=\"documentation-archivist\",\n       description=\"Organize and review existing documentation\",\n       prompt=\"\"\"\n       Domain: {domain}\n\n       Review existing documentation:\n       1. Architecture documents\n       2. Runbooks and procedures\n       3. Configuration guides\n       4. Troubleshooting guides\n       5. Historical incident reports\n\n       Assess each document:\n       - Currency (up-to-date?)\n       - Completeness (gaps?)\n       - Clarity (understandable?)\n       - Relevance (needed for transfer?)\n\n       Create Documentation Inventory:\n       - Core Docs (must review)\n       - Reference Docs (good to know)\n       - Archive Docs (historical context)\n       - Missing Docs (gaps to fill)\n\n       Organize in logical learning sequence\n\n       Output: .aiwg\u002Fknowledge\u002Fdocs\u002Fdocumentation-inventory.md\n       \"\"\"\n   )\n\n   # Agent 2: Subject Matter Expert (knowledge holder role)\n   Task(\n       subagent_type=\"subject-matter-expert\",\n       description=\"Identify and create missing documentation\",\n       prompt=\"\"\"\n       Acting as {from-member} (knowledge holder perspective)\n\n       Based on documentation inventory, create missing critical docs:\n\n       1. Runbooks for common operations:\n          - Daily\u002Fweekly tasks\n          - Deployment procedures\n          - Rollback procedures\n          - Monitoring and alerting\n\n       2. Troubleshooting guides:\n          - Common issues and solutions\n          - Debugging techniques\n          - Log analysis patterns\n          - Performance tuning\n\n       3. Architecture notes:\n          - Design decisions and rationale\n          - System boundaries and interfaces\n          - Data flows and dependencies\n          - Security considerations\n\n       4. Tribal knowledge:\n          - Undocumented gotchas\n          - Historical context (\"why it's this way\")\n          - Stakeholder relationships\n          - Political\u002Forganizational context\n\n       Focus on practical, hands-on knowledge needed for independent operation\n\n       Output to: .aiwg\u002Fknowledge\u002Fdocs\u002F{category}\u002F\n       \"\"\"\n   )\n\n   # Agent 3: Technical Writer\n   Task(\n       subagent_type=\"technical-writer\",\n       description=\"Enhance documentation clarity and completeness\",\n       prompt=\"\"\"\n       Review and enhance documentation for knowledge transfer:\n\n       Improvements:\n       1. Add missing context for newcomers\n       2. Clarify technical jargon\n       3. Add examples and scenarios\n       4. Create quick reference guides\n       5. Add diagrams where helpful\n\n       Ensure documentation is:\n       - Self-contained (minimal external references)\n       - Progressive (basic → advanced)\n       - Actionable (clear steps)\n       - Verifiable (testable outcomes)\n\n       Create consolidated reading list in order\n\n       Output enhanced docs to: .aiwg\u002Fknowledge\u002Fdocs\u002Fenhanced\u002F\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Documentation review in progress...\n  ✓ {X} existing documents inventoried\n  ✓ {Y} documentation gaps identified\n  ✓ {Z} new documents created\n✓ Documentation package complete: .aiwg\u002Fknowledge\u002Fdocs\u002F\n```\n\n### Step 3: Shadowing Phase (Receiver Observes)\n\n**Purpose**: Knowledge receiver observes holder performing actual work\n\n**Your Actions**:\n\n1. **Initialize Shadowing Sessions**:\n   ```\n   # Create session structure\n   mkdir -p .aiwg\u002Fknowledge\u002Fshadowing\u002Fsessions\n\n   # Define 4-8 sessions based on knowledge areas\n   For each critical knowledge area, allocate 1-2 sessions\n   ```\n\n2. **Launch Shadowing Simulation** (for each session):\n   ```\n   # For each shadowing session (4-8 total)\n   Task(\n       subagent_type=\"training-coordinator\",\n       description=\"Simulate shadowing session {N}\",\n       prompt=\"\"\"\n       Shadowing Session {N}\n       Knowledge Area: {area from knowledge map}\n       Duration: 1-2 hours (simulated)\n\n       Simulate session where {from-member} demonstrates:\n       1. Task execution (step-by-step)\n       2. Decision points (what and why)\n       3. Tool usage (specific commands\u002Finterfaces)\n       4. Common issues (what to watch for)\n       5. Best practices (efficiency tips)\n\n       {to-member} perspective:\n       - Observations noted\n       - Questions asked\n       - Concepts clarified\n       - Confidence assessment (1-5)\n\n       Create session log including:\n       - Tasks demonstrated\n       - Key decisions explained\n       - Questions and answers\n       - Key learnings captured\n       - Follow-up items identified\n       - Confidence rating\n\n       Output: .aiwg\u002Fknowledge\u002Fshadowing\u002Fsessions\u002Fsession-{N}-{area}.md\n       \"\"\"\n   )\n   ```\n\n3. **Synthesize Shadowing Learnings**:\n   ```\n   Task(\n       subagent_type=\"knowledge-manager\",\n       description=\"Synthesize shadowing phase learnings\",\n       prompt=\"\"\"\n       Read all shadowing session logs\n\n       Create synthesis:\n       1. Knowledge areas covered\n       2. Key learnings consolidated\n       3. Remaining questions\n       4. Confidence progression (trend over sessions)\n       5. Areas needing more practice\n\n       Identify patterns:\n       - Concepts requiring repetition\n       - Complex areas needing breakdown\n       - Tools requiring hands-on practice\n\n       Recommend focus for reverse shadowing\n\n       Output: .aiwg\u002Fknowledge\u002Fshadowing\u002Fshadowing-synthesis.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Shadowing phase in progress...\n  ✓ Session 1: Database operations (confidence: 3\u002F5)\n  ✓ Session 2: Deployment procedures (confidence: 2\u002F5)\n  ✓ Session 3: Incident response (confidence: 4\u002F5)\n  ✓ Session 4: Performance tuning (confidence: 2\u002F5)\n✓ Shadowing complete: {X} sessions, average confidence: {Y}\u002F5\n```\n\n### Step 4: Reverse Shadowing Phase (Receiver Leads)\n\n**Purpose**: Knowledge receiver performs tasks with holder observing\n\n**Your Actions**:\n\n1. **Plan Reverse Shadowing Sessions**:\n   ```\n   Based on shadowing synthesis, prioritize:\n   - Low confidence areas (2\u002F5 or below)\n   - Critical operations\n   - Complex procedures\n   ```\n\n2. **Launch Reverse Shadowing** (for each session):\n   ```\n   # For each reverse shadowing session (4-8 total)\n   Task(\n       subagent_type=\"learner\",\n       description=\"Simulate reverse shadowing session {N}\",\n       prompt=\"\"\"\n       Reverse Shadowing Session {N}\n       Knowledge Area: {area}\n       Receiver Leading: {to-member}\n       Holder Observing: {from-member}\n\n       Simulate {to-member} performing tasks:\n       1. Task approach (how they tackle it)\n       2. Decision making (choices and reasoning)\n       3. Challenges faced (what's difficult)\n       4. Holder interventions (when and why)\n       5. Corrections made (learning moments)\n\n       Holder feedback:\n       - What went well\n       - Areas for improvement\n       - Specific corrections\n       - Confidence assessment\n\n       Success indicators:\n       - Task completed correctly\n       - Minimal interventions needed\n       - Sound reasoning demonstrated\n\n       Create session log:\n       - Tasks performed\n       - Interventions required\n       - Feedback provided\n       - Outcome (SUCCESS, PARTIAL, NEEDS_PRACTICE)\n       - Confidence growth\n\n       Output: .aiwg\u002Fknowledge\u002Fshadowing\u002Freverse\u002Fsession-{N}-{area}.md\n       \"\"\"\n   )\n   ```\n\n3. **Assess Progress and Readiness**:\n   ```\n   Task(\n       subagent_type=\"training-coordinator\",\n       description=\"Assess reverse shadowing progress\",\n       prompt=\"\"\"\n       Read all reverse shadowing sessions\n\n       Assess readiness:\n       1. Tasks completed successfully (%)\n       2. Intervention frequency (trending down?)\n       3. Confidence ratings (trending up?)\n       4. Decision quality (sound reasoning?)\n\n       For each knowledge area:\n       - Status: READY | NEEDS_PRACTICE | NOT_READY\n       - Remaining gaps\n       - Recommended actions\n\n       Overall assessment:\n       - Ready for validation: YES\u002FNO\n       - Areas needing more practice\n       - Estimated additional time needed\n\n       Output: .aiwg\u002Fknowledge\u002Fshadowing\u002Freverse\u002Freadiness-assessment.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Reverse shadowing in progress...\n  ✓ Session 1: Database operations (SUCCESS, minimal intervention)\n  ✓ Session 2: Deployment procedures (PARTIAL, 2 interventions)\n  ✓ Session 3: Incident response (SUCCESS, no intervention)\n  ⚠️ Session 4: Performance tuning (NEEDS_PRACTICE, multiple interventions)\n✓ Reverse shadowing complete: 75% success rate\n```\n\n### Step 5: Knowledge Validation and Practical Testing\n\n**Purpose**: Validate knowledge acquisition through realistic scenarios\n\n**Your Actions**:\n\n1. **Create Validation Scenarios**:\n   ```\n   Task(\n       subagent_type=\"test-architect\",\n       description=\"Design validation scenarios\",\n       prompt=\"\"\"\n       Based on knowledge domain {domain}, create 4 validation scenarios:\n\n       Scenario 1: Routine Operation\n       - Common daily\u002Fweekly task\n       - Expected to complete independently\n       - Time limit: reasonable for task\n\n       Scenario 2: Troubleshooting\n       - Realistic problem to diagnose and fix\n       - Tests analytical skills\n       - Multiple solution paths acceptable\n\n       Scenario 3: Teach-Back\n       - Explain concept to simulated junior member\n       - Tests depth of understanding\n       - Must be accurate and clear\n\n       Scenario 4: Novel Situation\n       - New problem not explicitly covered\n       - Tests knowledge application\n       - Reasonable extrapolation expected\n\n       Each scenario includes:\n       - Context and setup\n       - Success criteria\n       - Evaluation rubric\n       - Time expectations\n\n       Output: .aiwg\u002Fknowledge\u002Fvalidation\u002Fvalidation-scenarios.md\n       \"\"\"\n   )\n   ```\n\n2. **Execute Validation Tests** (parallel where possible):\n   ```\n   # For each validation scenario\n   Task(\n       subagent_type=\"learner\",\n       description=\"Execute validation scenario {N}\",\n       prompt=\"\"\"\n       As {to-member}, complete validation scenario {N}\n\n       Demonstrate:\n       1. Understanding of the problem\n       2. Systematic approach\n       3. Correct solution or diagnosis\n       4. Appropriate tool usage\n       5. Documentation of actions\n\n       For teach-back scenario:\n       - Explain clearly\n       - Use examples\n       - Check understanding\n\n       For novel situation:\n       - Show problem-solving process\n       - Use available resources\n       - Apply learned principles\n\n       Document:\n       - Approach taken\n       - Solution provided\n       - Time taken\n       - Confidence level\n       - Resources consulted\n\n       Output: .aiwg\u002Fknowledge\u002Fvalidation\u002Fscenario-{N}-results.md\n       \"\"\"\n   )\n\n   # Parallel evaluation by holder\n   Task(\n       subagent_type=\"subject-matter-expert\",\n       description=\"Evaluate validation scenarios\",\n       prompt=\"\"\"\n       As {from-member}, evaluate {to-member}'s performance\n\n       For each scenario:\n       - Accuracy (correct solution?)\n       - Approach (systematic and logical?)\n       - Efficiency (reasonable time?)\n       - Independence (minimal help needed?)\n       - Documentation (clear and complete?)\n\n       Rating scale:\n       - EXCELLENT: Exceeds expectations\n       - PASS: Meets requirements\n       - CONDITIONAL: Mostly correct, minor gaps\n       - FAIL: Significant gaps, more practice needed\n\n       Provide specific feedback:\n       - What was done well\n       - Areas for improvement\n       - Recommendations\n\n       Overall readiness assessment:\n       - READY for independent operation\n       - READY with support period\n       - NOT READY, need more practice\n\n       Output: .aiwg\u002Fknowledge\u002Fvalidation\u002Fevaluation-results.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n⏳ Validation testing in progress...\n  ✓ Scenario 1 (Routine): PASS\n  ✓ Scenario 2 (Troubleshooting): PASS\n  ✓ Scenario 3 (Teach-Back): EXCELLENT\n  ⚠️ Scenario 4 (Novel): CONDITIONAL (minor gaps noted)\n✓ Validation complete: 3\u002F4 PASS or better\n```\n\n### Step 6: Handover Checklist and Signoff\n\n**Purpose**: Complete formal handover with all parties signing off\n\n**Your Actions**:\n\n1. **Generate Handover Checklist**:\n   ```\n   Task(\n       subagent_type=\"project-manager\",\n       description=\"Create comprehensive handover checklist\",\n       prompt=\"\"\"\n       Create handover checklist for:\n       - Domain: {domain}\n       - From: {from-member}\n       - To: {to-member}\n       - Duration: {weeks from start to now}\n\n       Checklist sections:\n\n       1. Documentation\n          - All docs reviewed: YES\u002FNO\n          - Gaps addressed: YES\u002FNO\n          - Bookmarks\u002Faccess: YES\u002FNO\n\n       2. Practical Skills\n          - Routine tasks: {validation results}\n          - Troubleshooting: {validation results}\n          - Emergency procedures: UNDERSTOOD\u002FPRACTICED\n\n       3. Knowledge Validation\n          - Scenarios passed: {X}\u002F4\n          - Teach-back successful: YES\u002FNO\n          - Holder confidence: {rating}\n\n       4. Access and Permissions\n          - System access: GRANTED\u002FPENDING\n          - Tool access: GRANTED\u002FPENDING\n          - Communication channels: ADDED\u002FPENDING\n\n       5. Operational Handoff\n          - On-call rotation: UPDATED\u002FPENDING\n          - Responsibility matrix: UPDATED\u002FPENDING\n          - Stakeholder notification: SENT\u002FPENDING\n\n       6. Follow-Up Plan\n          - 1-week check-in: {date}\n          - 1-month check-in: {date}\n          - Support period: {duration}\n\n       7. Residual Gaps (if any)\n          - List with severity and remediation plan\n\n       Use template if available: $AIWG_ROOT\u002Ftemplates\u002Fknowledge\u002Fhandover-checklist-template.md\n\n       Output: .aiwg\u002Fknowledge\u002Fhandover-checklist-{domain}.md\n       \"\"\"\n   )\n   ```\n\n2. **Collect Signoffs**:\n   ```\n   Task(\n       subagent_type=\"project-manager\",\n       description=\"Collect handover signoffs\",\n       prompt=\"\"\"\n       Document signoffs for handover:\n\n       Required signatures:\n       1. Knowledge Receiver ({to-member}):\n          \"I am confident in my ability to perform {domain} responsibilities independently\"\n          Confidence level: {1-5}\n          Concerns (if any): {list}\n\n       2. Knowledge Holder ({from-member}):\n          \"I am confident the receiver has the knowledge to succeed independently\"\n          Confidence level: {1-5}\n          Recommendations: {list}\n\n       3. Project Manager:\n          \"Knowledge transfer is complete and receiver is ready for independent operation\"\n          Decision: APPROVED \u002F CONDITIONAL \u002F NOT_APPROVED\n\n       Conditional requirements (if CONDITIONAL):\n       - What must be completed\n       - Timeline for completion\n       - Re-validation plan\n\n       Add signatures to handover checklist\n       \"\"\"\n   )\n   ```\n\n3. **Generate Final Report**:\n   ```\n   Task(\n       subagent_type=\"knowledge-manager\",\n       description=\"Generate knowledge transfer completion report\",\n       prompt=\"\"\"\n       Create comprehensive transfer report including:\n\n       1. Executive Summary\n          - Transfer status: COMPLETE\u002FPARTIAL\u002FINCOMPLETE\n          - Readiness: READY\u002FCONDITIONAL\u002FNOT_READY\n          - Key outcomes\n\n       2. Transfer Summary\n          - Scope (knowledge areas covered)\n          - Timeline (planned vs actual)\n          - Methods (shadowing, documentation, validation)\n\n       3. Knowledge Acquisition Metrics\n          - Shadowing sessions: {count}\n          - Reverse shadowing: {count}\n          - Validation scenarios: {passed}\u002F{total}\n          - Confidence progression: {start} → {end}\n\n       4. Documentation Improvements\n          - Docs created: {count}\n          - Docs enhanced: {count}\n          - Remaining gaps: {list}\n\n       5. Validation Results\n          - Detailed scenario outcomes\n          - Evaluator feedback\n          - Areas of strength\n          - Areas for improvement\n\n       6. Lessons Learned\n          - What worked well\n          - What could improve\n          - Recommendations for future transfers\n\n       7. Follow-Up Plan\n          - Check-in schedule\n          - Support arrangements\n          - Escalation path\n\n       8. Risk Assessment\n          - Operational risks\n          - Mitigation strategies\n          - Contingency plans\n\n       Output: .aiwg\u002Freports\u002Fknowledge-transfer-report-{domain}.md\n       \"\"\"\n   )\n   ```\n\n**Communicate Progress**:\n```\n✓ Handover checklist complete: .aiwg\u002Fknowledge\u002Fhandover-checklist-{domain}.md\n✓ All parties signed off\n✓ Transfer report generated: .aiwg\u002Freports\u002Fknowledge-transfer-report-{domain}.md\n```\n\n## Quality Gates\n\nBefore marking workflow complete, verify:\n- [ ] Knowledge assessment documented\n- [ ] Transfer plan created and followed\n- [ ] Documentation gaps addressed\n- [ ] Shadowing sessions completed (minimum 4)\n- [ ] Reverse shadowing completed (minimum 4)\n- [ ] Validation scenarios passed (≥75%)\n- [ ] Handover checklist complete\n- [ ] All required signoffs obtained\n- [ ] Follow-up plan established\n\n## User Communication\n\n**At start**: Confirm understanding and outline process\n\n```\nUnderstood. I'll orchestrate the knowledge transfer from {from-member} to {to-member} for {domain}.\n\nThis will include:\n- Knowledge assessment and gap analysis\n- Documentation review and enhancement\n- Shadowing sessions (observation)\n- Reverse shadowing (practice)\n- Validation testing\n- Formal handover and signoff\n\nExpected duration: 30-45 minutes orchestration.\nReal-world timeline: 2-6 weeks for actual transfer.\n\nStarting orchestration...\n```\n\n**During**: Update progress with clear indicators\n\n```\n✓ = Complete\n⏳ = In progress\n⚠️ = Attention needed\n❌ = Failed\u002Fblocked\n```\n\n**At end**: Summary report with status and next steps\n\n```\n─────────────────────────────────────────────\nKnowledge Transfer Complete\n─────────────────────────────────────────────\n\n**Transfer**: {from-member} → {to-member}\n**Domain**: {domain}\n**Status**: COMPLETE\n**Readiness**: READY FOR INDEPENDENT OPERATION\n\n**Summary**:\n✓ Knowledge gaps identified and addressed\n✓ Documentation: {X} docs created\u002Fupdated\n✓ Shadowing: {Y} sessions completed\n✓ Validation: {Z}\u002F4 scenarios passed\n✓ Handover: All parties signed off\n\n**Confidence Assessment**:\n- Receiver confidence: 4\u002F5\n- Holder confidence: 4\u002F5\n- Manager approval: APPROVED\n\n**Follow-Up Plan**:\n- 1-week check-in: {date}\n- 1-month review: {date}\n- Support period: {from-member} available for {duration}\n\n**Artifacts Generated**:\n- Knowledge Map: .aiwg\u002Fknowledge\u002Fknowledge-map-{domain}.md\n- Transfer Plan: .aiwg\u002Fknowledge\u002Ftransfer-plan-{from}-to-{to}.md\n- Documentation: .aiwg\u002Fknowledge\u002Fdocs\u002F\n- Validation Results: .aiwg\u002Fknowledge\u002Fvalidation\u002F\n- Handover Checklist: .aiwg\u002Fknowledge\u002Fhandover-checklist-{domain}.md\n- Final Report: .aiwg\u002Freports\u002Fknowledge-transfer-report-{domain}.md\n\n**Next Steps**:\n- Update team roster and responsibilities\n- Schedule follow-up check-ins\n- Monitor initial independent operation\n- Address any residual gaps per remediation plan\n\n─────────────────────────────────────────────\n```\n\n## Error Handling\n\n**Team Member Not Found**:\n```\n⚠️ Team member not found in roster\nProceeding with provided names: {from-member} → {to-member}\n\nNote: Consider updating .aiwg\u002Fteam\u002Fteam-profile.yaml\n```\n\n**Knowledge Domain Unclear**:\n```\n⚠️ Knowledge domain not specified\n\nDefaulting to: \"all responsibilities\"\nThis may extend timeline and scope.\n\nRecommendation: Specify domain for focused transfer\nExample: \"backend-api\", \"deployment\", \"security\"\n```\n\n**Validation Failure**:\n```\n❌ Validation scenario failed: {scenario}\n\nResult: {failure-reason}\nImpact: Receiver not ready for independent operation\n\nRecommendations:\n1. Additional practice in {area}\n2. Review relevant documentation\n3. Schedule extra reverse shadowing session\n4. Re-attempt validation after practice\n```\n\n**Insufficient Confidence**:\n```\n⚠️ Low confidence detected\n\nReceiver confidence: {X}\u002F5 (target: ≥3)\nHolder confidence: {Y}\u002F5 (target: ≥3)\n\nActions:\n1. Identify specific concern areas\n2. Provide additional shadowing\u002Fpractice\n3. Consider extended support period\n4. Document contingency plans\n```\n\n**Timeline Overrun**:\n```\n⚠️ Transfer taking longer than planned\n\nOriginal estimate: {X} weeks\nCurrent duration: {Y} weeks\n\nFactors:\n- Complexity underestimated\n- Availability constraints\n- Additional gaps discovered\n\nRecommendation: Adjust timeline and expectations\n```\n\n## Success Criteria\n\nThis orchestration succeeds when:\n- [ ] Knowledge gaps identified and documented\n- [ ] Transfer scope agreed by all parties\n- [ ] Documentation complete and accessible\n- [ ] Minimum 4 shadowing sessions completed\n- [ ] Minimum 4 reverse shadowing sessions completed\n- [ ] ≥75% validation scenarios passed\n- [ ] Receiver confidence ≥3\u002F5\n- [ ] Holder confidence ≥3\u002F5\n- [ ] Handover checklist signed off\n- [ ] Follow-up plan established\n\n## Metrics to Track\n\n**During orchestration, track**:\n- Documentation coverage: % of knowledge areas documented\n- Shadowing completion: # sessions completed vs planned\n- Confidence progression: Rating trend over time\n- Validation pass rate: % scenarios passed first attempt\n- Time to competency: Weeks from start to signoff\n\n## References\n\n**Templates** (via $AIWG_ROOT):\n- Knowledge Map: `templates\u002Fknowledge\u002Fknowledge-map-template.md`\n- Transfer Plan: `templates\u002Fknowledge\u002Ftransfer-plan-template.md`\n- Shadowing Log: `templates\u002Fknowledge\u002Fshadowing-log-template.md`\n- Validation Checklist: `templates\u002Fknowledge\u002Fknowledge-validation-checklist.md`\n- Handover Checklist: `templates\u002Fknowledge\u002Fhandover-checklist-template.md`\n\n**Related Commands**:\n- `team-roster` - Update team responsibilities\n- `update-oncall` - Modify on-call schedules\n- `flow-onboarding` - Full team member onboarding\n\n**Best Practices**:\n- `docs\u002Fknowledge-transfer-best-practices.md`\n- `docs\u002Fshadowing-techniques.md`\n- 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