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feat: implement human/LLM dual-format databank architecture with Joplin integration\n\n- Restructure databank with collab/artifacts/human/llm top-level directories\n- Move CTO and COO directories under pmo/artifacts/ as requested\n- Create dual-format architecture for human-friendly markdown and LLM-optimized structured data\n- Add Joplin integration pipeline in databank/collab/fromjoplin/\n- Create intake system with templates, responses, and workflows\n- Add sample files demonstrating human/LLM format differences\n- Link to TSYSDevStack repository in main README\n- Update PMO structure to reflect CTO/COO under artifacts/\n- Add processing scripts and workflows for automated conversion\n- Maintain clear separation between editable collab/ and readonly databank/\n- Create comprehensive README documentation for new architecture\n- Ensure all changes align with single source of truth principle

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Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
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Charles N Wyble - admin
2025-10-24 12:15:36 -05:00
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# Collab Intake System
This directory contains the collaborative intake system for populating and updating the databank through structured interviews and workflows.
## Structure
```
intake/
├── templates/ # Interview templates and question sets
├── responses/ # Collected responses from interviews
├── workflows/ # Automated intake workflows and processes
└── README.md # This file
```
## Purpose
The intake system facilitates:
- Structured knowledge capture through guided interviews
- Regular updates to keep databank information current
- Multi-modal input collection (text, voice, structured data)
- Quality control and validation of incoming information
- Automated synchronization between human and LLM formats
## Process
1. **Templates** - Use predefined interview templates for specific domains
2. **Interviews** - Conduct structured interviews using templates
3. **Responses** - Collect and store raw responses
4. **Processing** - Convert responses into both human and LLM formats
5. **Validation** - Review and validate converted information
6. **Synchronization** - Update both human and LLM directories
7. **Tracking** - Maintain version history and change tracking
## Templates
Template files guide the intake process with:
- Domain-specific questions
- Response format guidelines
- Validation criteria
- Cross-reference requirements
- Update frequency recommendations
---

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# Sample Intake Response - Personal Information
This is a sample response to demonstrate the intake system structure.
```yaml
identity:
legal_name: "Charles N Wyble"
preferred_name: "Charles"
handles:
- platform: "GitHub"
handle: "@ReachableCEO"
- platform: "Twitter"
handle: "@ReachableCEO"
contact_preferences:
- method: "email"
preference: "high"
- method: "signal"
preference: "medium"
location:
current: "Central Texas, USA"
planned_moves:
- destination: "Raleigh, NC"
date: "April 2026"
birth_year: 1984
professional_background:
career_timeline:
- start: "2002"
role: "Production Technical Operations"
company: "Various"
- start: "2025"
role: "Solo Entrepreneur"
company: "TSYS Group"
core_competencies:
- "Technical Operations"
- "System Administration"
- "DevOps"
- "AI Integration"
industry_experience:
- "Technology"
- "Manufacturing"
- "Energy"
certifications: []
achievements: []
current_focus: "AI-assisted workflow optimization"
philosophical_positions:
core_values:
- "Digital Data Sovereignty"
- "Rule of Law"
- "Separation of Powers"
political_affiliations:
- party: "Democratic"
strength: "Strong"
ethical_frameworks:
- "Pragmatic"
- "Transparent"
approach_to_work: "Results-focused with emphasis on automation"
ai_integration_views: "Essential for modern knowledge work"
data_privacy_stances: "Strong advocate for personal data control"
technical_preferences:
preferred_tools:
- "Codex"
- "Qwen"
- "Gemini"
technology_stack:
- "Docker"
- "Cloudron"
- "Coolify (planned)"
ai_tool_patterns:
- "Codex for code generation"
- "Qwen for system orchestration"
- "Gemini for audits"
development_methods:
- "Agile"
- "CI/CD"
security_practices:
- "Self-hosting"
- "Regular backups"
automation_approaches:
- "Infrastructure as Code"
- "AI-assisted workflows"
lifestyle_context:
daily_schedule: "Early morning focused work, flexible afternoon"
communication_preferences: "Direct, no flattery"
collaboration_approach: "Relaxed but professional"
work_life_balance: "Integrated but boundary-aware"
ongoing_projects:
- "TSYS Group ecosystem"
- "AI Home Directory optimization"
future_plans:
- "Relocation to Raleigh NC"
- "Full AI workflow integration"
relationships_networks:
key_relationships:
- "Albert (COO transition)"
- "Mike (Future VP Marketing)"
organizational_affiliations:
- "TSYS Group"
community_involvement: []
mentorship_roles: []
collaboration_patterns:
- "Solo entrepreneur with AI collaboration"
```

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# AI Tools and Agent Preferences Intake Template
## Overview
This template guides the collection of AI tool preferences and agent interaction guidelines.
## Interview Structure
### 1. Current Tool Usage
- Primary tools and their roles
- Subscription status and limitations
- Usage patterns and workflows
- Strengths and limitations of each tool
- Quota management and availability strategies
- Backup and alternative tool selections
### 2. Agent Guidelines and Rules
- Core operating principles
- Communication protocols and expectations
- Documentation standards and formats
- Quality assurance and validation approaches
- Error handling and recovery procedures
- Security and privacy considerations
### 3. Workflow Preferences
- Preferred interaction styles
- Response length and detail expectations
- Formatting and presentation preferences
- Decision-making and approval processes
- Feedback and iteration approaches
- Collaboration and delegation patterns
### 4. Technical Environment
- Development environment preferences
- Tool integration and interoperability
- Version control and change management
- Testing and quality assurance practices
- Deployment and delivery mechanisms
- Monitoring and observability requirements
### 5. Performance Optimization
- Token efficiency strategies
- Context window management
- Response time expectations
- Resource utilization considerations
- Cost optimization approaches
- Scalability and reliability requirements
## Response Format
Please provide responses in the following structured format:
```yaml
tool_usage:
primary_tools:
- name: ""
role: ""
subscription_status: ""
usage_patterns: []
strengths: []
limitations: []
quota_management:
strategies: []
backup_selections: []
workflow_integration:
primary_flows: []
backup_flows: []
agent_guidelines:
core_principles: []
communication_protocols: []
documentation_standards: []
quality_assurance: []
error_handling: []
security_considerations: []
workflow_preferences:
interaction_styles: []
response_expectations:
length_preference: ""
detail_level: ""
formatting_preferences: []
decision_processes: []
feedback_approaches: []
collaboration_patterns: []
technical_environment:
development_preferences: []
tool_integration: []
version_control: []
testing_practices: []
deployment_mechanisms: []
monitoring_requirements: []
performance_optimization:
token_efficiency: []
context_management: []
response_time: []
resource_utilization: []
cost_optimization: []
scalability_requirements: []
```
## Validation Criteria
- Alignment with current tool subscriptions
- Consistency with documented workflows
- Practicality of implementation
- Completeness of coverage
- Clarity of expectations
## Frequency
This intake should be updated:
- Semi-annually for tool changes
- As-needed for workflow modifications
- Quarterly for performance optimization reviews

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# Operations and Project Management Intake Template
## Overview
This template guides the collection of operational procedures and project management approaches.
## Interview Structure
### 1. Operational Procedures
- Daily/weekly/monthly routines and rituals
- System administration and maintenance tasks
- Monitoring and alerting procedures
- Backup and recovery processes
- Security and compliance practices
- Documentation and knowledge management
### 2. Project Management Approaches
- Project initiation and planning methods
- Task tracking and progress monitoring
- Resource allocation and scheduling
- Risk management and contingency planning
- Communication and stakeholder management
- Quality assurance and delivery processes
### 3. Infrastructure and Tools
- Hosting platforms and deployment targets
- Development and testing environments
- Monitoring and observability tools
- Security and compliance tooling
- Collaboration and communication platforms
- Automation and orchestration systems
### 4. Knowledge Management
- Information organization and categorization
- Documentation standards and practices
- Knowledge sharing and dissemination
- Learning and improvement processes
- Archive and retention policies
- Search and discovery optimization
### 5. Continuous Improvement
- Retrospective and review processes
- Metric tracking and analysis
- Process refinement and optimization
- Technology evaluation and adoption
- Skill development and training
- Innovation and experimentation approaches
## Response Format
Please provide responses in the following structured format:
```yaml
operational_procedures:
routines:
daily: []
weekly: []
monthly: []
system_administration: []
monitoring_procedures: []
backup_recovery: []
security_practices: []
documentation_management: []
project_management:
initiation_planning: []
task_tracking: []
resource_allocation: []
risk_management: []
stakeholder_communication: []
quality_assurance: []
infrastructure_tools:
hosting_platforms: []
development_environments: []
monitoring_tools: []
security_tooling: []
collaboration_platforms: []
automation_systems: []
knowledge_management:
information_organization: []
documentation_practices: []
knowledge_sharing: []
learning_processes: []
archive_policies: []
search_optimization: []
continuous_improvement:
retrospective_processes: []
metric_tracking: []
process_refinement: []
technology_evaluation: []
skill_development: []
innovation_approaches: []
```
## Validation Criteria
- Alignment with current operational reality
- Completeness of key operational areas
- Practicality of implementation
- Consistency with documented procedures
- Relevance to current projects and initiatives
## Frequency
This intake should be updated:
- Quarterly for operational reviews
- As-needed for procedure changes
- Semi-annually for infrastructure updates
- Annually for comprehensive process reviews

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# Personal Information Intake Template
## Overview
This template guides the collection of personal information for databank population.
## Interview Structure
### 1. Basic Identity
- Full legal name
- Preferred name/nickname
- Online handles and professional identities
- Contact preferences and methods
- Geographic location (current and planned moves)
- Age/birth year
### 2. Professional Background
- Career timeline and key positions
- Core competencies and specializations
- Industry experience and expertise areas
- Professional certifications and qualifications
- Notable achievements and recognitions
- Current professional focus and goals
### 3. Philosophical Positions
- Core values and beliefs
- Political affiliations and civic positions
- Ethical frameworks and guiding principles
- Approach to work and collaboration
- Views on technology and AI integration
- Stance on data privacy and sovereignty
### 4. Technical Preferences
- Preferred tools and platforms
- Technology stack and environment
- AI tool usage patterns and preferences
- Development methodologies and practices
- Security and privacy practices
- Automation and efficiency approaches
### 5. Lifestyle and Context
- Daily schedule and work patterns
- Communication preferences and style
- Collaboration approaches and expectations
- Work-life balance priorities
- Ongoing projects and initiatives
- Future plans and aspirations
### 6. Relationships and Networks
- Key professional relationships
- Organizational affiliations
- Community involvement
- Mentorship and advisory roles
- Partnership and collaboration patterns
## Response Format
Please provide responses in the following structured format:
```yaml
identity:
legal_name: ""
preferred_name: ""
handles:
- platform: ""
handle: ""
contact_preferences:
- method: ""
preference: "" # high/medium/low
location:
current: ""
planned_moves: []
birth_year: 0
professional_background:
career_timeline: []
core_competencies: []
industry_experience: []
certifications: []
achievements: []
current_focus: ""
philosophical_positions:
core_values: []
political_affiliations: []
ethical_frameworks: []
approach_to_work: ""
ai_integration_views: ""
data_privacy_stances: []
technical_preferences:
preferred_tools: []
technology_stack: []
ai_tool_patterns: []
development_methods: []
security_practices: []
automation_approaches: []
lifestyle_context:
daily_schedule: ""
communication_preferences: ""
collaboration_approach: ""
work_life_balance: ""
ongoing_projects: []
future_plans: []
relationships_networks:
key_relationships: []
organizational_affiliations: []
community_involvement: []
mentorship_roles: []
collaboration_patterns: []
```
## Validation Criteria
- Completeness of all sections
- Consistency with existing databank information
- Plausibility and internal coherence
- Relevance to professional and technical context
- Sufficient detail for AI agent understanding
## Frequency
This intake should be updated:
- Annually for major life changes
- Quarterly for ongoing project updates
- As-needed for significant changes in circumstances

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# Intake Processing Workflow
## Overview
This workflow describes the process for converting intake responses into synchronized human and LLM formats.
## Workflow Steps
### 1. Response Collection
- Receive completed intake templates
- Validate completeness and basic formatting
- Store in `responses/` directory with timestamp and identifier
- Create processing ticket/task in tracking system
### 2. Initial Processing
- Parse structured response data
- Identify sections requiring human review
- Flag inconsistencies or unclear responses
- Generate initial conversion drafts
### 3. Human Review and Validation
- Review parsed data for accuracy
- Validate against existing databank information
- Resolve flagged issues and ambiguities
- Approve or reject conversion drafts
### 4. Format Conversion
- Convert validated data to human-friendly markdown
- Convert validated data to LLM-optimized structured formats
- Generate cross-references and links
- Apply formatting standards and conventions
### 5. Synchronization
- Update both `../human/` and `../llm/` directories
- Maintain version history and change tracking
- Update README and index files as needed
- Validate synchronization integrity
### 6. Quality Assurance
- Verify formatting consistency
- Check cross-reference integrity
- Validate change tracking accuracy
- Confirm synchronization between formats
### 7. Documentation and Notification
- Update processing logs and metrics
- Notify stakeholders of updates
- Archive processing artifacts
- Close processing tickets/tasks
## Automation Opportunities
### Parsing and Validation
- Automated YAML/JSON schema validation
- Consistency checking against existing data
- Completeness verification
- Basic formatting normalization
### Format Conversion
- Template-driven markdown generation
- Structured data serialization
- Cross-reference generation
- Index and navigation updating
### Synchronization
- Automated file placement and naming
- Version tracking table updates
- Conflict detection and resolution
- Integrity verification
## Manual Review Requirements
### Complex Judgments
- Interpretation of ambiguous responses
- Resolution of conflicting information
- Quality assessment of converted content
- Approval of significant changes
### Creative Tasks
- Crafting human-friendly explanations
- Optimizing LLM data structures
- Designing intuitive navigation
- Balancing detail and conciseness
## Quality Gates
### Gate 1: Response Acceptance
- [ ] Response received and stored
- [ ] Basic formatting validated
- [ ] Completeness verified
- [ ] Processing ticket created
### Gate 2: Data Validation
- [ ] Structured data parsed successfully
- [ ] Inconsistencies identified and flagged
- [ ] Initial drafts generated
- [ ] Review tasks assigned
### Gate 3: Human Approval
- [ ] Manual review completed
- [ ] Issues resolved
- [ ] Conversion drafts approved
- [ ] Quality gate checklist signed off
### Gate 4: Format Conversion
- [ ] Human-friendly markdown generated
- [ ] LLM-optimized formats created
- [ ] Cross-references established
- [ ] Formatting standards applied
### Gate 5: Synchronization
- [ ] Both directories updated
- [ ] Version tracking maintained
- [ ] Integrity verified
- [ ] Change notifications prepared
### Gate 6: Quality Assurance
- [ ] Formatting consistency verified
- [ ] Cross-reference integrity confirmed
- [ ] Change tracking accuracy validated
- [ ] Final approval obtained
## Metrics and Tracking
### Processing Efficiency
- Time from response receipt to completion
- Automation vs. manual effort ratio
- Error rate and rework frequency
- Stakeholder satisfaction scores
### Quality Measures
- Accuracy of parsed data
- Completeness of converted content
- Consistency between formats
- User feedback and adoption rates
### Continuous Improvement
- Bottleneck identification and resolution
- Automation opportunity tracking
- Process optimization initiatives
- Skill development and training needs

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# Intake Processing Workflow
This script processes intake responses and converts them to both human and LLM formats.
```bash
#!/bin/bash
# intake-workflow.sh
INTAKE_DIR="../intake/responses"
HUMAN_OUTPUT="../../human"
LLM_OUTPUT="../../llm"
ARTIFACTS_DIR="../../artifacts"
echo "Starting intake processing workflow..."
# Process each intake response
for response in "$INTAKE_DIR"/*.yaml; do
if [[ -f "$response" ]]; then
filename=$(basename "$response" .yaml)
echo "Processing $filename..."
# Convert to human-friendly markdown
# python3 convert-intake-to-human.py "$response" "$HUMAN_OUTPUT/$filename.md"
# Convert to LLM-optimized JSON
# python3 convert-intake-to-llm.py "$response" "$LLM_OUTPUT/$filename.json"
# Store canonical version
# cp "$response" "$ARTIFACTS_DIR/$filename.yaml"
echo "Completed processing $filename"
fi
done
echo "Intake processing workflow completed."
```