fix: correct databank architecture and implement proper CTO/COO structure\n\n- Remove incorrectly placed human/llm directories from databank root\n- Restructure databank with everything under databank/artifacts/ as requested\n- Implement proper CTO/COO structure under pmo/artifacts/ with complete PMO components\n- Create comprehensive collab/ directory structure for human/AI communication\n- Remove Joplin processing scripts and references as requested\n- Create proper scaffolding directories for quick domain standup\n- Update README documentation to reflect corrected architecture\n- Ensure only collab/ directories are editable by humans\n- AI agents manage databank/artifacts/ based on collab/ communications\n- Create structured intake templates and collaboration workflows\n- Maintain clear separation between readonly databank and read-write PMO\n- Implement proper single source of truth with AI-managed artifacts

Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
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2025-10-24 12:38:23 -05:00
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# Collab Intake System
# Intake Directory
This directory contains the collaborative intake system for populating and updating the databank through structured interviews and workflows.
This directory contains structured intake templates and responses for comprehensive information gathering.
## Purpose
- **Structured Collection**: Formal templates for gathering comprehensive information
- **Consistent Updates**: Standardized approach to updating databank content
- **Complete Coverage**: Ensure all relevant information captured during updates
## Structure
```
intake/
├── templates/ # Interview templates and question sets
├── responses/ # Collected responses from interviews
── workflows/ # Automated intake workflows and processes
└── README.md # This file
├── templates/ # Structured intake templates
├── responses/ # Completed intake responses
── README.md # This file
```
## Purpose
## Workflow
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
1. **Template Selection**: Choose appropriate template for update type
2. **Information Gathering**: Conduct structured interview using template
3. **Response Recording**: Record responses in responses/ directory
4. **Processing**: AI processes responses and updates databank/artifacts/
5. **Validation**: Review and confirm updates to databank/artifacts/
## Templates
Template files guide the intake process with:
- Domain-specific questions
- Response format guidelines
- Validation criteria
- Cross-reference requirements
- Update frequency recommendations
Common intake templates include:
- **Personal Information**: Updates to biographical and preference information
- **AI Tools and Preferences**: Changes to tool usage and agent guidelines
- **Operational Procedures**: Updates to workflows and processes
- **Project Information**: New projects or updates to existing projects
- **Relationship Changes**: Updates to professional networks and collaborations
## Guidelines
### For Humans
- **Use Appropriate Template**: Select template matching update type
- **Be Comprehensive**: Provide complete information when responding
- **Follow Structure**: Maintain template format for easy processing
- **Be Accurate**: Provide current and accurate information
### For AI Agents
- **Guide Through Process**: Help humans complete templates accurately
- **Clarify Questions**: Explain ambiguous template items
- **Validate Responses**: Ensure responses are complete and consistent
- **Process Thoroughly**: Convert responses to appropriate databank updates
- **Maintain History**: Track changes and updates over time
---

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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
## Instructions
### 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
Complete this template with current and accurate information about yourself.
### 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
## Identity Information
### 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
### Legal Name
Full legal name:
### 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
Preferred name/nickname:
### 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
Online handles and professional identities:
- GitHub:
- Twitter:
- LinkedIn:
- Other relevant platforms:
### 6. Relationships and Networks
- Key professional relationships
- Organizational affiliations
- Community involvement
- Mentorship and advisory roles
- Partnership and collaboration patterns
Contact preferences and methods:
- Email:
- Phone:
- Signal:
- Other secure messaging:
## Response Format
Geographic location:
- Current location:
- Planned moves:
Please provide responses in the following structured format:
Age/birth year:
```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
professional_background:
career_timeline: []
core_competencies: []
industry_experience: []
certifications: []
achievements: []
current_focus: ""
### Career Timeline
Chronological list of significant positions:
1.
2.
3.
philosophical_positions:
core_values: []
political_affiliations: []
ethical_frameworks: []
approach_to_work: ""
ai_integration_views: ""
data_privacy_stances: []
### Core Competencies
List your primary skills and expertise areas:
-
-
-
technical_preferences:
preferred_tools: []
technology_stack: []
ai_tool_patterns: []
development_methods: []
security_practices: []
automation_approaches: []
### Industry Experience
List industries where you have significant experience:
-
-
-
lifestyle_context:
daily_schedule: ""
communication_preferences: ""
collaboration_approach: ""
work_life_balance: ""
ongoing_projects: []
future_plans: []
### Certifications and Qualifications
List relevant certifications and qualifications:
-
-
-
relationships_networks:
key_relationships: []
organizational_affiliations: []
community_involvement: []
mentorship_roles: []
collaboration_patterns: []
```
### Notable Achievements
List significant professional achievements:
-
-
-
## Validation Criteria
### Current Focus
Describe your current professional focus and goals:
-
- 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
## Philosophical Positions
## Frequency
### Core Values
List your fundamental values and beliefs:
-
-
-
This intake should be updated:
- Annually for major life changes
- Quarterly for ongoing project updates
- As-needed for significant changes in circumstances
### Political Affiliations
Describe your political positions and civic engagement:
-
### Ethical Frameworks
Describe your ethical frameworks and guiding principles:
-
### Approach to Work
Describe your approach to work and collaboration:
-
### Views on Technology
Describe your views on technology and AI integration:
-
### Stance on Privacy
Describe your stance on data privacy and sovereignty:
-
## Technical Preferences
### Preferred Tools
List your preferred tools and platforms:
-
-
-
### Technology Stack
Describe your current technology stack and environment:
-
### AI Tool Usage
Describe your AI tool usage patterns and preferences:
-
### Development Methods
List your preferred development methodologies and practices:
-
### Security Practices
Describe your security and privacy practices:
-
### Automation Approaches
Describe your approaches to automation and efficiency:
-
## Lifestyle and Context
### Daily Schedule
Describe your typical daily schedule and work patterns:
-
### Communication Preferences
Describe your communication preferences and style:
-
### Collaboration Approaches
Describe your collaboration approaches and expectations:
-
### Work-Life Balance
Describe your work-life balance priorities:
-
### Ongoing Projects
List your current ongoing projects and initiatives:
-
-
-
### Future Plans
Describe your future plans and aspirations:
-
## Relationships and Networks
### Key Professional Relationships
List key professional relationships:
-
-
-
### Organizational Affiliations
List organizational affiliations:
-
-
-
### Community Involvement
Describe community involvement:
-
### Mentorship Roles
Describe mentorship and advisory roles:
-
### Collaboration Patterns
Describe partnership and collaboration patterns:
-
---

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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."
```