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

Co-authored-by: Qwen-Coder <qwen-coder@alibabacloud.com>
2025-10-24 12:15:36 -05:00
parent 61919ae452
commit 919349aad2
34 changed files with 1154 additions and 14 deletions
--- a/databank/collab/intake/workflows/PROCESSING_WORKFLOW.md
+++ b/databank/collab/intake/workflows/PROCESSING_WORKFLOW.md
@@ -0,0 +1,141 @@
+# 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
--- a/databank/collab/intake/workflows/intake-workflow.sh
+++ b/databank/collab/intake/workflows/intake-workflow.sh
@@ -0,0 +1,36 @@
+# 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."
+```