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feat: Refactor and enhance brainstorming workflow commands

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- Removed deprecated `gemini-init.md` command and migrated its functionality to a new structure under `.claude/commands/gemini/`.
- Introduced `auto-parallel.md` for parallel brainstorming automation with dynamic role selection and concurrent execution.
- Added `auto-squeeze.md` for sequential command coordination in brainstorming workflows, ensuring structured execution from framework generation to synthesis.
- Updated `plan.md` to improve clarity on project structure analysis and command execution strategies.
- Enhanced error handling and session management across all commands to ensure robustness and user guidance.
- Improved documentation for generated files and command options to facilitate user understanding and usage.
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catlog22
2025-09-29 15:48:42 +08:00
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.claude/commands/workflow/brainstorm/data-architect.md
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---
name: data-architect
description: Data architect perspective brainstorming for data modeling, flow, and analytics analysis
usage: /workflow:brainstorm:data-architect <topic>
argument-hint: "topic or challenge to analyze from data architecture perspective"
description: Generate or update data-architect/analysis.md addressing topic-framework discussion points
usage: /workflow:brainstorm:data-architect [topic]
argument-hint: "optional topic - uses existing framework if available"
examples:
- /workflow:brainstorm:data-architect
- /workflow:brainstorm:data-architect "user analytics data pipeline"
- /workflow:brainstorm:data-architect "real-time data processing system"
- /workflow:brainstorm:data-architect "data warehouse modernization"
allowed-tools: Task(conceptual-planning-agent), TodoWrite(*)
allowed-tools: Task(conceptual-planning-agent), TodoWrite(*), Read(*), Write(*)
---
## 📊 **Role Overview: Data Architect**
## 📊 **Data Architect Analysis Generator**
### Role Definition
Strategic data professional responsible for designing scalable, efficient data architectures that enable data-driven decision making through robust data models, processing pipelines, and analytics platforms.
### Purpose
**Specialized command for generating data-architect/analysis.md** that addresses topic-framework.md discussion points from data architecture perspective. Creates or updates role-specific analysis with framework references.
### Core Responsibilities
- **Data Model Design**: Create efficient and scalable data models and schemas
- **Data Flow Design**: Plan data collection, processing, and storage workflows
- **Data Quality Management**: Ensure data accuracy, completeness, and consistency
- **Analytics and Insights**: Design data analysis and business intelligence solutions
### Core Function
- **Framework-based Analysis**: Address each discussion point in topic-framework.md
- **Data Architecture Focus**: Data models, pipelines, governance, and analytics perspective
- **Update Mechanism**: Create new or update existing analysis.md
- **Agent Delegation**: Use conceptual-planning-agent for analysis generation
### Focus Areas
- **Data Modeling**: Relational models, NoSQL, data warehouses, lakehouse architectures
- **Data Pipelines**: ETL/ELT processes, real-time processing, batch processing
- **Data Governance**: Data quality, security, privacy, compliance frameworks
- **Analytics Platforms**: BI tools, machine learning infrastructure, reporting systems
### Analysis Scope
- **Data Model Design**: Efficient and scalable data models and schemas
- **Data Flow Design**: Data collection, processing, and storage workflows
- **Data Quality Management**: Data accuracy, completeness, and consistency
- **Analytics and Insights**: Data analysis and business intelligence solutions
### Success Metrics
- Data quality and consistency metrics
- Processing performance and throughput
- Analytics accuracy and business impact
- Data governance and compliance adherence
## ⚙️ **Execution Protocol**
## 🧠 **Analysis Framework**
@~/.claude/workflows/brainstorming-principles.md
### Key Analysis Questions
**1. Data Requirements and Sources**
- What data is needed to support business decisions and analytics?
- How reliable and high-quality are the available data sources?
- What is the balance between real-time and historical data needs?
**2. Data Architecture and Storage**
- What is the most appropriate data storage solution for requirements?
- How should we design scalable and maintainable data models?
- What are the optimal data partitioning and indexing strategies?
**3. Data Processing and Workflows**
- What are the performance requirements for data processing?
- How should we design fault-tolerant and resilient data pipelines?
- What data versioning and change management strategies are needed?
**4. Analytics and Reporting**
- How can we support diverse analytical requirements and use cases?
- What balance between real-time dashboards and periodic reports is optimal?
- What self-service analytics and data visualization capabilities are needed?
## ⚡ **Two-Step Execution Flow**
### ⚠️ Session Management - FIRST STEP
Session detection and selection:
### Phase 1: Session & Framework Detection
```bash
# Check for active sessions
active_sessions=$(find .workflow -name ".active-*" 2>/dev/null)
if [ multiple_sessions ]; then
prompt_user_to_select_session()
else
use_existing_or_create_new()
fi
# Check active session and framework
CHECK: .workflow/.active-* marker files
IF active_session EXISTS:
session_id = get_active_session()
brainstorm_dir = .workflow/WFS-{session}/.brainstorming/
CHECK: brainstorm_dir/topic-framework.md
IF EXISTS:
framework_mode = true
load_framework = true
ELSE:
IF topic_provided:
framework_mode = false # Create analysis without framework
ELSE:
ERROR: "No framework found and no topic provided"
```
### Step 1: Context Gathering Phase
**Data Architect Perspective Questioning**
### Phase 2: Analysis Mode Detection
```bash
# Determine execution mode
IF framework_mode == true:
mode = "framework_based_analysis"
topic_ref = load_framework_topic()
discussion_points = extract_framework_points()
ELSE:
mode = "standalone_analysis"
topic_ref = provided_topic
discussion_points = generate_basic_structure()
```
Before agent assignment, gather comprehensive data architect context:
#### 📋 Role-Specific Questions
**1. Data Models and Flow Patterns**
- What types of data will you be working with (structured, semi-structured, unstructured)?
- What are the expected data volumes and growth projections?
- What are the primary data sources and how frequently will data be updated?
- Are there existing data models or schemas that need to be considered?
**2. Storage Strategies and Performance**
- What are the query performance requirements and expected response times?
- Do you need real-time processing, batch processing, or both?
- What are the data retention and archival requirements?
- Are there specific compliance or regulatory requirements for data storage?
**3. Analytics Requirements and Insights**
- What types of analytics and reporting capabilities are needed?
- Who are the primary users of the data and what are their skill levels?
- What business intelligence or machine learning use cases need to be supported?
- Are there specific dashboard or visualization requirements?
**4. Data Governance and Quality**
- What data quality standards and validation rules need to be implemented?
- Who owns the data and what are the access control requirements?
- Are there data privacy or security concerns that need to be addressed?
- What data lineage and auditing capabilities are required?
#### Context Validation
- **Minimum Response**: Each answer must be ≥50 characters
- **Re-prompting**: Insufficient detail triggers follow-up questions
- **Context Storage**: Save responses to `.brainstorming/data-architect-context.md`
### Step 2: Agent Assignment with Flow Control
**Dedicated Agent Execution**
### Phase 3: Agent Execution with Flow Control
**Framework-Based Analysis Generation**
```bash
Task(conceptual-planning-agent): "
[FLOW_CONTROL]
Execute dedicated data architect conceptual analysis for: {topic}
Execute data-architect analysis for existing topic framework
## Context Loading
ASSIGNED_ROLE: data-architect
OUTPUT_LOCATION: .brainstorming/data-architect/
USER_CONTEXT: {validated_responses_from_context_gathering}
OUTPUT_LOCATION: .workflow/WFS-{session}/.brainstorming/data-architect/
ANALYSIS_MODE: {framework_mode ? "framework_based" : "standalone"}
Flow Control Steps:
[
{
\"step\": \"load_role_template\",
\"action\": \"Load data-architect planning template\",
\"command\": \"bash($(cat ~/.claude/workflows/cli-templates/planning-roles/data-architect.md))\",
\"output_to\": \"role_template\"
}
]
## Flow Control Steps
1. **load_topic_framework**
- Action: Load structured topic discussion framework
- Command: Read(.workflow/WFS-{session}/.brainstorming/topic-framework.md)
- Output: topic_framework_content
Conceptual Analysis Requirements:
- Apply data architect perspective to topic analysis
- Focus on data models, flow patterns, storage strategies, and analytics requirements
- Use loaded role template framework for analysis structure
- Generate role-specific deliverables in designated output location
- Address all user context from questioning phase
2. **load_role_template**
- Action: Load data-architect planning template
- Command: bash($(cat ~/.claude/workflows/cli-templates/planning-roles/data-architect.md))
- Output: role_template_guidelines
Deliverables:
- analysis.md: Main data architect analysis
- recommendations.md: Data architect recommendations
- deliverables/: Data architect-specific outputs as defined in role template
3. **load_session_metadata**
- Action: Load session metadata and existing context
- Command: Read(.workflow/WFS-{session}/.brainstorming/session.json)
- Output: session_context
Embody data architect role expertise for comprehensive conceptual planning."
## Analysis Requirements
**Framework Reference**: Address all discussion points in topic-framework.md from data architecture perspective
**Role Focus**: Data models, pipelines, governance, analytics platforms
**Structured Approach**: Create analysis.md addressing framework discussion points
**Template Integration**: Apply role template guidelines within framework structure
## Expected Deliverables
1. **analysis.md**: Comprehensive data architecture analysis addressing all framework discussion points
2. **Framework Reference**: Include @../topic-framework.md reference in analysis
## Completion Criteria
- Address each discussion point from topic-framework.md with data architecture expertise
- Provide data model designs, pipeline architectures, and governance strategies
- Include scalability, performance, and quality considerations
- Reference framework document using @ notation for integration
"
```
### Progress Tracking
TodoWrite tracking for two-step process:
```json
[
{"content": "Gather data architect context through role-specific questioning", "status": "in_progress", "activeForm": "Gathering context"},
{"content": "Validate context responses and save to data-architect-context.md", "status": "pending", "activeForm": "Validating context"},
{"content": "Load data-architect planning template via flow control", "status": "pending", "activeForm": "Loading template"},
{"content": "Execute dedicated conceptual-planning-agent for data-architect role", "status": "pending", "activeForm": "Executing agent"}
]
## 📋 **TodoWrite Integration**
### Workflow Progress Tracking
```javascript
TodoWrite({
todos: [
{
content: "Detect active session and locate topic framework",
status: "in_progress",
activeForm: "Detecting session and framework"
},
{
content: "Load topic-framework.md and session metadata for context",
status: "pending",
activeForm: "Loading framework and session context"
},
{
content: "Execute data-architect analysis using conceptual-planning-agent with FLOW_CONTROL",
status: "pending",
activeForm: "Executing data-architect framework analysis"
},
{
content: "Generate analysis.md addressing all framework discussion points",
status: "pending",
activeForm: "Generating structured data-architect analysis"
},
{
content: "Update session.json with data-architect completion status",
status: "pending",
activeForm: "Updating session metadata"
}
]
});
```
## 📊 **Output Specification**
## 📊 **Output Structure**
### Output Location
### Framework-Based Analysis
```
.workflow/WFS-{topic-slug}/.brainstorming/data-architect/
├── analysis.md # Primary data architecture analysis
├── data-model.md # Data models, schemas, and relationships
├── pipeline-design.md # Data processing and ETL/ELT workflows
└── governance-plan.md # Data quality, security, and governance
.workflow/WFS-{session}/.brainstorming/data-architect/
└── analysis.md # Structured analysis addressing topic-framework.md discussion points
```
### Document Templates
#### analysis.md Structure
### Analysis Document Structure
```markdown
# Data Architect Analysis: {Topic}
*Generated: {timestamp}*
# Data Architect Analysis: [Topic from Framework]
## Executive Summary
[Key data architecture findings and recommendations overview]
## Framework Reference
**Topic Framework**: @../topic-framework.md
**Role Focus**: Data Architecture perspective
## Current Data Landscape
### Existing Data Sources
### Current Data Architecture
### Data Quality Assessment
### Performance Bottlenecks
## Discussion Points Analysis
[Address each point from topic-framework.md with data architecture expertise]
## Data Requirements Analysis
### Business Data Needs
### Technical Data Requirements
### Data Volume and Growth Projections
### Real-time vs Batch Processing Needs
### Core Requirements (from framework)
[Data architecture perspective on requirements]
## Proposed Data Architecture
### Data Model Design
### Storage Architecture
### Processing Pipeline Design
### Integration Patterns
### Technical Considerations (from framework)
[Data model, pipeline, and storage considerations]
## Data Quality and Governance
### Data Quality Framework
### Governance Policies
### Security and Privacy Controls
### Compliance Requirements
### User Experience Factors (from framework)
[Data access patterns and analytics user experience]
## Analytics and Reporting Strategy
### Business Intelligence Architecture
### Self-Service Analytics Design
### Performance Monitoring
### Scalability Planning
### Implementation Challenges (from framework)
[Data migration, quality, and governance challenges]
## Implementation Roadmap
### Migration Strategy
### Technology Stack Recommendations
### Resource Requirements
### Risk Mitigation Plan
### Success Metrics (from framework)
[Data quality metrics and analytics success criteria]
## Data Architecture Specific Recommendations
[Role-specific data architecture recommendations and solutions]
---
*Generated by data-architect analysis addressing structured framework*
```
## 🔄 **Session Integration**
### Status Synchronization
Upon completion, update `workflow-session.json`:
### Completion Status Update
```json
{
"phases": {
"BRAINSTORM": {
"data_architect": {
"status": "completed",
"completed_at": "timestamp",
"output_directory": ".workflow/WFS-{topic}/.brainstorming/data-architect/",
"key_insights": ["data_model_optimization", "pipeline_architecture", "analytics_strategy"]
}
}
"data_architect": {
"status": "completed",
"framework_addressed": true,
"output_location": ".workflow/WFS-{session}/.brainstorming/data-architect/analysis.md",
"framework_reference": "@../topic-framework.md"
}
}
```
### Cross-Role Collaboration
Data architect perspective provides:
- **Data Storage Requirements** → System Architect
- **Analytics Data Requirements** → Product Manager
- **Data Visualization Specifications** → UI Designer
- **Data Security Framework** → Security Expert
- **Feature Data Requirements** → Feature Planner
## ✅ **Quality Assurance**
### Required Architecture Elements
- [ ] Comprehensive data model with clear relationships and constraints
- [ ] Scalable data pipeline design with error handling and monitoring
- [ ] Data quality framework with validation rules and metrics
- [ ] Governance plan addressing security, privacy, and compliance
- [ ] Analytics architecture supporting business intelligence needs
### Data Architecture Principles
- [ ] **Scalability**: Architecture can handle data volume and velocity growth
- [ ] **Quality**: Built-in data validation, cleansing, and quality monitoring
- [ ] **Security**: Data protection, access controls, and privacy compliance
- [ ] **Performance**: Optimized for query performance and processing efficiency
- [ ] **Maintainability**: Clear data lineage, documentation, and change management
### Implementation Validation
- [ ] **Technical Feasibility**: All proposed solutions are technically achievable
- [ ] **Performance Requirements**: Architecture meets processing and query performance needs
- [ ] **Cost Effectiveness**: Storage and processing costs are optimized and sustainable
- [ ] **Governance Compliance**: Meets regulatory and organizational data requirements
- [ ] **Future Readiness**: Design accommodates anticipated growth and changing needs
### Data Quality Standards
- [ ] **Accuracy**: Data validation rules ensure correctness and consistency
- [ ] **Completeness**: Strategies for handling missing data and ensuring coverage
- [ ] **Timeliness**: Data freshness requirements met through appropriate processing
- [ ] **Consistency**: Data definitions and formats standardized across systems
- [ ] **Lineage**: Complete data lineage tracking from source to consumption
### Integration Points
- **Framework Reference**: @../topic-framework.md for structured discussion points
- **Cross-Role Synthesis**: Data architecture insights available for synthesis-report.md integration
- **Agent Autonomy**: Independent execution with framework guidance