Add comprehensive brainstorming commands for synthesis, system architecture, UI design, and user research perspectives

- Implemented the `brainstorm:synthesis` command to integrate insights from various roles into a cohesive analysis and recommendations report. - Created `brainstorm:system-architect` command for technical architecture and scalability analysis, including detailed execution protocols and output structures. - Developed `brainstorm:ui-designer` command focusing on user experience and interface design, with a structured approach to analysis and documentation. - Introduced `brainstorm:user-researcher` command for user behavior analysis and research insights, emphasizing user needs and usability assessments.
2026-02-13 02:41:50 +08:00 · 2025-09-08 23:35:23 +08:00
parent 1454c49ab0
commit d643a59307
11 changed files with 2568 additions and 506 deletions
--- a/.claude/commands/workflow/brainstorm/data-architect.md
+++ b/.claude/commands/workflow/brainstorm/data-architect.md
@@ -0,0 +1,253 @@
+---
+name: brainstorm:data-architect
+description: Data architect perspective brainstorming for data modeling, flow, and analytics analysis
+usage: /brainstorm:data-architect <topic>
+argument-hint: "topic or challenge to analyze from data architecture perspective"
+examples:
+  - /brainstorm:data-architect "user analytics data pipeline"
+  - /brainstorm:data-architect "real-time data processing system"
+  - /brainstorm:data-architect "data warehouse modernization"
+allowed-tools: Task(conceptual-planning-agent), TodoWrite(*)
+---
+
+## 📊 **角色定义: Data Architect**
+
+### 核心职责
+- **数据模型设计**: 设计高效、可扩展的数据模型
+- **数据流程设计**: 规划数据采集、处理、存储流程
+- **数据质量管理**: 确保数据准确性、完整性、一致性
+- **分析和洞察**: 设计数据分析和商业智能解决方案
+
+### 关注领域
+- **数据建模**: 关系模型、NoSQL、数据仓库、湖仓一体
+- **数据管道**: ETL/ELT流程、实时处理、批处理
+- **数据治理**: 数据质量、安全、隐私、合规
+- **分析平台**: BI工具、机器学习、报表系统
+
+## 🧠 **分析框架**
+
+@~/.claude/workflows/brainstorming-principles.md
+@~/.claude/workflows/conceptual-planning-agent.md
+
+### 核心分析问题
+1. **数据需求和来源**:
+   - 需要哪些数据来支持业务决策？
+   - 数据来源的可靠性和质量如何？
+   - 实时数据vs历史数据的需求平衡？
+
+2. **数据架构和存储**:
+   - 最适合的数据存储方案是什么？
+   - 如何设计可扩展的数据模型？
+   - 数据分区和索引策略？
+
+3. **数据处理和流程**:
+   - 数据处理的性能要求？
+   - 如何设计容错的数据管道？
+   - 数据变更和版本控制策略？
+
+4. **分析和报告**:
+   - 如何支持不同的分析需求？
+   - 实时仪表板vs定期报告？
+   - 数据可视化和自助分析能力？
+
+## ⚙️ **执行协议**
+
+### Phase 1: 会话检测与初始化
+```bash
+# 自动检测活动会话
+CHECK: .workflow/.active-* marker files
+IF active_session EXISTS:
+    session_id = get_active_session()
+    load_context_from(session_id)
+ELSE:
+    request_user_for_session_creation()
+```
+
+### Phase 2: 目录结构创建
+```bash
+# 创建数据架构师分析目录
+mkdir -p .workflow/WFS-{topic-slug}/.brainstorming/data-architect/
+```
+
+### Phase 3: TodoWrite 初始化
+设置数据架构师视角分析的任务跟踪：
+```json
+[
+  {"content": "Initialize data architect brainstorming session", "status": "completed", "activeForm": "Initializing session"},
+  {"content": "Analyze data requirements and sources", "status": "in_progress", "activeForm": "Analyzing data requirements"},
+  {"content": "Design optimal data model and schema", "status": "pending", "activeForm": "Designing data model"},
+  {"content": "Plan data pipeline and processing workflows", "status": "pending", "activeForm": "Planning data pipelines"},
+  {"content": "Evaluate data quality and governance", "status": "pending", "activeForm": "Evaluating data governance"},
+  {"content": "Design analytics and reporting solutions", "status": "pending", "activeForm": "Designing analytics"},
+  {"content": "Generate comprehensive data architecture documentation", "status": "pending", "activeForm": "Generating documentation"}
+]
+```
+
+### Phase 4: 概念规划代理协调
+```bash
+Task(conceptual-planning-agent): "
+Conduct data architect perspective brainstorming for: {topic}
+
+ROLE CONTEXT: Data Architect
+- Focus Areas: Data modeling, data flow, storage optimization, analytics infrastructure
+- Analysis Framework: Data-driven approach with emphasis on scalability, quality, and insights
+- Success Metrics: Data quality, processing efficiency, analytics accuracy, scalability
+
+USER CONTEXT: {captured_user_requirements_from_session}
+
+ANALYSIS REQUIREMENTS:
+1. Data Requirements Analysis
+   - Identify all data sources (internal, external, third-party)
+   - Analyze data types, volumes, and velocity requirements
+   - Define data freshness and latency requirements
+   - Assess data quality and completeness standards
+
+2. Data Architecture Design
+   - Design logical and physical data models
+   - Plan data storage strategy (relational, NoSQL, data lake, warehouse)
+   - Design data partitioning and sharding strategies
+   - Plan for data archival and retention policies
+
+3. Data Pipeline and Processing
+   - Design ETL/ELT processes and data transformation workflows
+   - Plan real-time vs batch processing requirements
+   - Design error handling and data recovery mechanisms
+   - Plan for data lineage and audit trails
+
+4. Data Quality and Governance
+   - Design data validation and quality monitoring
+   - Plan data governance framework and policies
+   - Assess privacy and compliance requirements (GDPR, CCPA, etc.)
+   - Design data access controls and security measures
+
+5. Analytics and Reporting Infrastructure
+   - Design data warehouse/data mart architecture
+   - Plan business intelligence and reporting solutions
+   - Design self-service analytics capabilities
+   - Plan for machine learning and advanced analytics integration
+
+6. Performance and Scalability
+   - Analyze current and projected data volumes
+   - Design indexing and query optimization strategies
+   - Plan horizontal and vertical scaling approaches
+   - Design monitoring and alerting for data systems
+
+OUTPUT REQUIREMENTS: Save comprehensive analysis to:
+.workflow/WFS-{topic-slug}/.brainstorming/data-architect/
+- analysis.md (main data architecture analysis)
+- data-model.md (detailed data models and schemas)
+- pipeline-design.md (data processing workflows and ETL design)
+- governance-plan.md (data quality, security, and compliance framework)
+
+Apply data architecture expertise to create scalable, reliable, and insightful data solutions."
+```
+
+## 📊 **输出结构**
+
+### 保存位置
+```
+.workflow/WFS-{topic-slug}/.brainstorming/data-architect/
+├── analysis.md                 # 主要数据架构分析
+├── data-model.md               # 详细数据模型和架构
+├── pipeline-design.md          # 数据处理工作流和ETL设计
+└── governance-plan.md          # 数据质量、安全和合规框架
+```
+
+### 文档模板
+
+#### analysis.md 结构
+```markdown
+# Data Architect Analysis: {Topic}
+*Generated: {timestamp}*
+
+## Executive Summary
+[核心数据架构发现和建议概述]
+
+## Current Data Landscape Assessment
+### Existing Data Sources
+### Data Quality Issues
+### Performance Bottlenecks
+### Integration Challenges
+
+## Data Requirements Analysis
+### Business Data Requirements
+### Technical Data Requirements
+- Volume: [预期数据量和增长]
+- Velocity: [数据更新频率]
+- Variety: [数据类型和格式]
+- Veracity: [数据质量要求]
+
+## Proposed Data Architecture
+### Data Storage Strategy
+### Data Model Design
+### Integration Architecture
+### Analytics Infrastructure
+
+## Data Pipeline Design
+### Data Ingestion Strategy
+### Processing Workflows
+### Transformation Rules
+### Quality Assurance
+
+## Governance and Compliance
+### Data Quality Framework
+### Security and Privacy
+### Audit and Lineage
+### Compliance Requirements
+
+## Performance and Scalability
+### Optimization Strategies
+### Scaling Plans
+### Monitoring and Alerting
+### Disaster Recovery
+```
+
+## 🔄 **会话集成**
+
+### 状态同步
+分析完成后，更新 `workflow-session.json`:
+```json
+{
+  "phases": {
+    "BRAINSTORM": {
+      "data_architect": {
+        "status": "completed",
+        "completed_at": "timestamp",
+        "output_directory": ".workflow/WFS-{topic}/.brainstorming/data-architect/",
+        "key_insights": ["data_model_optimization", "pipeline_efficiency", "governance_requirement"]
+      }
+    }
+  }
+}
+```
+
+### 与其他角色的协作
+数据架构师视角为其他角色提供：
+- **数据能力和限制** → Product Manager
+- **数据存储要求** → System Architect
+- **数据展示能力** → UI Designer
+- **数据安全要求** → Security Expert
+- **功能数据支持** → Feature Planner
+
+## ✅ **质量标准**
+
+### 必须包含的架构元素
+- [ ] 完整的数据模型设计
+- [ ] 详细的数据流程图
+- [ ] 数据质量保证方案
+- [ ] 可扩展性和性能优化
+- [ ] 合规和安全控制
+
+### 数据架构原则检查
+- [ ] 可扩展性：支持数据量和用户增长
+- [ ] 可靠性：具有容错和恢复机制
+- [ ] 可维护性：清晰的数据模型和流程
+- [ ] 安全性：数据保护和访问控制
+- [ ] 高效性：优化的查询和处理性能
+
+### 数据质量指标
+- [ ] 数据准确性和完整性标准
+- [ ] 数据一致性检查机制
+- [ ] 数据时效性和新鲜度要求
+- [ ] 数据可追溯性和审计能力
+- [ ] 合规性检查和报告机制