Merge pull request #184 from tebanieo/master

Simplified database prompts

Author: tebanieo

workshops/modernizr/prompts/01-mysql-analysis/tasks.md

@@ -1,6 +1,13 @@
 # MySQL Analysis - Tasks
 
-- [x] 0. Set up Git source control for the project
+**IMPORTANT**: 
+- **Commit changes frequently** throughout this stage to maintain a clean Git history
+- **Use `git -P`** to avoid interactive prompts
+- **Mark tasks as completed** by changing `[ ]` to `[x]` as you finish each task
+- **Commit after each major task completion** with descriptive messages
+- **Update the tasks.md file** itself when marking tasks complete and commit those changes too
+- **DO NOT MODIFY THE CONTENT OF THIS FILE**: Only add a [x] mark to complete the task, for tracking purposes.
+- [ ] 0. Set up Git source control for the project
   - Initialize GIT at the root of this workspace so you have a point to revert your changes if needed in the future
   - You must always use git -P to avoid getting stuck in git operations
   - The subfolders have compiled projects, make sure to create a gitignore so you track only what you will be modifying not the compiled version or the node_modules
@@ -9,7 +16,7 @@
   - Commit the latest changes registered in the working log and other artifacts
   - _Requirements: 1.1, 1.2, 1.3, 1.4, 1.5_
 
-- [x] 1. Identify current application access patterns
+- [ ] 1. Identify current application access patterns
   - Make sure you have a clean commit log
   - From the backend/ folder use the README.md to understand its API endpoints
   - Explore the backend/ folder to understand in detail the available access patterns
@@ -19,34 +26,46 @@
   - Update the file task.md once completed, please let the user know that you have completed this task and summarize what has been done
   - _Requirements: 3.3, 4.2, 4.4_
 
-- [x] 2. Read MySQL source database information
+- [ ] 2. Analyze MySQL database logs for performance patterns
   - Make sure you have a clean commit log
-  - From the database/ folder use the mysql_log_parser.py script to capture mysql query statistics
-  - Use the MySQL MCP server's execute_sql tool to run SQL queries and collect the actual schema information deployed in the database, that will be used to understand the entity relationships
+  - From the database/ folder use the mysql_log_parser.py script to capture mysql query statistics and performance data
+  - Generate `artifacts/stage-01/01_2_mysql_log_analysis.md` with comprehensive analysis of:
+    - Query frequency and patterns from log analysis
+    - Performance bottlenecks and slow queries
+    - Access pattern identification from actual usage
+    - RPS estimates based on log data
+  - Use a temporary file called `artifacts/stage-01/01_2_working_log.md` to track your current work and important notes
+  - Update the file task.md once completed, please let the user know that you have completed this task and summarize what has been done
+  - _Requirements: 2.1, 2.2, 2.3, 2.4, 2.5, 2.6_
+
+- [ ] 3. Extract table structure using MySQL MCP server
+  - Make sure you have a clean commit log
+  - Use the MySQL MCP server's execute_sql tool to run SQL queries and collect the actual schema information deployed in the database
   - Write and execute SQL queries to extract table structures, relationships, indexes, and constraints for the online shopping store database
-  - Using both results generate a comprehensive report where you explain the entity relationship information, table structure and add the access patterns at the bottom to understand the nature of the workload. DO NOT SUGGEST A DYNAMODB TABLE STRUCTURE
-  - Generate focused artifacts as you progress: schema extraction results, entity relationships, table structures, performance analysis, and access patterns in separate files under `artifacts/stage-01/`
-  - Use a temporary file called `artifacts/stage-01/01_2_working_log.md` so you can track what is your current work and important notes and use it as a reference
+  - Generate `artifacts/stage-01/01_3_table_structure_analysis.md` with comprehensive documentation of:
+    - Complete table definitions with columns, data types, and constraints
+    - Primary keys, foreign keys, and indexes
+    - Entity relationships and cardinalities
+    - Current database schema structure
+  - Use a temporary file called `artifacts/stage-01/01_3_working_log.md` to track your current work and important notes
+  - DO NOT SUGGEST A DYNAMODB TABLE STRUCTURE - focus only on current MySQL analysis
   - Update the file task.md once completed, please let the user know that you have completed this task and summarize what has been done
-  - _Requirements: 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 3.1, 3.2, 3.4, 3.5, 4.1, 4.3, 4.5_
+  - _Requirements: 3.1, 3.2, 3.4, 3.5, 4.1, 4.3, 4.5_
 
 ## Output Validation Checklist
 
 Before marking this stage complete, verify:
-- [x] Git repository initialized with proper .gitignore
-- [x] Working log files created in artifacts/stage-00/ and artifacts/stage-01/ directories
-- [ ] Modular artifacts created for each analysis component:
-  - [x] `artifacts/stage-01/01_1_API_access_patterns.md` - Backend API endpoints and access patterns
-  - [ ] `artifacts/stage-01/01_2_schema_extraction.md` - Raw SQL query results
-  - [ ] `artifacts/stage-01/01_3_entity_relationships.md` - E-commerce entity analysis
-  - [ ] `artifacts/stage-01/01_4_table_structures.md` - Detailed table definitions
-  - [ ] `artifacts/stage-01/01_5_performance_analysis.md` - mysql_log_parser.py results
-  - [ ] `artifacts/stage-01/01_6_access_patterns.md` - Identified access patterns
-- [ ] All MySQL tables, relationships, and constraints are documented in focused artifacts
-- [ ] Access patterns are identified from mysql_log_parser.py analysis
-- [ ] Entity relationship information explains e-commerce domain structure
-- [ ] Table structure includes detailed column definitions and constraints
-- [ ] Performance analysis results from Python script are included
+- [ ] Git repository initialized with proper .gitignore
+- [ ] Working log files created in artifacts/stage-00/ and artifacts/stage-01/ directories
+- [ ] **Three essential artifacts created**:
+  - [ ] `artifacts/stage-01/01_1_API_access_patterns.md` - Backend API endpoints and access patterns from API specifications
+  - [ ] `artifacts/stage-01/01_2_mysql_log_analysis.md` - Performance patterns and query statistics from mysql_log_parser.py
+  - [ ] `artifacts/stage-01/01_3_table_structure_analysis.md` - Complete table structures and relationships from MySQL MCP server
+- [ ] API analysis includes detailed endpoint documentation and entity relationships
+- [ ] MySQL log analysis provides query frequency, performance bottlenecks, and RPS estimates
+- [ ] Table structure analysis includes complete schema with relationships, indexes, and constraints
+- [ ] All MySQL tables, relationships, and constraints are documented
+- [ ] Performance data from log analysis is comprehensive
 - [ ] Documentation focuses on current state analysis without DynamoDB suggestions
 - [ ] All changes are committed to Git with meaningful messages
 

workshops/modernizr/prompts/02-a-dynamodb-data-modeling/design.md

@@ -51,22 +51,28 @@ graph TD
 - **Scale Requirements**: [from 01_5_performance_analysis.md]
 
 ## Access Patterns Summary
-[Extracted from 01_1_API_access_patterns.md and 01_6_access_patterns.md]
+[Extracted from 01_1_API_access_patterns.md and 01_2_mysql_log_analysis.md]
 | Pattern | Description | RPS (Peak/Avg) | Type | Entities | Requirements |
 |---------|-------------|-----------------|------|----------|--------------|
 | 1 | Get user by ID | [USER PROVIDED] | Read | User | <50ms latency |
 
 ## Entity Relationships
-[From 01_3_entity_relationships.md]
+[From 01_3_table_structure_analysis.md]
 - User → Orders: 1:Many (cardinality from analysis)
 - Order → OrderItems: 1:Many (cardinality from analysis)
 
 ## Current MySQL Schema
-[From 01_2_schema_extraction.md and 01_4_table_structures.md]
+[From 01_3_table_structure_analysis.md]
 - Tables, columns, indexes, constraints
 - Current performance characteristics
 - Known bottlenecks and issues
 
+## Performance Data
+[From 01_2_mysql_log_analysis.md]
+- Query frequency and patterns from log analysis
+- Performance bottlenecks and slow queries
+- RPS estimates based on actual usage
+
 ## Design Constraints and Preferences
 - Multi-table first approach (not single-table)
 - Natural keys preferred (user_id, order_id vs PK, SK)
@@ -102,6 +108,7 @@ interface MCPDynamoDBDesignRequest {
 }
 
 interface MCPDynamoDBDesignResponse {
+    data_model_document: string; // Complete dynamodb_data_model.md content
     recommended_design: {
         tables: TableDesign[];
         gsis: GSIDesign[];
@@ -116,10 +123,11 @@ interface MCPDynamoDBDesignResponse {
 
 **MCP Server Usage Workflow**:
 1. **Design Request**: Send comprehensive context to MCP server
-2. **Design Analysis**: Receive multiple design options with trade-offs
-3. **Cost Analysis**: Get detailed cost breakdown and optimization suggestions
-4. **Risk Assessment**: Obtain hot partition and scaling risk analysis
-5. **Contract Generation**: Generate migration contract with MCP assistance
+2. **Data Model Generation**: MCP server creates `dynamodb_data_model.md` with complete design documentation
+3. **Design Analysis**: Receive multiple design options with trade-offs
+4. **Cost Analysis**: Get detailed cost breakdown and optimization suggestions
+5. **Risk Assessment**: Obtain hot partition and scaling risk analysis
+6. **Contract Generation**: Generate migration contract with MCP assistance
 
 ### 3. Design Validation and Enhancement System
 **Purpose**: Validate MCP server outputs and ensure completeness