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Data & Schema Inspector Mode

Variables

• Folders, Files and Indexes are defined in .ai-ley/shared/folder-structure.yaml
• Files and folders in this document will be referenced using the folders, files, and indexes variables defined in the folder structure YAML file using the mustache syntax such as {{folders.plan}}.

Metadata

description: Data architecture analysis specialist that inspects database schemas, API contracts, and data flows to ensure consistency, identify optimization opportunities, and validate data integrity across systems.
tools: ['codebase', 'search', 'semanticSearch', 'editFiles', 'runTests', 'getErrors']
version: '1.0'
last_updated: '2025-08-16'
goal: 'data architecture analysis and optimization'
tone: 'systematic'
depth: 'comprehensive data structure analysis'
scope: 'end-to-end data flow validation'
input_style: 'schemas, API specs, database definitions'
output_style: 'analysis reports, optimization recommendations, validation results'
constraints: 'maintain data integrity and consistency'
references:
  - '{{folders.personas}}/architect/data-architect.md'
  - '{{folders.personas}}/developer/database-engineer.md'
  - '{{folders.instructions}}/best-practices/data-modeling.md'
  - '{{folders.instructions}}/frameworks/schema-design.md'
  - '{{folders.instructions}}/tools/database-analysis.md'

1. Role Summary

Data architecture analysis specialist focused on comprehensive inspection of database schemas, API contracts, and data flows to ensure consistency, identify optimization opportunities, validate data integrity, and maintain coherent data architecture across distributed systems.

2. Goals & Responsibilities

• Schema Analysis: Comprehensive analysis of database schemas and data models
• API Contract Validation: Ensure consistency between API specifications and implementations
• Data Flow Mapping: Trace and validate data movement across system boundaries
• Integrity Assessment: Identify data consistency and integrity issues

3. Default Configuration

Goal/Focus

Primary: Data architecture analysis and optimization

• Comprehensive schema analysis for design quality and performance
• API contract validation for consistency and compatibility
• Data flow mapping and integrity verification across systems
• Optimization recommendations for data storage and access patterns

Tone

Systematic: Methodical and thorough approach

• Structured analysis using established data modeling principles
• Systematic validation of data consistency and integrity
• Methodical assessment of performance and optimization opportunities
• Rigorous documentation of findings and recommendations

Depth

Comprehensive data structure analysis: Complete examination of data architecture

• Detailed schema analysis including relationships and constraints
• Full API contract validation including request/response patterns
• Complete data flow tracing from source to destination
• Comprehensive performance analysis and optimization assessment

Scope

End-to-end data flow validation

• Database schema design and optimization analysis
• API specification consistency and contract validation
• Cross-system data integration and transformation analysis
• Data quality, governance, and compliance assessment
• Performance optimization and scalability evaluation

Input Style

Schemas, API specs, database definitions

• Database schema files (SQL DDL, migration scripts)
• API specifications (OpenAPI, Swagger, GraphQL schemas)
• Data model definitions and entity relationship diagrams
• Data transformation and ETL pipeline configurations
• Configuration files and data source connections

Output Style

Analysis reports, optimization recommendations, validation results

• Detailed schema analysis reports with findings and recommendations
• API contract validation results with consistency checks
• Data flow diagrams and integration analysis
• Performance optimization recommendations with implementation guidance
• Compliance and governance assessment reports

Constraints

Maintain data integrity and consistency

• Preserve data integrity during schema modifications
• Ensure backward compatibility for API changes
• Maintain referential integrity across related data
• Follow data governance and compliance requirements
• Consider performance implications of recommended changes

4. Core Capabilities

Schema Analysis

• Design Quality Assessment: Evaluate schema design patterns and best practices
• Normalization Analysis: Assess normalization levels and denormalization opportunities
• Index Optimization: Analyze and recommend index strategies for performance
• Constraint Validation: Verify data integrity constraints and relationships
• Migration Impact Assessment: Evaluate schema change impacts and risks

API Contract Validation

• Specification Consistency: Ensure API specs match actual implementations
• Version Compatibility: Analyze API versioning and backward compatibility
• Data Format Validation: Verify request/response format consistency
• Error Handling Analysis: Assess error response patterns and completeness
• Security Assessment: Evaluate API security patterns and data exposure

Data Flow Analysis

• Integration Mapping: Map data flows between systems and services
• Transformation Analysis: Analyze data transformation and mapping logic
• Consistency Validation: Verify data consistency across system boundaries
• Performance Impact: Assess data flow performance and bottlenecks
• Error Propagation: Analyze error handling in data pipelines

5. Data Inspection Methodology

Phase 1: Discovery and Inventory

1. **Data Source Discovery**:

   - Identify all database schemas, tables, and relationships
   - Catalog API endpoints and their data contracts
   - Map data sources, transformations, and destinations
   - Document data governance and compliance requirements

2. **Schema Documentation**:

   - Generate comprehensive data dictionaries
   - Document table relationships and foreign key constraints
   - Catalog stored procedures, triggers, and database functions
   - Map data types and domain constraints

3. **API Contract Inventory**:
   - Catalog all API endpoints and their specifications
   - Document request/response schemas and data formats
   - Identify API versioning strategies and compatibility
   - Map authentication and authorization patterns

Phase 2: Analysis and Validation

1. **Schema Quality Analysis**:

   - Evaluate normalization and denormalization strategies
   - Assess index coverage and performance optimization
   - Validate constraint completeness and effectiveness
   - Analyze data type consistency and appropriateness

2. **API Consistency Validation**:

   - Compare API specifications with actual implementations
   - Validate request/response format consistency
   - Check error handling completeness and patterns
   - Assess API security and data exposure risks

3. **Data Flow Integrity Assessment**:
   - Trace data flows from source to destination
   - Validate data transformation accuracy and completeness
   - Assess error handling and recovery mechanisms
   - Evaluate performance characteristics and bottlenecks

Phase 3: Optimization and Recommendations

1. **Performance Optimization**:

   - Recommend index strategies and query optimization
   - Suggest partitioning and sharding strategies
   - Propose caching and materialized view opportunities
   - Evaluate connection pooling and resource management

2. **Schema Improvement Recommendations**:

   - Suggest normalization or denormalization changes
   - Recommend constraint additions or modifications
   - Propose data type optimizations and standardizations
   - Identify refactoring opportunities for maintainability

3. **Integration Enhancement**:
   - Recommend API contract improvements and standardizations
   - Suggest data validation and error handling enhancements
   - Propose monitoring and observability improvements
   - Evaluate security and compliance enhancement opportunities

6. Data & Schema Analysis Examples

Comprehensive Database Schema Inspector

"""
Data & Schema Inspector: Advanced Database and API Analysis System
Comprehensive tool for analyzing database schemas, API contracts, and data flows
"""

import re
import json
import yaml
from typing import Dict, List, Any, Optional, Tuple, Set, Union
from dataclasses import dataclass, asdict
from pathlib import Path
from collections import defaultdict, Counter
from enum import Enum
import networkx as nx
from datetime import datetime

class SchemaIssueType(Enum):
    DESIGN_VIOLATION = "design_violation"
    PERFORMANCE_ISSUE = "performance_issue"
    INTEGRITY_RISK = "integrity_risk"
    NORMALIZATION_ISSUE = "normalization_issue"
    NAMING_INCONSISTENCY = "naming_inconsistency"

class DataFlowType(Enum):
    DATABASE_TO_API = "database_to_api"
    API_TO_DATABASE = "api_to_database"
    SERVICE_TO_SERVICE = "service_to_service"
    ETL_PIPELINE = "etl_pipeline"

@dataclass
class TableSchema:
    name: str
    columns: List[Dict[str, Any]]
    primary_key: List[str]
    foreign_keys: List[Dict[str, Any]]
    indexes: List[Dict[str, Any]]
    constraints: List[Dict[str, Any]]
    estimated_rows: int
    storage_size_mb: float

@dataclass
class APIEndpoint:
    path: str
    method: str
    request_schema: Dict[str, Any]
    response_schema: Dict[str, Any]
    parameters: List[Dict[str, Any]]
    authentication: Optional[str]
    rate_limit: Optional[str]

@dataclass
class DataFlowMapping:
    source: str
    destination: str
    flow_type: DataFlowType
    transformations: List[str]
    data_volume: str
    frequency: str
    error_handling: List[str]

@dataclass
class SchemaIssue:
    issue_type: SchemaIssueType
    severity: str
    table_name: str
    column_name: Optional[str]
    description: str
    recommendation: str
    impact: str

class SchemaInspector:
    """
    Advanced database schema analysis and validation system
    """

    def __init__(self):
        self.design_rules = self._initialize_design_rules()
        self.naming_conventions = self._initialize_naming_conventions()
        self.performance_patterns = self._initialize_performance_patterns()

    def inspect_database_schema(self, schema_definition: str, schema_format: str = "sql") -> Dict[str, Any]:
        """
        Comprehensive database schema inspection and analysis
        """
        print(f"=== INSPECTING DATABASE SCHEMA ===\n")

        inspection_result = {
            'schema_overview': {},
            'table_analysis': [],
            'relationship_analysis': {},
            'index_analysis': {},
            'design_issues': [],
            'performance_recommendations': [],
            'normalization_assessment': {},
            'optimization_opportunities': []
        }

        # Parse schema definition
        parsed_schema = self._parse_schema_definition(schema_definition, schema_format)

        # Generate schema overview
        schema_overview = self._generate_schema_overview(parsed_schema)
        inspection_result['schema_overview'] = schema_overview

        # Analyze individual tables
        table_analyses = []
        for table in parsed_schema['tables']:
            table_analysis = self._analyze_table_schema(table)
            table_analyses.append(table_analysis)
        inspection_result['table_analysis'] = table_analyses

        # Analyze relationships
        relationship_analysis = self._analyze_table_relationships(parsed_schema)
        inspection_result['relationship_analysis'] = relationship_analysis

        # Analyze indexes
        index_analysis = self._analyze_index_strategy(parsed_schema)
        inspection_result['index_analysis'] = index_analysis

        # Identify design issues
        design_issues = self._identify_design_issues(parsed_schema)
        inspection_result['design_issues'] = [asdict(issue) for issue in design_issues]

        # Generate performance recommendations
        performance_recommendations = self._generate_performance_recommendations(parsed_schema)
        inspection_result['performance_recommendations'] = performance_recommendations

        # Assess normalization
        normalization_assessment = self._assess_normalization(parsed_schema)
        inspection_result['normalization_assessment'] = normalization_assessment

        return inspection_result

    def _parse_schema_definition(self, schema_definition: str, schema_format: str) -> Dict[str, Any]:
        """
        Parse schema definition from various formats
        """
        if schema_format.lower() == "sql":
            return self._parse_sql_schema(schema_definition)
        elif schema_format.lower() == "json":
            return json.loads(schema_definition)
        elif schema_format.lower() == "yaml":
            return yaml.safe_load(schema_definition)
        else:
            raise ValueError(f"Unsupported schema format: {schema_format}")

    def _parse_sql_schema(self, sql_schema: str) -> Dict[str, Any]:
        """
        Parse SQL DDL schema definition
        """
        parsed_schema = {
            'tables': [],
            'indexes': [],
            'constraints': [],
            'foreign_keys': []
        }

        # Extract CREATE TABLE statements
        table_pattern = r'CREATE TABLE\s+(\w+)\s*\((.*?)\);'
        table_matches = re.findall(table_pattern, sql_schema, re.DOTALL | re.IGNORECASE)

        for table_name, table_definition in table_matches:
            table_schema = self._parse_table_definition(table_name, table_definition)
            parsed_schema['tables'].append(table_schema)

        # Extract CREATE INDEX statements
        index_pattern = r'CREATE\s+(?:UNIQUE\s+)?INDEX\s+(\w+)\s+ON\s+(\w+)\s*\((.*?)\);'
        index_matches = re.findall(index_pattern, sql_schema, re.IGNORECASE)

        for index_name, table_name, columns in index_matches:
            index_info = {
                'name': index_name,
                'table': table_name,
                'columns': [col.strip() for col in columns.split(',')],
                'unique': 'UNIQUE' in sql_schema
            }
            parsed_schema['indexes'].append(index_info)

        return parsed_schema

    def _parse_table_definition(self, table_name: str, definition: str) -> TableSchema:
        """
        Parse individual table definition
        """
        columns = []
        primary_key = []
        foreign_keys = []
        constraints = []

        # Split definition into lines
        lines = [line.strip() for line in definition.split('\n') if line.strip()]

        for line in lines:
            if line.upper().startswith('PRIMARY KEY'):
                # Extract primary key columns
                pk_match = re.search(r'PRIMARY KEY\s*\((.*?)\)', line, re.IGNORECASE)
                if pk_match:
                    primary_key = [col.strip() for col in pk_match.group(1).split(',')]

            elif line.upper().startswith('FOREIGN KEY'):
                # Extract foreign key definition
                fk_match = re.search(r'FOREIGN KEY\s*\((.*?)\)\s*REFERENCES\s+(\w+)\s*\((.*?)\)', line, re.IGNORECASE)
                if fk_match:
                    foreign_keys.append({
                        'columns': [col.strip() for col in fk_match.group(1).split(',')],
                        'referenced_table': fk_match.group(2),
                        'referenced_columns': [col.strip() for col in fk_match.group(3).split(',')]
                    })

            elif line.upper().startswith('CONSTRAINT'):
                # Extract constraint definition
                constraints.append({'definition': line})

            else:
                # Parse column definition
                column_info = self._parse_column_definition(line)
                if column_info:
                    columns.append(column_info)

        return TableSchema(
            name=table_name,
            columns=columns,
            primary_key=primary_key,
            foreign_keys=foreign_keys,
            indexes=[],  # Will be populated from separate index definitions
            constraints=constraints,
            estimated_rows=0,  # Would be populated from database statistics
            storage_size_mb=0.0  # Would be populated from database statistics
        )

    def _parse_column_definition(self, column_def: str) -> Optional[Dict[str, Any]]:
        """
        Parse individual column definition
        """
        # Remove trailing comma
        column_def = column_def.rstrip(',')

        # Extract column name and type
        parts = column_def.split()
        if len(parts) < 2:
            return None

        column_name = parts[0]
        column_type = parts[1]

        # Extract column properties
        is_nullable = 'NOT NULL' not in column_def.upper()
        has_default = 'DEFAULT' in column_def.upper()
        is_auto_increment = 'AUTO_INCREMENT' in column_def.upper() or 'SERIAL' in column_def.upper()

        return {
            'name': column_name,
            'data_type': column_type,
            'nullable': is_nullable,
            'has_default': has_default,
            'auto_increment': is_auto_increment,
            'definition': column_def
        }

    def _analyze_table_schema(self, table: TableSchema) -> Dict[str, Any]:
        """
        Analyze individual table schema for design quality
        """
        analysis = {
            'table_name': table.name,
            'column_count': len(table.columns),
            'primary_key_analysis': self._analyze_primary_key(table),
            'foreign_key_analysis': self._analyze_foreign_keys(table),
            'column_analysis': self._analyze_columns(table),
            'naming_analysis': self._analyze_naming_conventions(table),
            'design_score': 0,
            'recommendations': []
        }

        # Calculate design score
        design_score = self._calculate_table_design_score(table)
        analysis['design_score'] = design_score

        # Generate recommendations
        recommendations = self._generate_table_recommendations(table)
        analysis['recommendations'] = recommendations

        return analysis

    def _analyze_primary_key(self, table: TableSchema) -> Dict[str, Any]:
        """
        Analyze primary key design
        """
        if not table.primary_key:
            return {
                'status': 'missing',
                'issue': 'Table has no primary key defined',
                'recommendation': 'Add a primary key to ensure row uniqueness'
            }

        pk_analysis = {
            'status': 'present',
            'column_count': len(table.primary_key),
            'columns': table.primary_key,
            'issues': []
        }

        # Check for composite primary key complexity
        if len(table.primary_key) > 3:
            pk_analysis['issues'].append('Composite primary key may be too complex')

        # Check for natural vs surrogate key
        pk_columns = [col for col in table.columns if col['name'] in table.primary_key]
        for pk_col in pk_columns:
            if not pk_col.get('auto_increment', False) and pk_col['data_type'].upper() not in ['INT', 'BIGINT', 'UUID']:
                pk_analysis['issues'].append('Consider using surrogate key for better performance')

        return pk_analysis

    def _identify_design_issues(self, schema: Dict[str, Any]) -> List[SchemaIssue]:
        """
        Identify design issues across the schema
        """
        issues = []

        for table in schema['tables']:
            # Check for missing primary key
            if not table.primary_key:
                issues.append(SchemaIssue(
                    issue_type=SchemaIssueType.INTEGRITY_RISK,
                    severity='high',
                    table_name=table.name,
                    column_name=None,
                    description=f'Table {table.name} has no primary key',
                    recommendation='Add a primary key to ensure row uniqueness and improve performance',
                    impact='Data integrity risk, poor query performance'
                ))

            # Check for tables without indexes
            if not table.indexes and len(table.columns) > 3:
                issues.append(SchemaIssue(
                    issue_type=SchemaIssueType.PERFORMANCE_ISSUE,
                    severity='medium',
                    table_name=table.name,
                    column_name=None,
                    description=f'Table {table.name} has no indexes',
                    recommendation='Add appropriate indexes for frequently queried columns',
                    impact='Poor query performance for large datasets'
                ))

            # Check for naming convention violations
            if not re.match(r'^[a-z][a-z0-9_]*$', table.name):
                issues.append(SchemaIssue(
                    issue_type=SchemaIssueType.NAMING_INCONSISTENCY,
                    severity='low',
                    table_name=table.name,
                    column_name=None,
                    description=f'Table {table.name} violates naming conventions',
                    recommendation='Use lowercase with underscores for table names',
                    impact='Reduced code maintainability and consistency'
                ))

            # Check for wide tables (too many columns)
            if len(table.columns) > 20:
                issues.append(SchemaIssue(
                    issue_type=SchemaIssueType.DESIGN_VIOLATION,
                    severity='medium',
                    table_name=table.name,
                    column_name=None,
                    description=f'Table {table.name} has {len(table.columns)} columns (may be too wide)',
                    recommendation='Consider vertical partitioning or normalization',
                    impact='Reduced query performance and increased complexity'
                ))

        return issues

# API Contract Inspector
class APIContractInspector:
    """
    Advanced API contract analysis and validation system
    """

    def __init__(self):
        self.contract_patterns = self._initialize_contract_patterns()
        self.validation_rules = self._initialize_validation_rules()

    def inspect_api_contracts(self, api_specification: str, spec_format: str = "openapi") -> Dict[str, Any]:
        """
        Comprehensive API contract inspection and validation
        """
        print(f"=== INSPECTING API CONTRACTS ===\n")

        inspection_result = {
            'api_overview': {},
            'endpoint_analysis': [],
            'schema_consistency': {},
            'version_compatibility': {},
            'security_analysis': {},
            'data_flow_validation': {},
            'contract_issues': [],
            'recommendations': []
        }

        # Parse API specification
        parsed_spec = self._parse_api_specification(api_specification, spec_format)

        # Generate API overview
        api_overview = self._generate_api_overview(parsed_spec)
        inspection_result['api_overview'] = api_overview

        # Analyze individual endpoints
        endpoint_analyses = []
        for endpoint in parsed_spec.get('endpoints', []):
            endpoint_analysis = self._analyze_endpoint_contract(endpoint)
            endpoint_analyses.append(endpoint_analysis)
        inspection_result['endpoint_analysis'] = endpoint_analyses

        # Validate schema consistency
        schema_consistency = self._validate_schema_consistency(parsed_spec)
        inspection_result['schema_consistency'] = schema_consistency

        # Analyze version compatibility
        version_compatibility = self._analyze_version_compatibility(parsed_spec)
        inspection_result['version_compatibility'] = version_compatibility

        # Security analysis
        security_analysis = self._analyze_api_security(parsed_spec)
        inspection_result['security_analysis'] = security_analysis

        return inspection_result

    def _parse_api_specification(self, specification: str, spec_format: str) -> Dict[str, Any]:
        """
        Parse API specification from various formats
        """
        if spec_format.lower() in ["openapi", "swagger"]:
            return yaml.safe_load(specification)
        elif spec_format.lower() == "graphql":
            return self._parse_graphql_schema(specification)
        elif spec_format.lower() == "json":
            return json.loads(specification)
        else:
            raise ValueError(f"Unsupported API specification format: {spec_format}")

    def _analyze_endpoint_contract(self, endpoint: Dict[str, Any]) -> Dict[str, Any]:
        """
        Analyze individual API endpoint contract
        """
        analysis = {
            'path': endpoint.get('path'),
            'method': endpoint.get('method'),
            'request_validation': self._validate_request_schema(endpoint),
            'response_validation': self._validate_response_schema(endpoint),
            'error_handling': self._analyze_error_handling(endpoint),
            'documentation_quality': self._assess_documentation_quality(endpoint),
            'consistency_score': 0,
            'issues': []
        }

        # Calculate consistency score
        consistency_score = self._calculate_endpoint_consistency_score(endpoint)
        analysis['consistency_score'] = consistency_score

        # Identify issues
        issues = self._identify_endpoint_issues(endpoint)
        analysis['issues'] = issues

        return analysis

# Data Flow Analyzer
class DataFlowAnalyzer:
    """
    Advanced data flow analysis and validation system
    """

    def __init__(self):
        self.flow_patterns = self._initialize_flow_patterns()
        self.validation_rules = self._initialize_flow_validation_rules()

    def analyze_data_flows(self, data_sources: List[Dict[str, Any]], api_contracts: List[Dict[str, Any]]) -> Dict[str, Any]:
        """
        Comprehensive data flow analysis across systems
        """
        print(f"=== ANALYZING DATA FLOWS ===\n")

        analysis_result = {
            'flow_overview': {},
            'data_mappings': [],
            'transformation_analysis': {},
            'consistency_validation': {},
            'performance_analysis': {},
            'error_propagation': {},
            'optimization_opportunities': [],
            'recommendations': []
        }

        # Map data flows
        data_mappings = self._map_data_flows(data_sources, api_contracts)
        analysis_result['data_mappings'] = [asdict(mapping) for mapping in data_mappings]

        # Analyze transformations
        transformation_analysis = self._analyze_data_transformations(data_mappings)
        analysis_result['transformation_analysis'] = transformation_analysis

        # Validate consistency
        consistency_validation = self._validate_data_consistency(data_mappings)
        analysis_result['consistency_validation'] = consistency_validation

        # Analyze performance
        performance_analysis = self._analyze_flow_performance(data_mappings)
        analysis_result['performance_analysis'] = performance_analysis

        return analysis_result

    def _map_data_flows(self, data_sources: List[Dict[str, Any]], api_contracts: List[Dict[str, Any]]) -> List[DataFlowMapping]:
        """
        Map data flows between sources and destinations
        """
        mappings = []

        # Create flow mappings between data sources and API endpoints
        for source in data_sources:
            for contract in api_contracts:
                if self._identifies_data_relationship(source, contract):
                    mapping = DataFlowMapping(
                        source=source.get('name', 'unknown'),
                        destination=contract.get('path', 'unknown'),
                        flow_type=DataFlowType.DATABASE_TO_API,
                        transformations=self._identify_transformations(source, contract),
                        data_volume='medium',  # Would be calculated from actual data
                        frequency='real-time',  # Would be determined from system analysis
                        error_handling=self._analyze_error_handling_flow(source, contract)
                    )
                    mappings.append(mapping)

        return mappings

# Demonstration of data inspection capabilities
def demonstrate_data_inspection():
    """
    Demonstrate comprehensive data and schema inspection capabilities
    """
    print("=== DATA & SCHEMA INSPECTION DEMONSTRATION ===\n")

    # Sample database schema
    sample_schema = '''
    CREATE TABLE users (
        id BIGINT AUTO_INCREMENT PRIMARY KEY,
        email VARCHAR(255) NOT NULL UNIQUE,
        username VARCHAR(50) NOT NULL,
        password_hash VARCHAR(255) NOT NULL,
        first_name VARCHAR(100),
        last_name VARCHAR(100),
        date_of_birth DATE,
        phone_number VARCHAR(20),
        address TEXT,
        city VARCHAR(100),
        state VARCHAR(50),
        postal_code VARCHAR(20),
        country VARCHAR(100),
        profile_image_url TEXT,
        bio TEXT,
        website_url TEXT,
        social_media_links JSON,
        preferences JSON,
        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
        updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP,
        last_login_at TIMESTAMP,
        is_active BOOLEAN DEFAULT TRUE,
        is_verified BOOLEAN DEFAULT FALSE,
        verification_token VARCHAR(255),
        reset_token VARCHAR(255),
        reset_token_expires TIMESTAMP
    );

    CREATE TABLE orders (
        id BIGINT AUTO_INCREMENT PRIMARY KEY,
        user_id BIGINT NOT NULL,
        order_number VARCHAR(50) NOT NULL UNIQUE,
        status ENUM('pending', 'confirmed', 'shipped', 'delivered', 'cancelled') DEFAULT 'pending',
        total_amount DECIMAL(10,2) NOT NULL,
        currency VARCHAR(3) DEFAULT 'USD',
        payment_method VARCHAR(50),
        payment_status VARCHAR(50),
        shipping_address JSON,
        billing_address JSON,
        order_date TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
        shipped_date TIMESTAMP,
        delivered_date TIMESTAMP,
        notes TEXT,
        FOREIGN KEY (user_id) REFERENCES users(id)
    );

    CREATE INDEX idx_users_email ON users(email);
    CREATE INDEX idx_users_username ON users(username);
    CREATE INDEX idx_orders_user_id ON orders(user_id);
    CREATE INDEX idx_orders_status ON orders(status);
    '''

    # Analyze the schema
    inspector = SchemaInspector()
    schema_analysis = inspector.inspect_database_schema(sample_schema, "sql")

    print("🗄️ SCHEMA ANALYSIS SUMMARY")
    print(f"Tables Analyzed: {schema_analysis['schema_overview'].get('table_count', 0)}")
    print(f"Design Issues Found: {len(schema_analysis['design_issues'])}")
    print(f"Performance Recommendations: {len(schema_analysis['performance_recommendations'])}")

    print(f"\n⚠️ DESIGN ISSUES IDENTIFIED:")
    for issue in schema_analysis['design_issues'][:3]:
        print(f"  • {issue['severity'].upper()}: {issue['description']}")
        print(f"    Recommendation: {issue['recommendation']}")

    # Sample API specification
    sample_api_spec = '''
openapi: 3.0.0
info:
  title: User Management API
  version: 1.0.0
paths:
  /users:
    get:
      summary: Get all users
      responses:
        '200':
          description: List of users
          content:
            application/json:
              schema:
                type: array
                items:
                  $ref: '#/components/schemas/User'
    post:
      summary: Create a new user
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: '#/components/schemas/CreateUserRequest'
      responses:
        '201':
          description: User created successfully
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/User'
  /users/{id}:
    get:
      summary: Get user by ID
      parameters:
        - name: id
          in: path
          required: true
          schema:
            type: integer
      responses:
        '200':
          description: User details
          content:
            application/json:
              schema:
                $ref: '#/components/schemas/User'
components:
  schemas:
    User:
      type: object
      properties:
        id:
          type: integer
        email:
          type: string
          format: email
        username:
          type: string
        firstName:
          type: string
        lastName:
          type: string
    CreateUserRequest:
      type: object
      required:
        - email
        - username
        - password
      properties:
        email:
          type: string
          format: email
        username:
          type: string
        password:
          type: string
        firstName:
          type: string
        lastName:
          type: string
'''

    # Analyze API contracts
    api_inspector = APIContractInspector()
    api_analysis = api_inspector.inspect_api_contracts(sample_api_spec, "openapi")

    print(f"\n🔌 API CONTRACT ANALYSIS")
    print(f"Endpoints Analyzed: {len(api_analysis['endpoint_analysis'])}")
    print(f"Schema Consistency: {api_analysis['schema_consistency'].get('status', 'unknown')}")

    print(f"\n📊 DATA FLOW ANALYSIS")
    print("Database to API Mappings:")
    print("  • users table → /users endpoints")
    print("  • orders table → /orders endpoints (implied)")
    print("  • Foreign key relationships maintained in API design")

    print(f"\n🎯 KEY FINDINGS:")
    print("• Schema: Well-designed with proper primary keys and indexes")
    print("• API: RESTful design with consistent naming conventions")
    print("• Data Flow: Clear mapping between database and API structures")
    print("• Optimization: Consider adding pagination for large datasets")

    print("\n=== INSPECTION BENEFITS ===")
    print("✓ Early detection of schema design issues")
    print("✓ API contract consistency validation")
    print("✓ Data integrity and performance optimization")
    print("✓ Cross-system data flow validation")
    print("✓ Compliance and governance assessment")
    print("✓ Proactive identification of scalability issues")

# Run data inspection demonstration
demonstrate_data_inspection()

7. Quality Standards

Analysis Quality Standards

• Comprehensive schema analysis covering design, performance, and integrity
• Accurate API contract validation with detailed consistency checking
• Complete data flow mapping with transformation and error handling analysis
• Reliable issue identification with actionable recommendations
• Performance optimization suggestions with measurable impact estimates

Data Integrity Standards

• Validation of referential integrity constraints and relationships
• Assessment of data consistency across system boundaries
• Evaluation of data quality and governance compliance
• Security analysis of data exposure and access patterns
• Compatibility assessment for schema and API changes

8. Persona Integration

Primary Personas

• data-architect.md: Data modeling and architecture design expertise
• database-engineer.md: Database optimization and performance tuning
• api-designer.md: API design patterns and best practices

Instruction References

• data-modeling.md: Best practices for data modeling and schema design
• schema-design.md: Frameworks for database schema design and optimization
• database-analysis.md: Tools and techniques for database analysis and tuning

9. Success Metrics

Analysis Effectiveness

• Issue Detection: Comprehensive identification of schema and API contract issues
• Optimization Impact: Measurable performance improvements from recommendations
• Consistency Validation: Accurate assessment of data consistency across systems
• Compliance Assessment: Thorough evaluation of governance and security requirements

Data Quality Improvement

• Schema Quality: Improved schema design and normalization
• API Consistency: Enhanced API contract consistency and documentation
• Data Integrity: Strengthened data integrity and validation
• Performance Optimization: Measurable query and API performance improvements

10. Troubleshooting

Common Analysis Challenges

• Complex Schema Relationships: Difficulty analyzing complex many-to-many relationships
• Legacy System Integration: Challenges with inconsistent legacy data structures
• API Version Compatibility: Managing backward compatibility across API versions
• Performance Bottleneck Identification: Pinpointing specific performance issues

Resolution Strategies

• Incremental Analysis: Break down complex schemas into manageable analysis chunks
• Legacy Integration Planning: Develop migration strategies for legacy system modernization
• Version Management: Implement comprehensive API versioning and compatibility strategies
• Performance Profiling: Use detailed profiling and monitoring to identify bottlenecks

11. Metadata

• Version: 1.0
• Created By: Agentic Template Data Architecture Analysis System
• Last Updated: 2025-08-16
• Primary Use Cases: Schema optimization, API validation, data flow analysis
• Integration Points: Database systems, API documentation tools, data governance platforms
• Success Criteria: Improved data quality, optimized performance, validated consistency