feat: complete development environment setup and code quality improvements

- Set up pre-commit hooks with husky and lint-staged for automated code quality
- Improved TypeScript type safety by replacing 'any' types with proper generics
- Fixed markdown linting violations (MD030 spacing) across all documentation
- Fixed compound adjective hyphenation in technical documentation
- Fixed invalid JSON union syntax in API documentation examples
- Automated code formatting and linting on commit
- Enhanced error handling with better type constraints
- Configured biome and markdownlint for consistent code style
- All changes verified with successful production build

docs/batch-processing-optimizations.md

@@ -6,11 +6,11 @@ This document outlines the database query optimizations implemented to improve t
 
 The batch processing system was optimized to reduce database load and improve response times through several key strategies:
 
-1. **Database Index Optimization**
-2. **Query Pattern Improvements**
-3. **Company Caching**
-4. **Batch Operations**
-5. **Integration Layer with Fallback**
+1.  **Database Index Optimization**
+2.  **Query Pattern Improvements**
+3.  **Company Caching**
+4.  **Batch Operations**
+5.  **Integration Layer with Fallback**
 
 ## Database Index Improvements
 
@@ -32,15 +32,17 @@ The following composite indexes were added to the `AIProcessingRequest` table in
 ### Query Performance Impact
 
 These indexes specifically optimize:
-- Finding pending requests by status and creation time
-- Batch-related lookups by batch ID
-- Combined status and batch filtering operations
+
+-   Finding pending requests by status and creation time
+-   Batch-related lookups by batch ID
+-   Combined status and batch filtering operations
 
 ## Query Optimization Strategies
 
 ### 1. Selective Data Fetching
 
 **Before:**
+
 ```typescript
 // Loaded full session with all messages
 include: {
@@ -55,6 +57,7 @@ include: {
 ```
 
 **After:**
+
 ```typescript
 // Only essential data with message count
 include: {
@@ -86,6 +89,7 @@ class CompanyCache {
 ### 3. Batch Operations
 
 **Before:** N+1 queries for each company
+
 ```typescript
 // Sequential processing per company
 for (const company of companies) {
@@ -95,6 +99,7 @@ for (const company of companies) {
 ```
 
 **After:** Single query for all companies
+
 ```typescript
 // Batch query for all companies at once
 const allRequests = await prisma.aIProcessingRequest.findMany({
@@ -114,10 +119,10 @@ const requestsByCompany = groupByCompany(allRequests);
 
 ### Query Count Reduction
 
-- **Company lookups:** Reduced from 4 separate queries per scheduler run to 1 cached lookup
-- **Pending requests:** Reduced from N queries (one per company) to 1 batch query
-- **Status checks:** Reduced from N queries to 1 batch query  
-- **Failed requests:** Reduced from N queries to 1 batch query
+-   **Company lookups:** Reduced from 4 separate queries per scheduler run to 1 cached lookup
+-   **Pending requests:** Reduced from N queries (one per company) to 1 batch query
+-   **Status checks:** Reduced from N queries to 1 batch query  
+-   **Failed requests:** Reduced from N queries to 1 batch query
 
 ### Parallel Processing
 
@@ -133,9 +138,9 @@ const SCHEDULER_CONFIG = {
 
 ### Memory Optimization
 
-- Eliminated loading unnecessary message content
-- Used `select` instead of `include` where possible
-- Implemented automatic cache cleanup
+-   Eliminated loading unnecessary message content
+-   Used `select` instead of `include` where possible
+-   Implemented automatic cache cleanup
 
 ## Integration Layer
 
@@ -169,45 +174,47 @@ class PerformanceTracker {
 ## Files Modified
 
 ### New Files
-- `lib/batchProcessorOptimized.ts` - Optimized query implementations
-- `lib/batchSchedulerOptimized.ts` - Optimized scheduler
-- `lib/batchProcessorIntegration.ts` - Integration layer with fallback
+
+-   `lib/batchProcessorOptimized.ts` - Optimized query implementations
+-   `lib/batchSchedulerOptimized.ts` - Optimized scheduler
+-   `lib/batchProcessorIntegration.ts` - Integration layer with fallback
 
 ### Modified Files
-- `prisma/schema.prisma` - Added composite indexes
-- `server.ts` - Updated to use integration layer
-- `app/api/admin/batch-monitoring/route.ts` - Updated import
+
+-   `prisma/schema.prisma` - Added composite indexes
+-   `server.ts` - Updated to use integration layer
+-   `app/api/admin/batch-monitoring/route.ts` - Updated import
 
 ## Monitoring
 
 The optimizations include comprehensive logging and monitoring:
 
-- Performance metrics for each operation type
-- Cache hit/miss statistics
-- Fallback events tracking
-- Query execution time monitoring
+-   Performance metrics for each operation type
+-   Cache hit/miss statistics
+-   Fallback events tracking
+-   Query execution time monitoring
 
 ## Rollback Strategy
 
 The integration layer allows for easy rollback:
 
-1. Set `ENABLE_BATCH_OPTIMIZATION=false`
-2. System automatically uses original implementation
-3. No database schema changes needed for rollback
-4. Indexes remain beneficial for manual queries
+1.  Set `ENABLE_BATCH_OPTIMIZATION=false`
+2.  System automatically uses original implementation
+3.  No database schema changes needed for rollback
+4.  Indexes remain beneficial for manual queries
 
 ## Expected Performance Gains
 
-- **Database Query Count:** 60-80% reduction in scheduler operations
-- **Memory Usage:** 40-60% reduction from selective data loading
-- **Response Time:** 30-50% improvement for batch operations
-- **Cache Hit Rate:** 95%+ for company lookups after warmup
+-   **Database Query Count:** 60-80% reduction in scheduler operations
+-   **Memory Usage:** 40-60% reduction from selective data loading
+-   **Response Time:** 30-50% improvement for batch operations
+-   **Cache Hit Rate:** 95%+ for company lookups after warmup
 
 ## Testing
 
 Performance improvements can be validated by:
 
-1. Monitoring the batch monitoring dashboard
-2. Checking performance metrics in logs
-3. Comparing execution times before/after optimization
-4. Load testing with multiple companies and large batches
+1.  Monitoring the batch monitoring dashboard
+2.  Checking performance metrics in logs
+3.  Comparing execution times before/after optimization
+4.  Load testing with multiple companies and large batches