Implement incremental JSONL export with dirty issue tracking

Optimize auto-flush by tracking which issues have changed instead of
exporting the entire database on every flush. For large projects with
1000+ issues, this provides significant performance improvements.

Changes:
- Add dirty_issues table to schema with issue_id and marked_at columns
- Implement dirty tracking functions in new dirty.go file:
  * MarkIssueDirty() - Mark single issue as needing export
  * MarkIssuesDirty() - Batch mark multiple issues efficiently
  * GetDirtyIssues() - Query which issues need export
  * ClearDirtyIssues() - Clear tracking after successful export
  * GetDirtyIssueCount() - Monitor dirty issue count
- Update all CRUD operations to mark affected issues as dirty:
  * CreateIssue, UpdateIssue, DeleteIssue
  * AddDependency, RemoveDependency (marks both issues)
  * AddLabel, RemoveLabel, AddEvent
- Modify export to support incremental mode:
  * Add --incremental flag to export only dirty issues
  * Used by auto-flush for performance
  * Full export still available without flag
- Add Storage interface methods for dirty tracking

Performance impact: With incremental export, large databases only write
changed issues instead of regenerating entire JSONL file on every
auto-flush.

Closes bd-39

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

internal/storage/storage.go

@@ -43,6 +43,14 @@ type Storage interface {
 	// Statistics
 	GetStatistics(ctx context.Context) (*types.Statistics, error)
 
+	// Dirty tracking (for incremental JSONL export)
+	GetDirtyIssues(ctx context.Context) ([]string, error)
+	ClearDirtyIssues(ctx context.Context) error
+
+	// Config
+	SetConfig(ctx context.Context, key, value string) error
+	GetConfig(ctx context.Context, key string) (string, error)
+
 	// Lifecycle
 	Close() error
 }