diff --git a/local-app/python-tools/cross-organization/assess_check_s3_buckets.py b/local-app/python-tools/cross-organization/assess_check_s3_buckets.py
index 94e3f8bb..81a88e50 100755
--- a/local-app/python-tools/cross-organization/assess_check_s3_buckets.py
+++ b/local-app/python-tools/cross-organization/assess_check_s3_buckets.py
@@ -9,7 +9,7 @@
 from collections import defaultdict
 
 # --- VERSIONING ---
-__version__ = "1.2.1"
+__version__ = "1.2.2"
 
 UNIT_CONVERSION_MAP = {
     "B":  {"divisor": 1, "label": "Bytes"},
@@ -24,7 +24,7 @@ def find_latest_file(pattern):
     return max(files, key=os.path.getctime) if files else None
 
 def main():
-    parser = argparse.ArgumentParser(description="AWS S3 Fleet Storage Filtered Assessor Suite v1.2.1")
+    parser = argparse.ArgumentParser(description="AWS S3 Fleet Storage Filtered Assessor Suite v1.2.2")
     parser.add_argument("--input", help="JSON file (default: latest audit_results.check_s3_buckets.*.json)")
     parser.add_argument("--size-units", default="GB", choices=["B", "MB", "GB", "TB", "PB"], 
                         help="Target size scale unit representation to output in reports (Default: GB)")
@@ -45,7 +45,6 @@ def main():
     txt_filename = f"{os.path.splitext(input_file)[0]}.txt"
     csv_filename = f"s3_global_inventory_{timestamp_suffix}.csv"
 
-    # Compile the bucket filter regex if provided
     bucket_regex = None
     if args.bucket_name:
         try:
@@ -102,7 +101,6 @@ def main():
                 b["versioning_status"], b["kms_key_id"], b["has_lifecycle"]
             ])
 
-    # Write Filtered Inventory CSV File
     with open(csv_filename, "w", newline="", encoding="utf-8") as cf:
         writer = csv.writer(cf)
         writer.writerow(["Account ID", "Account Alias", "Bucket Name", "Region", "ARN", f"Size ({target_unit})", "Object Count", "Versioning Status", "KMS Key ID", "Has Lifecycle"])
@@ -110,7 +108,6 @@ def main():
             scaled_csv_size = r[5] if target_unit == "B" else round(r[5] / divisor, 4)
             writer.writerow([r[0], r[1], r[2], r[3], r[4], scaled_csv_size, r[6], r[7], r[8], r[9]])
 
-    # Compile Aligned TXT Dashboard Report Asset
     with open(txt_filename, "w", encoding="utf-8") as tf:
         tf.write("=" * 155 + "\n")
         tf.write(f"AWS ORGANIZATIONAL S3 METRICS STORAGE OPTIMIZATION REPORT | Source: {input_base_name}\n")
@@ -127,7 +124,7 @@ def main():
         tf.write(f"  ▶ Total Global Footprint Aggregation : {scaled_global_size:{fmt_spec}} {unit_label}\n")
         tf.write(f"  ▶ Total Allocated Object Count       : {total_global_objects:,} Objects\n\n")
 
-        # --- FIXED: Explicitly isolate clean format specifiers upfront to avoid syntax tuples ---
+        # Section 2
         tf.write("[SECTION 2: ACCOUNT BILLING SECTOR SUMMARY BREAKDOWN]\n")
         if target_unit == "B":
             fmt_acct = f"     • Target Account: {{:<45}} | Buckets: {{:>4}} | Size ({target_unit}): {{:>15,d}} | Objects: {{:>14,}}\n"
@@ -139,7 +136,7 @@ def main():
             tf.write(fmt_acct.format(acct, metrics["count"], scaled_acct_size, metrics["objects"]))
         tf.write("\n")
 
-        # --- FIXED: Isolate clean format specifiers for Section 3 ---
+        # Section 3
         tf.write("[SECTION 3: SPACE DISTRIBUTION BY STORAGE TYPE TIER]\n")
         if target_unit == "B":
             fmt_tier = f"     • Storage Tier: {{:<30}} | Footprint ({target_unit}): {{:>17,d}} | Share %: {{:>8.2f}}\n"
@@ -152,21 +149,21 @@ def main():
             tf.write(fmt_tier.format(s_tier, scaled_tier_size, share))
         tf.write("\n")
 
-        # Section 4: Granular Matrix Table
+        # --- FIXED: Use clean pre-compiled f-string masks to inject tracking widths safely ---
         tf.write("[SECTION 4: GRANULAR BUCKET METADATA INVENTORY MATRIX]\n")
         size_header_str = f"Size ({target_unit})"
         
-        fmt_row_header = f"  {{:<12}} | {{:<{{max_alias_w}}}} | {{:<{{max_bucket_w}}}} | {{:<12}} | {{:>18}} | {{:>12}} | {{:<10}} | {{:<13}}\n"
-        fmt_row_data   = f"  {{:<12}} | {{:<{{max_alias_w}}}} | {{:<{{max_bucket_w}}}} | {{:<12}} | {{:>18{',d' if target_unit == 'B' else ',.2f'}}} | {{:>12,}} | {{:<10}} | {{:<13}}\n"
+        fmt_row_header = f"  {{:<12}} | {{:<{max_alias_w}}} | {{:<{max_bucket_w}}} | {{:<12}} | {{:>18}} | {{:>12}} | {{:<10}} | {{:<13}}\n"
         
-        f_header = fmt_row_header.format("Account ID", size_header_str, "Version", "Lifecycle", max_alias_w=max_alias_w, max_bucket_w=max_bucket_w)
-        tf.write(f_header.format("Account ID", "Account Alias", "Bucket Name", "Region", size_header_str, "Objects", "Version", "Lifecycle"))
+        size_data_spec = ",d" if target_unit == "B" else ",.2f"
+        fmt_row_data   = f"  {{:<12}} | {{:<{max_alias_w}}} | {{:<{max_bucket_w}}} | {{:<12}} | {{:>18{size_data_spec}}} | {{:>12,}} | {{:<10}} | {{:<13}}\n"
         
+        tf.write(fmt_row_header.format("Account ID", "Account Alias", "Bucket Name", "Region", size_header_str, "Objects", "Version", "Lifecycle"))
         tf.write("  " + "-" * (max_alias_w + max_bucket_w + 100) + "\n")
+        
         for r in flat_rows:
             scaled_bucket_size = r[5] if target_unit == "B" else (r[5] / divisor)
-            f_data = fmt_row_data.format(max_alias_w=max_alias_w, max_bucket_w=max_bucket_w)
-            tf.write(f_data.format(r[0], r[1], r[2], r[3], scaled_bucket_size, r[6], r[7], r[9]))
+            tf.write(fmt_row_data.format(r[0], r[1], r[2], r[3], scaled_bucket_size, r[6], r[7], r[9]))
 
     print(f"[+] Output unit normalization target mapped successfully to: {target_unit}")
     print(f"[+] Multi-Account Analytical Dashboard Saved: {txt_filename}")