diff --git a/script/shell_script_odoo/brin_advisor.py b/script/shell_script_odoo/brin_advisor.py
new file mode 100644
index 0000000..2f27518
--- /dev/null
+++ b/script/shell_script_odoo/brin_advisor.py
@@ -0,0 +1,191 @@
+#!/usr/bin/env python3
+"""
+brin_advisor.py — Recommends the optimal index type for an Odoo 18 model
+
+Usage (via odoo-bin shell):
+    source .venv/bin/activate
+    echo "MODEL='account.move.line'" | cat - brin_advisor.py \
+        | odoo-bin shell -d my_database --no-http
+
+    # Or with export:
+    export MODEL=mail.message
+    cat brin_advisor.py | odoo-bin shell -d my_database --no-http
+"""
+
+import os
+
+# ── Constants ─────────────────────────────────────────────────────────────────
+
+DATE_COLUMNS = [
+    "date",
+    "create_date",
+    "write_date",
+    "invoice_date",
+    "scheduled_date",
+    "date_done",
+    "in_date",
+    "expiration_date",
+]
+
+THRESHOLDS = [
+    (0.9, "✅ BRIN ideal", "\033[0;32m"),
+    (0.5, "⚠️  BRIN acceptable", "\033[1;33m"),
+    (0.1, "❌ Prefer B-tree", "\033[0;31m"),
+    (-1.0, "❌ B-tree mandatory", "\033[0;31m"),
+]
+
+RESET = "\033[0m"
+BOLD = "\033[1m"
+CYAN = "\033[0;36m"
+GREEN = "\033[0;32m"
+YELLOW = "\033[1;33m"
+RED = "\033[0;31m"
+
+
+# ── Helpers ───────────────────────────────────────────────────────────────────
+
+
+def recommend(correlation):
+    for threshold, label, color in THRESHOLDS:
+        if correlation >= threshold:
+            return label, color
+    return THRESHOLDS[-1][1], THRESHOLDS[-1][2]
+
+
+def model_to_table(model_name):
+    """
+    Resolve model name to table via the Odoo ORM — more reliable than a plain
+    replace('.', '_') since some models override _table explicitly.
+    """
+    if model_name not in env:
+        # Fallback: standard Odoo naming convention
+        return model_name.replace(".", "_")
+    return env[model_name]._table
+
+
+# ── Model resolution ──────────────────────────────────────────────────────────
+# Priority: MODEL variable defined before the pipe, then environment variable
+model_name = (
+    globals().get("MODEL") or os.environ.get("MODEL") or "account.move.line"
+)
+table = model_to_table(model_name)
+cr = env.cr  # PostgreSQL cursor provided by odoo-bin shell
+
+print(f"\n{BOLD}{CYAN}=== BRIN Advisor for: {model_name} ==={RESET}\n")
+print(f"  Model  : {model_name}")
+print(f"  Table  : {table}\n")
+
+# ── Check table exists ────────────────────────────────────────────────────────
+cr.execute(
+    """
+    SELECT COUNT(*) FROM information_schema.tables
+    WHERE table_schema = 'public' AND table_name = %s
+""",
+    (table,),
+)
+if cr.fetchone()[0] == 0:
+    print(f"{RED}❌  Table '{table}' not found in the database.{RESET}\n")
+    raise SystemExit(1)
+
+# ── Automatic ANALYZE ─────────────────────────────────────────────────────────
+# odoo-bin shell runs inside an open transaction — ANALYZE is not allowed
+# inside a transaction, so we temporarily switch to AUTOCOMMIT.
+print(
+    f"  {YELLOW}⟳  Running ANALYZE on {table}...{RESET}", end=" ", flush=True
+)
+with env.cr._cnx.cursor() as auto_cr:
+    old_isolation = env.cr._cnx.isolation_level
+    env.cr._cnx.set_isolation_level(0)  # AUTOCOMMIT
+    auto_cr.execute(
+        f"ANALYZE {table}"
+    )  # table name validated via ORM, no injection risk
+    env.cr._cnx.set_isolation_level(old_isolation)
+print(f"{GREEN}OK{RESET}\n")
+
+# ── Table size ────────────────────────────────────────────────────────────────
+cr.execute(
+    """
+    SELECT
+        pg_size_pretty(pg_total_relation_size(%s::regclass)),
+        reltuples::bigint
+    FROM pg_class WHERE relname = %s
+""",
+    (table, table),
+)
+row = cr.fetchone()
+if row:
+    size_pretty, row_count = row
+    print(
+        f"  Size   : {BOLD}{size_pretty}{RESET}  (~{row_count:,} estimated rows)\n"
+    )
+
+# ── Correlation analysis ──────────────────────────────────────────────────────
+cr.execute(
+    """
+    SELECT attname, correlation
+    FROM pg_stats
+    WHERE tablename = %s
+      AND attname = ANY(%s)
+    ORDER BY ABS(correlation) DESC NULLS LAST
+""",
+    (table, DATE_COLUMNS),
+)
+stats = cr.fetchall()
+
+print(f"{BOLD}Date columns found:{RESET}")
+print("─" * 70)
+print(f"  {'COLUMN':<25} {'CORRELATION':<14} {'IDX SIZE':<12} RECOMMENDATION")
+print("─" * 70)
+
+if not stats:
+    print(f"  {YELLOW}No standard date columns found on this table.{RESET}")
+else:
+    for col, corr in stats:
+        if corr is None:
+            continue
+        # Size of existing BRIN index on this column, if any
+        cr.execute(
+            """
+            SELECT pg_size_pretty(pg_relation_size(idx.indexrelid))
+            FROM pg_index idx
+            JOIN pg_class i ON i.oid = idx.indexrelid
+            JOIN pg_attribute a
+                ON a.attrelid = %s::regclass
+                AND a.attnum = ANY(idx.indkey)
+                AND a.attname = %s
+            JOIN pg_am am ON am.oid = i.relam AND am.amname = 'brin'
+            WHERE idx.indrelid = %s::regclass
+            LIMIT 1
+        """,
+            (table, col, table),
+        )
+        idx_row = cr.fetchone()
+        idx_size = idx_row[0] if idx_row else "none"
+
+        label, color = recommend(corr)
+        print(
+            f"  {col:<25} {corr:<14.4f} {idx_size:<12} {color}{label}{RESET}"
+        )
+
+print("─" * 70)
+
+# ── Existing BRIN indexes ─────────────────────────────────────────────────────
+print(f"\n{BOLD}Existing BRIN indexes:{RESET}")
+cr.execute(
+    """
+    SELECT indexname, indexdef
+    FROM pg_indexes
+    WHERE tablename = %s
+      AND indexdef LIKE '%%USING brin%%'
+""",
+    (table,),
+)
+brin_indexes = cr.fetchall()
+
+if not brin_indexes:
+    print(f"  {YELLOW}No BRIN indexes found on this table.{RESET}")
+else:
+    for iname, _ in brin_indexes:
+        print(f"  {GREEN}✔{RESET}  {iname}")
+
+print()
diff --git a/script/shell_script_odoo/brin_cluster.py b/script/shell_script_odoo/brin_cluster.py
new file mode 100644
index 0000000..1bcbc57
--- /dev/null
+++ b/script/shell_script_odoo/brin_cluster.py
@@ -0,0 +1,286 @@
+#!/usr/bin/env python3
+"""
+brin_cluster.py — Reorders table rows physically to optimize BRIN indexes.
+
+Strategy:
+  1. Check correlation of candidate columns via pg_stats
+  2. Pick the best column (highest absolute correlation, ideally create_date)
+  3. Create a temporary B-tree index on that column
+  4. CLUSTER the table on that index (rewrites rows in physical order)
+  5. ANALYZE to refresh statistics
+  6. Drop the temporary B-tree index
+  7. Report the new correlation
+
+WARNING:
+  - CLUSTER acquires an ACCESS EXCLUSIVE lock — the table is fully locked.
+  - On large tables, this can take minutes to hours.
+  - Run during a maintenance window or off-peak hours.
+  - Always pg_dump before running on production.
+
+Usage:
+    export MODEL=account.move.line
+    cat brin_cluster.py | odoo-bin shell -d my_database --no-http
+
+    # Dry run (no changes, just shows what would be done):
+    export MODEL=account.move.line DRY_RUN=1
+    cat brin_cluster.py | odoo-bin shell -d my_database --no-http
+"""
+
+import os
+import time
+
+# ── Constants ─────────────────────────────────────────────────────────────────
+
+# Columns considered as clustering candidates, in priority order.
+# create_date is first: it's never updated, so correlation is always perfect.
+CANDIDATE_COLUMNS = [
+    "create_date",
+    "date",
+    "invoice_date",
+    "write_date",
+    "scheduled_date",
+    "date_done",
+    "in_date",
+]
+
+BRIN_IDEAL_THRESHOLD = 0.9
+
+RESET = "\033[0m"
+BOLD = "\033[1m"
+CYAN = "\033[0;36m"
+GREEN = "\033[0;32m"
+YELLOW = "\033[1;33m"
+RED = "\033[0;31m"
+
+
+# ── Config ────────────────────────────────────────────────────────────────────
+
+model_name = (
+    globals().get("MODEL") or os.environ.get("MODEL") or "account.move.line"
+)
+dry_run = bool(globals().get("DRY_RUN") or os.environ.get("DRY_RUN"))
+
+if model_name not in env:
+    table = model_name.replace(".", "_")
+else:
+    table = env[model_name]._table
+
+cr = env.cr
+cnx = env.cr._cnx
+
+print(f"\n{BOLD}{CYAN}=== BRIN Cluster for: {model_name} ==={RESET}")
+print(f"  Table   : {table}")
+print(f"  Dry run : {dry_run}\n")
+
+
+# ── Helpers ───────────────────────────────────────────────────────────────────
+
+
+def autocommit(sql, params=None):
+    """Execute a DDL statement outside the current transaction (AUTOCOMMIT)."""
+    old = cnx.isolation_level
+    cnx.set_isolation_level(0)
+    with cnx.cursor() as c:
+        c.execute(sql, params)
+    cnx.set_isolation_level(old)
+
+
+def get_correlations():
+    cr.execute(
+        """
+        SELECT attname, correlation
+        FROM pg_stats
+        WHERE tablename = %s
+          AND attname = ANY(%s)
+          AND correlation IS NOT NULL
+        ORDER BY ABS(correlation) DESC
+    """,
+        (table, CANDIDATE_COLUMNS),
+    )
+    return {row[0]: row[1] for row in cr.fetchall()}
+
+
+def table_size():
+    cr.execute(
+        """
+        SELECT
+            pg_size_pretty(pg_total_relation_size(%s::regclass)),
+            reltuples::bigint
+        FROM pg_class WHERE relname = %s
+    """,
+        (table, table),
+    )
+    return cr.fetchone()
+
+
+def index_exists(index_name):
+    cr.execute(
+        """
+        SELECT COUNT(*) FROM pg_indexes
+        WHERE tablename = %s AND indexname = %s
+    """,
+        (table, index_name),
+    )
+    return cr.fetchone()[0] > 0
+
+
+# ── Step 1: ANALYZE to get fresh stats ───────────────────────────────────────
+
+print(f"  {YELLOW}⟳  ANALYZE {table}...{RESET}", end=" ", flush=True)
+autocommit(f"ANALYZE {table}")
+print(f"{GREEN}OK{RESET}\n")
+
+size_pretty, row_count = table_size()
+print(
+    f"  Size    : {BOLD}{size_pretty}{RESET}  (~{row_count:,} estimated rows)\n"
+)
+
+# ── Step 2: Check current correlations ───────────────────────────────────────
+
+correlations = get_correlations()
+
+if not correlations:
+    print(f"{RED}❌  No date columns found on {table}. Aborting.{RESET}\n")
+    raise SystemExit(1)
+
+print(f"{BOLD}Current correlations:{RESET}")
+for col, corr in sorted(correlations.items(), key=lambda x: -abs(x[1])):
+    if abs(corr) >= BRIN_IDEAL_THRESHOLD:
+        icon = f"{GREEN}✅ BRIN ideal{RESET}"
+    else:
+        icon = f"{YELLOW}⚠️  needs clustering{RESET}"
+    print(f"  {col:<25} {corr:+.4f}   {icon}")
+
+# ── Step 3: Pick the best clustering column ───────────────────────────────────
+
+# Only cluster if at least one *candidate* column is suboptimal.
+# Note: a column like `date` (user-editable) may never reach ideal correlation
+# regardless of clustering — that's expected and not a reason to cluster.
+suboptimal = {
+    col: corr
+    for col, corr in correlations.items()
+    if abs(corr) < BRIN_IDEAL_THRESHOLD
+}
+ideal = {
+    col: corr
+    for col, corr in correlations.items()
+    if abs(corr) >= BRIN_IDEAL_THRESHOLD
+}
+
+if suboptimal:
+    print(f"\n  {YELLOW}⚠️  Suboptimal columns: {', '.join(suboptimal)}{RESET}")
+    print(
+        f"  {CYAN}Note: columns like 'date' or 'invoice_date' may stay suboptimal"
+    )
+    print(
+        f"  after clustering if users enter backdated records — that's expected.{RESET}"
+    )
+else:
+    print(
+        f"\n{GREEN}✅  All columns already have ideal correlation. No clustering needed.{RESET}\n"
+    )
+    raise SystemExit(0)
+
+# Pick the best clustering column: highest absolute correlation among candidates.
+# Prefer create_date first (never updated → guaranteed monotonic order),
+# then fall back to the column with highest abs(correlation).
+cluster_col = None
+for preferred in CANDIDATE_COLUMNS:
+    if preferred in ideal:  # already ideal → good anchor for physical order
+        cluster_col = preferred
+        break
+if not cluster_col:
+    # All candidates are suboptimal — pick the least bad one
+    cluster_col = max(suboptimal, key=lambda c: abs(suboptimal[c]))
+
+if not cluster_col:
+    print(f"{RED}❌  No suitable clustering column found. Aborting.{RESET}\n")
+    raise SystemExit(1)
+
+print(
+    f"\n  {BOLD}Clustering column chosen:{RESET} {cluster_col}  "
+    f"(current correlation: {correlations[cluster_col]:+.4f})\n"
+)
+
+# ── Step 4: Create temporary B-tree index ────────────────────────────────────
+
+tmp_index = f"_brin_cluster_tmp_{table}_{cluster_col}"
+
+if index_exists(tmp_index):
+    print(
+        f"  {YELLOW}⚠️  Temporary index already exists, dropping it first...{RESET}"
+    )
+    if not dry_run:
+        autocommit(f'DROP INDEX IF EXISTS "{tmp_index}"')
+
+print(
+    f"  {YELLOW}⟳  Creating temporary B-tree index on {cluster_col}...{RESET}",
+    end=" ",
+    flush=True,
+)
+if not dry_run:
+    autocommit(f'CREATE INDEX "{tmp_index}" ON "{table}" ("{cluster_col}")')
+    print(f"{GREEN}OK{RESET}")
+else:
+    print(f"{CYAN}[DRY RUN]{RESET}")
+
+# ── Step 5: CLUSTER ───────────────────────────────────────────────────────────
+
+print(f"\n  {YELLOW}⟳  CLUSTER {table} on {cluster_col}...{RESET}")
+print(
+    f"  {YELLOW}     (ACCESS EXCLUSIVE lock — table unavailable during this operation){RESET}",
+    end=" ",
+    flush=True,
+)
+
+if not dry_run:
+    t0 = time.time()
+    autocommit(f'CLUSTER "{table}" USING "{tmp_index}"')
+    elapsed = time.time() - t0
+    print(f"{GREEN}OK{RESET}  ({elapsed:.1f}s)")
+else:
+    print(f"{CYAN}[DRY RUN]{RESET}")
+
+# ── Step 6: Drop temporary index ─────────────────────────────────────────────
+
+print(
+    f"\n  {YELLOW}⟳  Dropping temporary index...{RESET}", end=" ", flush=True
+)
+if not dry_run:
+    autocommit(f'DROP INDEX IF EXISTS "{tmp_index}"')
+    print(f"{GREEN}OK{RESET}")
+else:
+    print(f"{CYAN}[DRY RUN]{RESET}")
+
+# ── Step 7: ANALYZE again + report new correlations ──────────────────────────
+
+print(
+    f"\n  {YELLOW}⟳  ANALYZE after clustering...{RESET}", end=" ", flush=True
+)
+if not dry_run:
+    autocommit(f"ANALYZE {table}")
+    print(f"{GREEN}OK{RESET}\n")
+
+    new_correlations = get_correlations()
+
+    print(f"{BOLD}Correlations after clustering:{RESET}")
+    print("─" * 55)
+    print(f"  {'COLUMN':<25} {'BEFORE':>8}   {'AFTER':>8}   RESULT")
+    print("─" * 55)
+    for col in sorted(correlations.keys()):
+        before = correlations.get(col, 0.0)
+        after = new_correlations.get(col, 0.0)
+        delta = after - before
+        if abs(after) >= BRIN_IDEAL_THRESHOLD:
+            result = f"{GREEN}✅ BRIN ideal{RESET}"
+        else:
+            result = f"{YELLOW}⚠️  still suboptimal{RESET}"
+        arrow = "↑" if delta > 0.01 else ("↓" if delta < -0.01 else "→")
+        print(
+            f"  {col:<25} {before:>+8.4f}   {after:>+8.4f} {arrow}  {result}"
+        )
+    print("─" * 55)
+else:
+    print(f"{CYAN}[DRY RUN]{RESET}")
+
+print(f"\n{GREEN}✅  Done.{RESET}\n")