diff --git a/src/pdf_extract.py b/src/pdf_extract.py
index 07d23a2..cd35143 100644
--- a/src/pdf_extract.py
+++ b/src/pdf_extract.py
@@ -417,16 +417,432 @@ def _coerce_amount_columns(
     return out
 
 
+# ---------------------------------------------------------------------------
+# Row-heuristic extraction (mode = "row_heuristic", default for new templates)
+# ---------------------------------------------------------------------------
+
+
+_DATE_RES = [
+    re.compile(r"\b(\d{1,2}/\d{1,2}/\d{2,4})\b"),
+    re.compile(r"\b(\d{1,2}-\d{1,2}-\d{2,4})\b"),
+    re.compile(r"\b(\d{4}-\d{2}-\d{2})\b"),
+    re.compile(r"\b([A-Z][a-z]{2}\s+\d{1,2},?\s+\d{2,4})\b"),
+    re.compile(r"\b(\d{1,2}\s+[A-Z][a-z]{2}\s+\d{2,4})\b"),
+    # Short month-day (e.g. "Jan 15") — sometimes used when year is
+    # implied by the statement period. Lower-priority match.
+    re.compile(r"\b([A-Z][a-z]{2}\s+\d{1,2})\b"),
+]
+
+# Amount tokens: optional $/€/£, optional leading -, optional parens,
+# 1-3 digits before grouping with comma-thousand groups, optional
+# decimal portion. Trailing minus also captured.
+_AMOUNT_RE = re.compile(
+    r"(?<![\w.])"                                  # no preceding letter/dot
+    r"(\(?-?[\$€£]?-?\d{1,3}(?:,\d{3})*(?:\.\d{1,4})?\)?-?)"
+    r"(?![\w.])"
+)
+
+
+def _looks_like_amount(token: str) -> bool:
+    """Reject tokens that match the amount regex but are obviously
+    not money — e.g. a bare year or a page number. Real amounts
+    have at least one of: currency symbol, decimal point, parens,
+    minus sign, or a thousand separator."""
+    if not token:
+        return False
+    return bool(re.search(r"[\$€£.,()\-]", token))
+
+
+def _find_dates_in_words(
+    row_words: list[WordBox],
+) -> list[tuple[int, str]]:
+    """Find the FIRST date-like substring on this row.
+
+    Returns ``[(word_index, date_text)]`` or empty list. Searches
+    word-by-word so we can identify which word(s) constitute the
+    date and exclude them from the description."""
+    for i, w in enumerate(row_words):
+        # Stitch the next few words together — some date formats
+        # like "Jan 15, 2026" span 3 word tokens.
+        for window in (3, 2, 1):
+            chunk = " ".join(x.text for x in row_words[i : i + window])
+            for rx in _DATE_RES:
+                m = rx.search(chunk)
+                if m:
+                    return [(i, m.group(1))]
+    return []
+
+
+def _find_amount_tokens(
+    row_words: list[WordBox],
+) -> list[tuple[int, WordBox, str]]:
+    """Find amount-shaped tokens on this row, keeping their position.
+
+    Returns ``[(word_index, wordbox, normalized_text)]``. The
+    word_index lets the caller exclude these from description text;
+    the wordbox preserves the x-position so we can cluster amount
+    columns later without templated coordinates."""
+    out: list[tuple[int, WordBox, str]] = []
+    for i, w in enumerate(row_words):
+        # Each word might contain multiple amount tokens if the PDF
+        # extractor merged things, but in practice one match per word.
+        m = _AMOUNT_RE.search(w.text)
+        if m and _looks_like_amount(m.group(1)):
+            out.append((i, w, m.group(1)))
+    return out
+
+
+def _row_is_transaction(
+    row_words: list[WordBox],
+    *,
+    min_amounts: int,
+    max_amounts: int,
+) -> bool:
+    """A transaction line has at least one date AND enough amount
+    tokens to satisfy the configured shape."""
+    if not _find_dates_in_words(row_words):
+        return False
+    amounts = _find_amount_tokens(row_words)
+    return min_amounts <= len(amounts) <= max_amounts
+
+
+def _description_from_row(
+    row_words: list[WordBox],
+    date_idx: int,
+    amount_idxs: set[int],
+) -> str:
+    """Stitch the row's description: everything between the date
+    word and the first amount token, plus anything after the last
+    amount that isn't itself an amount."""
+    keep: list[str] = []
+    seen_first_amount = False
+    last_amount_idx = max(amount_idxs) if amount_idxs else -1
+    for i, w in enumerate(row_words):
+        if i == date_idx:
+            continue
+        if i in amount_idxs:
+            seen_first_amount = True
+            continue
+        # After the last amount, trailing tokens are usually a
+        # check number or memo — keep them too.
+        if seen_first_amount and i < last_amount_idx:
+            continue
+        keep.append(w.text)
+    return " ".join(keep).strip()
+
+
+def _assign_amounts_by_shape(
+    amount_tokens: list[tuple[int, WordBox, str]],
+    shape: str,
+    parse_opts: dict[str, Any],
+    column_centers: list[float] | None = None,
+) -> dict[str, Any]:
+    """Map raw amount tokens to typed CSV fields per the shape.
+
+    Shapes:
+      ``single``  → first amount is ``amount`` (sign in value)
+      ``txn_balance`` → leftmost is ``amount``, rightmost is
+        ``balance``
+      ``debit_credit`` → if one token, assign to debit or credit by
+        x-position (uses ``column_centers``); if two, leftmost is
+        debit, next is credit. Combine into signed ``amount``.
+      ``debit_credit_balance`` → leftmost is debit, middle is
+        credit, rightmost is balance.
+    """
+    out: dict[str, Any] = {}
+    if not amount_tokens:
+        return out
+    txt = [t[2] for t in amount_tokens]
+    boxes = [t[1] for t in amount_tokens]
+
+    if shape == "single":
+        out["amount"] = parse_amount(txt[0], parse_opts)
+
+    elif shape == "txn_balance":
+        out["amount"] = parse_amount(txt[0], parse_opts)
+        if len(txt) >= 2:
+            out["balance"] = parse_amount(txt[-1], parse_opts)
+
+    elif shape == "debit_credit":
+        debit_val: float | None = None
+        credit_val: float | None = None
+        if len(txt) == 1 and column_centers and len(column_centers) >= 2:
+            # Decide debit vs credit by which column-center the token's
+            # midpoint is closest to.
+            mid = (boxes[0].x0 + boxes[0].x1) / 2
+            distances = [abs(mid - c) for c in column_centers[:2]]
+            if distances[0] <= distances[1]:
+                debit_val = parse_amount(txt[0], parse_opts)
+            else:
+                credit_val = parse_amount(txt[0], parse_opts)
+        else:
+            # Two tokens: leftmost = debit, rightmost = credit.
+            if len(txt) >= 1:
+                debit_val = parse_amount(txt[0], parse_opts)
+            if len(txt) >= 2:
+                credit_val = parse_amount(txt[1], parse_opts)
+        amt = 0.0
+        if credit_val:
+            amt += credit_val
+        if debit_val:
+            amt -= debit_val
+        out["amount"] = amt
+        out["type"] = "credit" if amt > 0 else ("debit" if amt < 0 else "")
+
+    elif shape == "debit_credit_balance":
+        debit_val = None
+        credit_val = None
+        if len(txt) == 2 and column_centers and len(column_centers) >= 3:
+            # Two tokens but the shape expects three — fall through
+            # to x-position assignment using the configured columns.
+            mids = [(b.x0 + b.x1) / 2 for b in boxes]
+            assigned: list[int | None] = [None, None, None]
+            for k, m in enumerate(mids):
+                col = min(
+                    range(3),
+                    key=lambda c, m=m: abs(m - column_centers[c]),
+                )
+                assigned[col] = k
+            if assigned[0] is not None:
+                debit_val = parse_amount(txt[assigned[0]], parse_opts)
+            if assigned[1] is not None:
+                credit_val = parse_amount(txt[assigned[1]], parse_opts)
+            if assigned[2] is not None:
+                out["balance"] = parse_amount(txt[assigned[2]], parse_opts)
+        else:
+            if len(txt) >= 1:
+                debit_val = parse_amount(txt[0], parse_opts)
+            if len(txt) >= 2:
+                credit_val = parse_amount(txt[1], parse_opts)
+            if len(txt) >= 3:
+                out["balance"] = parse_amount(txt[2], parse_opts)
+        amt = 0.0
+        if credit_val:
+            amt += credit_val
+        if debit_val:
+            amt -= debit_val
+        out["amount"] = amt
+        out["type"] = "credit" if amt > 0 else ("debit" if amt < 0 else "")
+    else:
+        # Unknown shape — fall back to the simplest interpretation.
+        out["amount"] = parse_amount(txt[0], parse_opts)
+    return out
+
+
+def _infer_amount_column_centers(
+    rows: list[list[WordBox]],
+    *,
+    expected: int,
+    min_amounts: int,
+    max_amounts: int,
+) -> list[float]:
+    """Cluster amount-token x-midpoints across all transaction rows
+    to find natural column centers. Returns up to *expected* centers
+    sorted left-to-right.
+
+    Avoids re-introducing user-drawn coordinates: the columns are
+    inferred from the data itself. We can't run k-means without
+    scikit-learn, so use a simple sorted-midpoints + greedy bucket
+    by proximity tolerance approach.
+    """
+    midpoints: list[float] = []
+    for row_words in rows:
+        if not _row_is_transaction(
+            row_words, min_amounts=min_amounts, max_amounts=max_amounts,
+        ):
+            continue
+        for _, w, _ in _find_amount_tokens(row_words):
+            midpoints.append((w.x0 + w.x1) / 2)
+    if not midpoints:
+        return []
+    midpoints.sort()
+    # Bucket by adjacency: any gap > 30pt starts a new bucket.
+    # 30pt ≈ 4x the typical inter-column spacing on bank statements.
+    buckets: list[list[float]] = [[midpoints[0]]]
+    for m in midpoints[1:]:
+        if m - buckets[-1][-1] <= 30:
+            buckets[-1].append(m)
+        else:
+            buckets.append([m])
+    centers = [sum(b) / len(b) for b in buckets]
+    if len(centers) <= expected:
+        return centers
+    # More buckets than expected — keep the *expected* most-populated.
+    by_pop = sorted(
+        zip(centers, (len(b) for b in buckets)),
+        key=lambda x: x[1],
+        reverse=True,
+    )[:expected]
+    return sorted(c for c, _ in by_pop)
+
+
+def find_transaction_rows(
+    pages: list[Page],
+    template: dict[str, Any],
+) -> list[dict[str, Any]]:
+    """Heuristic row detector. Returns a list of preview records
+    suitable for rendering in the build-mode preview table.
+
+    Each record carries the raw text + parsed fields; the GUI
+    surfaces these so the user can confirm or tune the template
+    before extraction commits to disk.
+    """
+    rd = template.get("row_detection", {}) or {}
+    amt_cfg = template.get("amounts", {}) or {}
+    date_cfg = template.get("date", {}) or {}
+    pages_cfg = template.get("pages", {}) or {}
+
+    pages_used = _pages_in_range(pages, pages_cfg.get("range", "all"))
+    skip_pages_re = pages_cfg.get("skip_matching") or ""
+    if skip_pages_re:
+        skip_re = re.compile(skip_pages_re, re.IGNORECASE)
+        pages_used = [p for p in pages_used if not skip_re.search(p.text)]
+
+    min_amounts = int(rd.get("min_amounts_per_row", 1))
+    max_amounts = int(rd.get("max_amounts_per_row", 3))
+    skip_row_res = [
+        re.compile(p, re.IGNORECASE)
+        for p in (rd.get("skip_rows_matching") or [])
+    ]
+    shape = amt_cfg.get("shape", "single")
+    expected_amount_cols = {
+        "single": 1,
+        "txn_balance": 2,
+        "debit_credit": 2,
+        "debit_credit_balance": 3,
+    }.get(shape, 1)
+
+    parse_opts = {
+        "decimal_separator": amt_cfg.get("decimal_separator", "."),
+        "thousands_separator": amt_cfg.get("thousands_separator", ","),
+        "currency_strip": amt_cfg.get("currency_strip", "$"),
+        "negative_in_parens": amt_cfg.get("negative_in_parens", True),
+    }
+    date_formats: list[str] = list(date_cfg.get("formats_fallback") or [])
+    if date_cfg.get("format"):
+        date_formats = [date_cfg["format"]] + date_formats
+
+    # First pass per page: gather rows so we can also infer amount
+    # column centers across the whole document.
+    all_rows: list[tuple[Page, list[list[WordBox]]]] = []
+    for page in pages_used:
+        rows = cluster_rows(
+            page.words,
+            y_tolerance=float(rd.get("y_tolerance", 3.0)),
+        )
+        all_rows.append((page, rows))
+
+    flat_rows = [r for _, rows in all_rows for r in rows]
+    column_centers = _infer_amount_column_centers(
+        flat_rows,
+        expected=expected_amount_cols,
+        min_amounts=min_amounts,
+        max_amounts=max_amounts,
+    )
+
+    out: list[dict[str, Any]] = []
+    merge_multi = bool(rd.get("merge_multiline_description", True))
+    prev: dict[str, Any] | None = None
+
+    for page, rows in all_rows:
+        for row_words in rows:
+            line = " ".join(w.text for w in row_words)
+            if not line.strip():
+                continue
+            if any(rx.search(line) for rx in skip_row_res):
+                continue
+
+            dates = _find_dates_in_words(row_words)
+            amount_tokens = _find_amount_tokens(row_words)
+
+            is_txn = bool(dates) and (
+                min_amounts <= len(amount_tokens) <= max_amounts
+            )
+
+            if not is_txn:
+                # Possible multi-line description continuation —
+                # a no-date, no-amount line directly following a
+                # transaction.
+                if (
+                    merge_multi
+                    and prev is not None
+                    and not amount_tokens
+                    and not dates
+                ):
+                    prev["description"] = (
+                        (prev.get("description") or "") + " " + line
+                    ).strip()
+                continue
+
+            date_idx, date_text = dates[0]
+            amount_idxs = {idx for idx, _, _ in amount_tokens}
+            desc = _description_from_row(row_words, date_idx, amount_idxs)
+
+            record: dict[str, Any] = {
+                "date": parse_date(date_text, date_formats) or date_text,
+                "description": desc,
+                "_page": page.page_no,
+                "_raw_line": line,
+            }
+            record.update(_assign_amounts_by_shape(
+                amount_tokens, shape, parse_opts, column_centers,
+            ))
+            out.append(record)
+            prev = record
+
+    return out
+
+
+def apply_template_row_heuristic(
+    pages: list[Page],
+    template: dict[str, Any],
+) -> pd.DataFrame:
+    """Row-heuristic counterpart to ``apply_template``. Same return
+    shape (a DataFrame) so callers don't care which mode produced it."""
+    rows = find_transaction_rows(pages, template)
+    if not rows:
+        return pd.DataFrame()
+    df = pd.DataFrame(rows)
+    # Drop internal helper columns from the user-facing output.
+    if "_raw_line" in df.columns:
+        df = df.drop(columns=["_raw_line"])
+    preferred = ["date", "description", "amount", "type", "balance"]
+    cols = [c for c in preferred if c in df.columns]
+    extras = [c for c in df.columns if c not in cols and c != "_page"]
+    df = df[cols + extras + (["_page"] if "_page" in df.columns else [])]
+    return df
+
+
 def apply_template(
     pages: list[Page],
     template: dict[str, Any],
 ) -> pd.DataFrame:
-    """Run *template* over *pages* and return the extracted DataFrame.
+    """Dispatch by template mode and return the extracted DataFrame.
 
-    Template schema is defined in ``src/pdf_templates.py``. Missing
-    keys fall through to sensible defaults so a half-built template
-    in the GUI still produces a preview.
+    ``mode="row_heuristic"`` (default for new templates): no
+    coordinates needed — finds transaction lines by date+amount
+    pattern matching. Robust to layout drift between statements.
+
+    ``mode="column_visual"`` (legacy): uses x-position boundaries
+    from the visual picker. Kept for templates saved before the
+    row-heuristic shift.
+
+    Templates without a mode key default to ``column_visual`` for
+    backward compatibility with schema_version=1 templates.
     """
+    mode = template.get("mode", "column_visual")
+    if mode == "row_heuristic":
+        return apply_template_row_heuristic(pages, template)
+    return _apply_template_column_visual(pages, template)
+
+
+def _apply_template_column_visual(
+    pages: list[Page],
+    template: dict[str, Any],
+) -> pd.DataFrame:
+    """Original column-x-position pipeline. Now the legacy code
+    path; kept for any v1 templates and as the manual-override
+    advanced mode in the build UI."""
     pages_cfg = template.get("pages", {}) or {}
     table_cfg = template.get("table", {}) or {}
     columns_cfg = template.get("columns", []) or []
diff --git a/tests/test_pdf_row_heuristic.py b/tests/test_pdf_row_heuristic.py
new file mode 100644
index 0000000..c9f06b9
--- /dev/null
+++ b/tests/test_pdf_row_heuristic.py
@@ -0,0 +1,280 @@
+"""Tests for the row-heuristic extraction pipeline.
+
+This is now the primary extraction mode — uses date + amount
+pattern matching to find transaction lines, with no dependency
+on x-position column boundaries. Robust to layout drift across
+statements from the same bank.
+
+The legacy column-visual pipeline keeps its own tests in
+``test_pdf_extract.py``.
+"""
+
+from __future__ import annotations
+
+import pandas as pd
+
+from src.pdf_extract import (
+    Page,
+    WordBox,
+    apply_template,
+    apply_template_row_heuristic,
+    find_transaction_rows,
+    _find_amount_tokens,
+    _find_dates_in_words,
+    _infer_amount_column_centers,
+)
+
+
+def _w(text: str, x0: float, top: float) -> WordBox:
+    return WordBox(
+        x0=x0,
+        top=top,
+        x1=x0 + 8 * len(text),
+        bottom=top + 10,
+        text=text,
+    )
+
+
+class TestFindDatesInRow:
+    def test_us_slash(self):
+        row = [_w("01/15/2026", 0, 0), _w("Coffee", 100, 0)]
+        assert _find_dates_in_words(row) == [(0, "01/15/2026")]
+
+    def test_two_digit_year(self):
+        row = [_w("01/15/26", 0, 0), _w("Foo", 100, 0)]
+        result = _find_dates_in_words(row)
+        assert result and result[0][1] == "01/15/26"
+
+    def test_iso(self):
+        row = [_w("2026-01-15", 0, 0), _w("Tx", 100, 0)]
+        assert _find_dates_in_words(row) == [(0, "2026-01-15")]
+
+    def test_month_name(self):
+        # "Jan 15, 2026" — three word tokens, should stitch.
+        row = [_w("Jan", 0, 0), _w("15,", 25, 0), _w("2026", 50, 0)]
+        result = _find_dates_in_words(row)
+        assert result, "Multi-word month-day-year should match"
+        assert "Jan 15" in result[0][1]
+
+    def test_no_date(self):
+        row = [_w("Just", 0, 0), _w("text", 50, 0)]
+        assert _find_dates_in_words(row) == []
+
+
+class TestFindAmountTokens:
+    def test_currency_format(self):
+        row = [_w("Coffee", 0, 0), _w("$4.50", 100, 0)]
+        out = _find_amount_tokens(row)
+        assert len(out) == 1
+        assert out[0][2] == "$4.50"
+
+    def test_parens_negative(self):
+        row = [_w("(123.45)", 0, 0)]
+        out = _find_amount_tokens(row)
+        assert out and out[0][2] == "(123.45)"
+
+    def test_no_amount_on_pure_text(self):
+        row = [_w("Hello", 0, 0), _w("World", 50, 0)]
+        assert _find_amount_tokens(row) == []
+
+    def test_rejects_bare_year(self):
+        # "2026" matches the digit pattern but lacks $/decimal/etc.,
+        # so the looks-like-amount filter should drop it.
+        row = [_w("2026", 0, 0)]
+        # Bare integer can pass the regex but not the heuristic.
+        out = _find_amount_tokens(row)
+        # Either filtered out OR included — both are defensible.
+        # If included, it'd be missed-amount territory not a false-
+        # positive. Pin the conservative behavior: NO match.
+        assert out == [], "Bare 4-digit year should not register as amount"
+
+
+class TestInferAmountColumnCenters:
+    def test_two_clear_columns(self):
+        # 5 rows, each with two amounts at roughly x=300 and x=450.
+        rows = []
+        for top in range(0, 100, 20):
+            rows.append([
+                _w("01/15/2026", 20, top),
+                _w("Item", 100, top),
+                _w("$10.00", 300, top),
+                _w("$1,000.00", 450, top),
+            ])
+        centers = _infer_amount_column_centers(
+            rows, expected=2, min_amounts=2, max_amounts=2,
+        )
+        assert len(centers) == 2
+        # Left center ≈ 300 + 8*len("$10.00")/2 = 300+24 = 324
+        assert 310 < centers[0] < 340
+        assert 460 < centers[1] < 490
+
+    def test_no_transactions_returns_empty(self):
+        rows = [[_w("just", 0, 0), _w("text", 50, 0)]]
+        assert _infer_amount_column_centers(
+            rows, expected=2, min_amounts=1, max_amounts=3,
+        ) == []
+
+
+class TestRowHeuristicEndToEnd:
+    """Synthetic ``Page`` objects exercise the full row-heuristic
+    pipeline end-to-end without a real PDF."""
+
+    def _page_single_amount(self) -> Page:
+        words = [
+            _w("ACME BANK STATEMENT", 20, 0),
+            _w("01/15/2026", 20, 30), _w("Coffee", 100, 30),
+            _w("Shop", 150, 30), _w("$4.50", 400, 30),
+            _w("01/16/2026", 20, 50), _w("Refund", 100, 50),
+            _w("from", 100, 70), _w("vendor", 140, 70),  # continuation
+            _w("Vendor", 140, 50), _w("$12.00", 400, 50),
+            _w("Page", 20, 90), _w("1", 60, 90),  # not a txn
+        ]
+        return Page(page_no=1, width=600, height=120, text="", words=words)
+
+    def test_extracts_two_rows_single_amount(self):
+        tpl = {
+            "mode": "row_heuristic",
+            "row_detection": {
+                "min_amounts_per_row": 1,
+                "max_amounts_per_row": 1,
+                "merge_multiline_description": True,
+            },
+            "amounts": {"shape": "single", "negative_in_parens": True},
+            "date": {"format": "%m/%d/%Y"},
+        }
+        df = apply_template_row_heuristic([self._page_single_amount()], tpl)
+        assert len(df) == 2
+        assert list(df["date"]) == ["2026-01-15", "2026-01-16"]
+        # Multi-line description merged
+        assert "from vendor" in df.iloc[1]["description"]
+
+    def test_dispatches_through_apply_template(self):
+        tpl = {
+            "mode": "row_heuristic",
+            "row_detection": {"min_amounts_per_row": 1, "max_amounts_per_row": 1},
+            "amounts": {"shape": "single"},
+            "date": {"format": "%m/%d/%Y"},
+        }
+        df = apply_template([self._page_single_amount()], tpl)
+        assert isinstance(df, pd.DataFrame)
+        assert len(df) == 2
+
+    def test_txn_balance_shape(self):
+        page = Page(
+            page_no=1, width=600, height=100, text="", words=[
+                _w("01/15/2026", 20, 0), _w("Coffee", 100, 0),
+                _w("(4.50)", 300, 0), _w("1,000.00", 450, 0),
+                _w("01/16/2026", 20, 20), _w("Refund", 100, 20),
+                _w("12.00", 300, 20), _w("1,012.00", 450, 20),
+            ],
+        )
+        tpl = {
+            "mode": "row_heuristic",
+            "row_detection": {"min_amounts_per_row": 2, "max_amounts_per_row": 2},
+            "amounts": {"shape": "txn_balance", "negative_in_parens": True},
+            "date": {"format": "%m/%d/%Y"},
+        }
+        df = apply_template([page], tpl)
+        assert len(df) == 2
+        assert df.iloc[0]["amount"] == -4.50
+        assert df.iloc[0]["balance"] == 1000.00
+        assert df.iloc[1]["amount"] == 12.00
+        assert df.iloc[1]["balance"] == 1012.00
+
+    def test_debit_credit_balance_shape(self):
+        page = Page(
+            page_no=1, width=600, height=100, text="", words=[
+                _w("01/15/2026", 20, 0), _w("Coffee", 100, 0),
+                _w("4.50", 300, 0), _w("1,000.00", 450, 0),
+                _w("01/16/2026", 20, 20), _w("Refund", 100, 20),
+                _w("12.00", 380, 20), _w("1,012.00", 450, 20),
+            ],
+        )
+        tpl = {
+            "mode": "row_heuristic",
+            "row_detection": {"min_amounts_per_row": 2, "max_amounts_per_row": 3},
+            "amounts": {"shape": "debit_credit_balance"},
+            "date": {"format": "%m/%d/%Y"},
+        }
+        df = apply_template([page], tpl)
+        assert len(df) == 2
+        # Row 0: amount at x=300 (debit column) → debit, balance at 450
+        assert df.iloc[0]["amount"] == -4.50
+        assert df.iloc[0]["type"] == "debit"
+        # Row 1: amount at x=380 (credit column) → credit, balance at 450
+        assert df.iloc[1]["amount"] == 12.00
+        assert df.iloc[1]["type"] == "credit"
+
+    def test_skip_rows_matching(self):
+        page = self._page_single_amount()
+        tpl = {
+            "mode": "row_heuristic",
+            "row_detection": {
+                "min_amounts_per_row": 1,
+                "max_amounts_per_row": 1,
+                "skip_rows_matching": ["Refund"],
+            },
+            "amounts": {"shape": "single"},
+            "date": {"format": "%m/%d/%Y"},
+        }
+        df = apply_template_row_heuristic([page], tpl)
+        assert len(df) == 1
+        assert df.iloc[0]["date"] == "2026-01-15"
+
+    def test_layout_drift_doesnt_matter(self):
+        """The whole point of row-heuristic: same template works
+        on pages of different sizes / different column x-positions."""
+        # Page A: amounts at x=400
+        page_a = Page(
+            page_no=1, width=600, height=80, text="", words=[
+                _w("01/15/2026", 20, 0), _w("Coffee", 100, 0),
+                _w("$4.50", 400, 0),
+            ],
+        )
+        # Page B: amounts shifted to x=520 (different layout)
+        page_b = Page(
+            page_no=1, width=720, height=80, text="", words=[
+                _w("01/15/2026", 50, 0), _w("Coffee", 150, 0),
+                _w("$4.50", 520, 0),
+            ],
+        )
+        tpl = {
+            "mode": "row_heuristic",
+            "row_detection": {"min_amounts_per_row": 1, "max_amounts_per_row": 1},
+            "amounts": {"shape": "single"},
+            "date": {"format": "%m/%d/%Y"},
+        }
+        df_a = apply_template([page_a], tpl)
+        df_b = apply_template([page_b], tpl)
+        # Both should extract — proves no coordinate dependency.
+        assert len(df_a) == 1
+        assert len(df_b) == 1
+        assert df_a.iloc[0]["amount"] == df_b.iloc[0]["amount"] == 4.50
+
+
+class TestFindTransactionRows:
+    """The pre-DataFrame stage — returns dict records the build UI
+    uses to render a preview before the user commits."""
+
+    def test_returns_records(self):
+        page = Page(
+            page_no=1, width=600, height=80, text="", words=[
+                _w("01/15/2026", 20, 0), _w("Coffee", 100, 0),
+                _w("$4.50", 400, 0),
+            ],
+        )
+        tpl = {
+            "mode": "row_heuristic",
+            "row_detection": {"min_amounts_per_row": 1, "max_amounts_per_row": 1},
+            "amounts": {"shape": "single"},
+            "date": {"format": "%m/%d/%Y"},
+        }
+        rows = find_transaction_rows([page], tpl)
+        assert len(rows) == 1
+        r = rows[0]
+        assert r["date"] == "2026-01-15"
+        assert r["description"] == "Coffee"
+        assert r["amount"] == 4.50
+        assert r["_page"] == 1
+        # Raw line is preserved so the GUI can show "what we saw"
+        assert "_raw_line" in r