diff --git a/tools/misc/stratified_block_randomization.py b/tools/misc/stratified_block_randomization.py
new file mode 100644
index 000000000..fa9eddd02
--- /dev/null
+++ b/tools/misc/stratified_block_randomization.py
@@ -0,0 +1,462 @@
+#!/usr/bin/env python3
+"""Hierarchical proportional block assignment.
+
+Samples are split into blocks of size m (the last block may be smaller).
+Balancing is done by levels:
+1. Factor 1 is matched proportionally in every block.
+2. Within each factor-1 slice, factor 2 is matched proportionally.
+3. The same logic is applied recursively for additional factors.
+
+Integer quotas are computed with largest-remainder rounding while enforcing
+exact totals across both dimensions.
+"""
+import argparse
+from dataclasses import dataclass
+import sys
+
+import numpy as np
+import pandas as pd
+
+
+class StudyDesignerError(Exception):
+    """Base exception for recoverable tool errors."""
+
+
+@dataclass(frozen=True)
+class StudyDesignConfig:
+    """Runtime configuration for the study designer."""
+
+    input_path: str
+    output_path: str
+    block_size: int
+    factors: list[str]
+    sample_col: str
+    columns_mode: str
+    seed: int
+    summary_path: str | None
+
+
+def parse_args() -> StudyDesignConfig:
+    parser = argparse.ArgumentParser(
+        description="Create balanced random blocks preserving factor proportions."
+    )
+    parser.add_argument("--input", required=True, help="Input TSV file with samples.")
+    parser.add_argument(
+        "--block_size",
+        type=int,
+        required=True,
+        help=(
+            "Target size per block. Blocks are created sequentially from this size; "
+            "the final block may be smaller."
+        ),
+    )
+    parser.add_argument(
+        "--factors",
+        nargs="+",
+        required=True,
+        help="Column names to balance on (e.g. --factors group status).",
+    )
+    parser.add_argument(
+        "--sample_col",
+        default="sample",
+        help="Name of the sample identifier column (default: sample).",
+    )
+    parser.add_argument(
+        "--columns_mode",
+        choices=["names", "indices"],
+        default="names",
+        help=(
+            "Interpret --factors/--sample_col as column names (default) or as "
+            "1-based column indices. In 'indices' mode, --factors may be a "
+            "comma-separated list (e.g. 2,3) or space-separated values."
+        ),
+    )
+    parser.add_argument(
+        "--seed", type=int, default=42, help="Random seed for reproducibility."
+    )
+    parser.add_argument(
+        "--output",
+        required=True,
+        help="Output TSV file: original columns plus a 'block' column.",
+    )
+    parser.add_argument(
+        "--summary",
+        default=None,
+        help="Optional path to write a per-block factor-count summary TSV.",
+    )
+    args = parser.parse_args()
+    return StudyDesignConfig(
+        input_path=args.input,
+        output_path=args.output,
+        block_size=args.block_size,
+        factors=args.factors,
+        sample_col=args.sample_col,
+        columns_mode=args.columns_mode,
+        seed=args.seed,
+        summary_path=args.summary,
+    )
+
+
+def load_and_prepare_input(input_path: str) -> pd.DataFrame:
+    """Load input data as-is from TSV."""
+    df = pd.read_csv(input_path, sep="\t").reset_index(drop=True)
+    return df
+
+
+def add_original_index(df: pd.DataFrame) -> pd.DataFrame:
+    """Add a persistent original row index column."""
+    df = df.copy()
+    df.insert(0, "original_index", df.index)
+    return df
+
+
+def _index_token_to_name(columns: list[str], token: str, arg_name: str) -> str:
+    """Convert one 1-based index token (e.g. '3' or 'c3') to a column name."""
+    t = token.strip()
+    if t.lower().startswith("c"):
+        t = t[1:]
+
+    if not t.isdigit():
+        raise StudyDesignerError(
+            f"{arg_name}: invalid column index token '{token}'"
+        )
+
+    idx = int(t)
+    if idx < 1 or idx > len(columns):
+        raise StudyDesignerError(
+            f"{arg_name}: column index {idx} out of range (1..{len(columns)})"
+        )
+    return columns[idx - 1]
+
+
+def resolve_column_args(
+    df: pd.DataFrame,
+    factors: list[str],
+    sample_col: str,
+    columns_mode: str,
+) -> tuple[list[str], str]:
+    """Resolve factors/sample column arguments from names or 1-based indices."""
+    if columns_mode == "names":
+        return factors, sample_col
+
+    columns = list(df.columns)
+
+    factor_tokens: list[str] = []
+    for token in factors:
+        factor_tokens.extend([x for x in token.split(",") if x.strip()])
+
+    resolved_factors = [
+        _index_token_to_name(columns, token, "--factors") for token in factor_tokens
+    ]
+    resolved_sample_col = _index_token_to_name(columns, sample_col, "--sample_col")
+    return resolved_factors, resolved_sample_col
+
+
+def validate_inputs(
+    df: pd.DataFrame,
+    factors: list[str],
+    sample_col: str,
+    block_size: int,
+) -> None:
+    """Validate required columns and basic argument constraints."""
+    missing = [f for f in factors + [sample_col] if f not in df.columns]
+    if missing:
+        raise StudyDesignerError(f"columns not found in input: {missing}")
+
+    if block_size is not None and block_size < 1:
+        raise StudyDesignerError("--block_size must be >= 1")
+
+    na_mask = df.isna()
+    blank_mask = pd.DataFrame(False, index=df.index, columns=df.columns)
+    object_cols = df.select_dtypes(include=["object", "string"]).columns
+    for col in object_cols:
+        blank_mask[col] = df[col].astype("string").str.strip().eq("")
+
+    missing_mask = na_mask | blank_mask
+    if missing_mask.any().any():
+        missing_by_column = missing_mask.sum()
+        missing_by_column = missing_by_column[missing_by_column > 0]
+        details = ", ".join(
+            [f"{col}: {int(count)}" for col, count in missing_by_column.items()]
+        )
+        raise StudyDesignerError(
+            "input table contains missing values; please clean the input first. "
+            f"Missing counts by column: {details}"
+        )
+
+
+def normalize_factor_columns(
+    df: pd.DataFrame,
+    factors: list[str],
+) -> tuple[pd.DataFrame, list[str]]:
+    """Create normalized internal factor columns used for stratification."""
+    work = df.copy()
+    strat_cols = []
+    for factor in factors:
+        col = f"_strat_{factor}"
+        work[col] = work[factor].astype(str).str.strip().str.lower()
+        strat_cols.append(col)
+    return work, strat_cols
+
+
+def compute_block_sizes(total_samples: int, block_size: int) -> dict[int, int]:
+    """Compute block size map where the final block may be smaller."""
+    n_blocks = (total_samples + block_size - 1) // block_size
+    block_ids = list(range(n_blocks))
+    return {b: min(block_size, total_samples - b * block_size) for b in block_ids}
+
+
+def _allocate_matrix(
+    row_totals: dict[int, int],
+    col_totals: dict[str, int],
+    rng: np.random.Generator,
+    context: str = "",
+) -> dict[int, dict[str, int]]:
+    """Allocate integer matrix matching row/column margins.
+
+    Uses fractional quota target + floor + largest-remainder increments while
+    respecting both row and column deficits.
+    """
+    row_keys = list(row_totals.keys())
+    col_keys = list(col_totals.keys())
+
+    row = np.array([row_totals[k] for k in row_keys], dtype=int)
+    col = np.array([col_totals[k] for k in col_keys], dtype=int)
+    total = int(row.sum())
+
+    if total == 0:
+        return {rk: {ck: 0 for ck in col_keys} for rk in row_keys}
+
+    col_total = int(col.sum())
+    if total != col_total:
+        raise StudyDesignerError(
+            "ERROR: internal allocation mismatch (row/column totals differ) "
+            f"at {context or 'root'}: row_total={total}, col_total={col_total}."
+        )
+
+    quotas = np.outer(row, col) / total
+    alloc = np.floor(quotas).astype(int)
+    frac = quotas - alloc
+
+    row_def = row - alloc.sum(axis=1)
+    col_def = col - alloc.sum(axis=0)
+
+    while int(row_def.sum()) > 0:
+        best_score = None
+        best_cell = None
+        for i in range(len(row_keys)):
+            if row_def[i] <= 0:
+                continue
+            for j in range(len(col_keys)):
+                if col_def[j] <= 0:
+                    continue
+                # Tiny random tie-breaker keeps output reproducible with seed.
+                score = frac[i, j] + float(rng.random()) * 1e-9
+                if best_score is None or score > best_score:
+                    best_score = score
+                    best_cell = (i, j)
+
+        if best_cell is None:
+            raise StudyDesignerError(
+                "internal allocation failed to resolve deficits"
+            )
+
+        i, j = best_cell
+        alloc[i, j] += 1
+        row_def[i] -= 1
+        col_def[j] -= 1
+
+    return {
+        row_keys[i]: {col_keys[j]: int(alloc[i, j]) for j in range(len(col_keys))}
+        for i in range(len(row_keys))
+    }
+
+
+def _hierarchical_leaf_targets(
+    df_subset: pd.DataFrame,
+    factors: list[str],
+    block_totals: dict[int, int],
+    rng: np.random.Generator,
+    prefix: tuple[str, ...] = (),
+) -> dict[tuple[str, ...], dict[int, int]]:
+    """Recursively allocate counts for each factor combination per block."""
+    current = factors[0]
+    level_counts = df_subset[current].value_counts(sort=False, dropna=False).to_dict()
+    level_alloc = _allocate_matrix(
+        block_totals,
+        level_counts,
+        rng,
+        context="/".join(prefix) if prefix else factors[0],
+    )
+
+    if len(factors) == 1:
+        leaf_targets = {}
+        for level in level_counts:
+            leaf = prefix + (level,)
+            leaf_targets[leaf] = {
+                block: level_alloc[block][level] for block in block_totals.keys()
+            }
+        return leaf_targets
+
+    leaf_targets = {}
+    for level in level_counts:
+        sub_df = df_subset[df_subset[current] == level]
+        sub_block_totals = {
+            block: level_alloc[block][level] for block in block_totals.keys()
+        }
+        sub_targets = _hierarchical_leaf_targets(
+            sub_df,
+            factors[1:],
+            sub_block_totals,
+            rng,
+            prefix=prefix + (level,),
+        )
+        leaf_targets.update(sub_targets)
+
+    return leaf_targets
+
+
+def assign_factor_targets_to_blocks(
+    work: pd.DataFrame,
+    strat_cols: list[str],
+    block_sizes: dict[int, int],
+    rng: np.random.Generator,
+) -> pd.DataFrame:
+    """Assign block ids by matching hierarchical proportional targets."""
+    block_ids = list(block_sizes.keys())
+
+    work["block"] = -1
+
+    # Compute exact per-block targets for every factor combination using
+    # hierarchical proportional allocation.
+    leaf_targets = _hierarchical_leaf_targets(work, strat_cols, block_sizes, rng)
+
+    # Group sample indices by full factor combination and assign per block.
+    grouped = work.groupby(strat_cols, sort=False)
+    for key, group_df in grouped:
+        leaf = key if isinstance(key, tuple) else (key,)
+        indices = group_df.index.to_numpy().copy()
+        rng.shuffle(indices)
+
+        offset = 0
+        block_plan = leaf_targets[leaf]
+        for b in block_ids:
+            take = block_plan[b]
+            if take > 0:
+                chosen = indices[offset : offset + take]
+                work.loc[chosen, "block"] = b
+                offset += take
+
+        if offset != len(indices):
+            raise StudyDesignerError(
+                "internal assignment mismatch while distributing samples "
+                f"for factor level {leaf}."
+            )
+
+    return work
+
+
+def shuffle_within_blocks(work: pd.DataFrame, rng: np.random.Generator) -> pd.DataFrame:
+    """Shuffle order inside blocks and add 1-based position within each block."""
+
+    # Randomize sample order within each block (reproducible with --seed).
+    # This keeps block composition intact while removing any deterministic
+    # ordering that comes from allocation steps.
+    work["_within_block_shuffle"] = rng.random(len(work))
+    work = work.sort_values(["block", "_within_block_shuffle"]).copy()
+
+    # Position of each sample inside its block after shuffling (1-based).
+    work["block_position"] = work.groupby("block").cumcount() + 1
+
+    return work
+
+
+def assign_blocks(
+    df: pd.DataFrame,
+    factors: list[str],
+    block_size: int,
+    seed: int,
+) -> pd.DataFrame:
+    """Orchestrate full assignment pipeline."""
+    rng = np.random.default_rng(seed)
+
+    work, strat_cols = normalize_factor_columns(df, factors)
+    block_sizes = compute_block_sizes(len(work), block_size)
+    work = assign_factor_targets_to_blocks(work, strat_cols, block_sizes, rng)
+    work = shuffle_within_blocks(work, rng)
+
+    # Drop internal stratification columns
+    work.drop(columns=strat_cols + ["_within_block_shuffle"], inplace=True)
+    return work
+
+
+def build_summary_tables(
+    df: pd.DataFrame,
+    factors: list[str],
+) -> tuple[pd.DataFrame, pd.DataFrame, pd.Series, int, int]:
+    """Build summary tables for printing and optional export."""
+    assigned = df[df["block"] >= 0]
+    cols = ["block"] + factors
+    counts = assigned.groupby(cols).size().reset_index(name="n")
+
+    pivot = counts.pivot_table(index=factors, columns="block", values="n", fill_value=0)
+    pivot = pivot.astype(int)
+
+    sizes = assigned.groupby("block").size()
+    last_block = df["block"].max()
+    last_size = int((df["block"] == last_block).sum())
+    full_blocks = df[df["block"] < last_block].groupby("block").size()
+    full_block_size = int(full_blocks.iloc[0]) if len(full_blocks) else 0
+
+    return counts, pivot, sizes, full_block_size, last_size
+
+
+def print_summary(df: pd.DataFrame, factors: list[str], summary_path: str | None = None) -> None:
+    counts, pivot, sizes, full_block_size, last_size = build_summary_tables(df, factors)
+
+    print("\n=== Block assignment counts ===")
+    print(pivot.to_string())
+
+    print("\n=== Block sizes ===")
+    print(sizes.to_string())
+
+    if full_block_size:
+        print(f"\nFull block size : {full_block_size}")
+    print(f"Last block size : {last_size}")
+
+    if summary_path:
+        counts.to_csv(summary_path, sep="\t", index=False)
+        print(f"\nSummary written to: {summary_path}")
+
+
+def main():
+    cfg = parse_args()
+    try:
+        df = load_and_prepare_input(cfg.input_path)
+        factors, sample_col = resolve_column_args(
+            df,
+            cfg.factors,
+            cfg.sample_col,
+            cfg.columns_mode,
+        )
+        validate_inputs(df, factors, sample_col, cfg.block_size)
+
+        df = add_original_index(df)
+
+        result = assign_blocks(
+            df,
+            factors=factors,
+            block_size=cfg.block_size,
+            seed=cfg.seed,
+        )
+
+        result.to_csv(cfg.output_path, sep="\t", index=False)
+        print(f"Output written to: {cfg.output_path}")
+
+        print_summary(result, factors, cfg.summary_path)
+    except StudyDesignerError as exc:
+        sys.exit(f"ERROR: {exc}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/tools/misc/stratified_block_randomization.xml b/tools/misc/stratified_block_randomization.xml
new file mode 100644
index 000000000..acdb94ade
--- /dev/null
+++ b/tools/misc/stratified_block_randomization.xml
@@ -0,0 +1,124 @@
+<tool id="stratified_block_randomization" name="stratified block randomization" version="@TOOL_VERSION@+galaxy@VERSION_SUFFIX@" profile="20.01" license="MIT">
+    <description>for hierarchical block-based balancing with proportional matching via largest-remainder rounding.</description>
+    <macros>
+        <token name="@TOOL_VERSION@">0.1.0</token>
+        <token name="@VERSION_SUFFIX@">0</token>
+    </macros>
+    <creator>
+      <person
+          givenName="Helge"
+          familyName="Hecht"
+          url="https://github.com/hechth"
+          identifier="0000-0001-6744-996X" />
+      <organization
+          url="https://www.recetox.muni.cz/"
+          email="GalaxyToolsDevelopmentandDeployment@space.muni.cz"
+          name="RECETOX MUNI" />
+    </creator>
+    <!-- TODO: please annotate this tool with topics and operations from http://edamontology.org -->
+    <!-- TODO: for more information see: https://galaxy-iuc-standards.readthedocs.io/en/latest/best_practices/tool_xml.html#edam-topics-and-operations -->
+    <edam_topics>
+        <edam_topic>topic_3678</edam_topic>
+        <edam_topic>topic_3391</edam_topic>
+    </edam_topics>
+    <edam_operations>
+        <edam_operation>operation_3802</edam_operation>
+        <edam_operation>operation_2430</edam_operation>
+    </edam_operations>
+    <requirements>
+        <requirement type="package" version="2.4.6">numpy</requirement>
+        <requirement type="package" version="3.0.3">pandas</requirement>
+    </requirements>
+    <required_files>
+        <include path="stratified_block_randomization.py"/>
+    </required_files>
+    <command detect_errors="exit_code"><![CDATA[
+        python3 '$__tool_directory__/stratified_block_randomization.py' 
+            --input '$input' 
+            --block_size '$block_size' 
+            --columns_mode indices 
+            --factors '$factors' 
+            --sample_col '$sample_col' 
+            --seed '$seed' 
+            --output '$output' 
+            #if $write_summary
+                --summary '$summary'
+            #end if
+    ]]></command>
+    <inputs>
+        <param name="input" type="data" format="tabular,tsv" label="Input sample table" help="Tabular file containing one row per sample and one column per sample attribute."/>
+        <param name="sample_col" type="data_column" data_ref="input" use_header_names="true" label="Sample identifier column" help="Column containing unique sample identifiers."/>
+        <param name="factors" type="data_column" data_ref="input" use_header_names="true" multiple="true" min="1" label="Balancing factor columns" help="Select one or more columns used for balancing. The order matters: the first selected factor has the highest priority, the second is balanced within the first, and so on."/>
+        <param name="block_size" type="integer" min="1" value="100" label="Block size" help="Target number of samples per block. The last block may contain fewer samples."/>
+        <param name="seed" type="integer" value="42" label="Random seed" help="Seed used for reproducible random assignment and within-block shuffling."/>
+        <param name="write_summary" type="boolean" checked="true" truevalue="TRUE" falsevalue="FALSE" label="Generate summary output" help="Create an additional summary table with counts per block and factor combination."/>
+    </inputs>
+    <outputs>
+        <data name="output" format="tabular"/>
+        <data name="summary" format="tabular" label="${tool.name} on ${on_string}: summary">
+            <filter>write_summary</filter>
+        </data>
+    </outputs>
+    <tests>
+        <test>
+            <param name="input" value="samples.tsv"/>
+            <param name="sample_col" value="1"/>
+            <param name="factors" value="2,3"/>
+            <output name="output">
+                <assert_contents>
+                    <has_n_lines n="510"/>
+                    <has_n_columns n="6"/>
+                </assert_contents>
+            </output>
+            <output name="summary">
+                <assert_contents>
+                    <has_n_lines n="117"/>
+                </assert_contents>
+            </output>
+        </test>
+    </tests>
+    <help><![CDATA[
+
+.. class:: infomark
+
+**What it does**
+
+This tool assigns samples from a metadata table into balanced study blocks.
+
+Samples are split into blocks of a user-defined size. The distribution is balanced hierarchically according to the selected factor columns:
+
+- the first factor is balanced across all blocks,
+- the second factor is balanced within each level of the first factor,
+- additional factors are balanced in the same nested way.
+
+The tool uses proportional allocation with integer rounding, then randomly shuffles sample order within each block. The output table contains the original sample information together with the assigned block and the position of each sample inside that block.
+
+Usage
+.....
+
+Use this tool when you need to prepare balanced sample batches for downstream experimental processing, for example to distribute cases and controls evenly across analytical runs or laboratory batches.
+
+
+**Input**
+
+- **Input sample table**: Tabular file with one row per sample.
+- **Sample identifier column**: Column containing the sample names or IDs.
+- **Balancing factor columns**: One or more metadata columns used for hierarchical balancing.
+- **Block size**: Desired number of samples in each block.
+- **Random seed**: Optional seed for reproducible randomization.
+
+
+**Output**
+
+- **Assigned blocks table**: The input table with additional columns for `original_index`, `block`, and `block_position`.
+- **Summary table**: Optional table containing counts for each block and factor combination.
+
+
+    ]]></help>
+    <citations>
+        <citation type="doi">10.5281/zenodo.3509134</citation>
+        <citation type="doi">10.25080/Majora-92bf1922-00a</citation>
+        <citation type="doi">10.1007/978-1-4612-1192-1</citation>
+        <citation type="doi">10.1007/978-1-4419-9246-8</citation>
+    </citations>
+</tool>
\ No newline at end of file
diff --git a/tools/misc/test-data/samples.tsv b/tools/misc/test-data/samples.tsv
new file mode 100644
index 000000000..54abd4c0d
--- /dev/null
+++ b/tools/misc/test-data/samples.tsv
@@ -0,0 +1,510 @@
+sample	group	status
+993fc6dc-120b-42a7-8c42-756e3ff8a03c	group_01	status_01
+57916f7b-048c-4eef-a004-750be23ae7bc	group_01	status_01
+58453668-a4ad-4afd-b975-787017fe13cb	group_01	status_01
+dd31c1a8-d048-4155-94fb-f6d530db865b	group_01	status_01
+ff868ab8-c6c5-462b-a6d1-18061ed3f7dc	group_01	status_02
+cd1441a7-7d7f-44f4-a824-0d5a74ca2d9a	group_01	status_02
+b8386d0c-6316-40e9-a24f-99ce62cdf73d	group_01	status_01
+88948ce3-1d4c-4853-9444-72498df2a40f	group_01	status_02
+d2a36738-4ec6-481f-9ff2-13b48758c6d9	group_01	status_02
+8270584a-2481-4d1c-a209-660620f9fcc1	group_01	status_01
+e082d6e1-bef4-47e4-accb-8fa662757225	group_01	status_01
+d8d5227d-7cb7-4d9b-9a5c-052d77abe4ac	group_01	status_02
+8ce67629-9e19-41dd-bd11-41248f203d09	group_01	status_02
+c421df70-e916-48fa-a27a-703c9e501f63	group_01	status_01
+f7b64a4a-4cd6-4b8a-92a8-aeb2dfa0c9ac	group_01	status_02
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