diff --git a/src/depiction_targeted_preproc/workflow/prepare_pipeline/standardize_table.py b/src/depiction_targeted_preproc/workflow/prepare_pipeline/standardize_table.py
deleted file mode 100644
index b771732..0000000
--- a/src/depiction_targeted_preproc/workflow/prepare_pipeline/standardize_table.py
+++ /dev/null
@@ -1,31 +0,0 @@
-from pathlib import Path
-
-import polars as pl
-
-
-def standardize_table(input_df: pl.DataFrame) -> pl.DataFrame:
-    # TODO this is a total hack for a quick setup
-    mapping = {}
-    for column in input_df.columns:
-        if column.lower() in ["marker", "label"]:
-            mapping[column] = "label"
-        elif column.lower() in ["mass", "m/z", "pc-mt (m+h)+"]:
-            mapping[column] = "mass"
-        elif column.lower() in ["tol"]:
-            mapping[column] = "tol"
-    output_df = input_df.rename(mapping)
-
-    if "tol" not in output_df:
-        # TODO make configurable
-        output_df = output_df.with_columns([pl.Series("tol", [0.2] * len(output_df))])
-    return output_df
-
-
-def copy_standardized_table(input_csv: Path, output_csv: Path):
-    input_df = pl.read_csv(input_csv)
-    write_standardized_table(input_df, output_csv)
-
-
-def write_standardized_table(input_df: pl.DataFrame, output_csv: Path) -> None:
-    output_df = standardize_table(input_df)
-    output_df.write_csv(output_csv)
diff --git a/src/depiction_targeted_preproc/workflow/proc/mass_list_preparation.py b/src/depiction_targeted_preproc/workflow/proc/mass_list_preparation.py
deleted file mode 100644
index 9dce722..0000000
--- a/src/depiction_targeted_preproc/workflow/proc/mass_list_preparation.py
+++ /dev/null
@@ -1,62 +0,0 @@
-from __future__ import annotations
-
-from pathlib import Path
-
-import cyclopts
-import polars as pl
-
-app = cyclopts.App()
-
-COLUMN_NAMES = {
-    "mass": {"m/z", "mass", "pc-mt (m+h)+"},
-    "label": {"marker", "label"},
-    "tol": {"tol", "tolerance"},
-}
-
-
-def identify_column_correspondence(raw_df: pl.DataFrame) -> dict[str, str]:
-    identified_columns = {}
-    for column_name in raw_df.columns:
-        for key, values in COLUMN_NAMES.items():
-            if column_name.lower() in values:
-                if key not in identified_columns:
-                    identified_columns[key] = column_name
-                else:
-                    raise ValueError(
-                        f"Column {column_name} is ambiguous, it could be {key} or {identified_columns[key]}"
-                    )
-    required_columns = {"mass", "label"}
-    missing_columns = required_columns - set(identified_columns.keys())
-    if missing_columns:
-        raise ValueError(f"Missing columns: {missing_columns}")
-    # reverse the mapping
-    return {original: target for target, original in identified_columns.items()}
-
-
-@app.default
-def mass_list_preparation(
-    raw_csv: Path,
-    out_csv: Path,
-) -> None:
-    raw_df = pl.read_csv(raw_csv)
-
-    # identify columns
-    column_correspondence = identify_column_correspondence(raw_df)
-
-    # rename columns (and drop the rest)
-    renamed = (
-        raw_df.select(column_correspondence.values())
-        .rename(column_correspondence)
-        .select(sorted(column_correspondence.values()))
-    )
-
-    # add tol column if not present
-    if "tol" not in renamed.columns:
-        renamed = renamed.with_column("tol", pl.Null)
-
-    # write the results
-    renamed.write_csv(out_csv)
-
-
-if __name__ == "__main__":
-    app()