diff --git a/examples/ripley_l_params.json b/examples/ripley_l_params.json
new file mode 100644
index 00000000..c666f59a
--- /dev/null
+++ b/examples/ripley_l_params.json
@@ -0,0 +1,14 @@
+{
+  "input_data": "data/mock_input.pickle",
+  "radii": [0, 50, 100, 150, 200, 250, 300],
+  "annotation": "renamed_phenotypes",
+  "phenotypes": ["B cells", "CD8 T cells"],
+  "regions": null,
+  "n_simulations": 100,
+  "area": null,
+  "seed": 42,
+  "spatial_key": "spatial",
+  "edge_correction": true,
+  "output_path": "data/ripley_output.pickle"
+}
+
diff --git a/src/spac/templates/__init__.py b/src/spac/templates/__init__.py
new file mode 100644
index 00000000..89c61771
--- /dev/null
+++ b/src/spac/templates/__init__.py
@@ -0,0 +1,13 @@
+"""
+Canonical SPAC template sub‑package.
+
+Each template is a self‑contained module that
+  • reads parameters from JSON/dict
+  • runs a SPAC analysis function
+  • returns / saves results
+
+Available templates
+-------------------
+- ripley_l_template.run_from_json
+"""
+
diff --git a/src/spac/templates/ripley_l_template.py b/src/spac/templates/ripley_l_template.py
new file mode 100644
index 00000000..f742f1f4
--- /dev/null
+++ b/src/spac/templates/ripley_l_template.py
@@ -0,0 +1,242 @@
+"""
+Platform-agnostic Ripley-L template converted from NIDAP.
+Maintains the exact logic from the NIDAP template.
+
+Usage
+-----
+>>> from spac.templates.ripley_l_template import run_from_json
+>>> run_from_json("examples/ripley_l_params.json")
+"""
+import json
+import sys
+from pathlib import Path
+from typing import Any, Dict, Union, List
+import pandas as pd
+
+# Add parent directory to path for imports
+sys.path.append(str(Path(__file__).parent.parent))
+
+from spac.spatial_analysis import ripley_l
+from spac.templates.template_utils import (
+    load_input,
+    save_outputs,
+    parse_params,
+    text_to_value,
+)
+
+
+# def _prepare_ripley_uns_for_h5ad(adata) -> None:
+#     """
+#     Minimal fix for ripley_l results serialization.
+#     Converts object columns to appropriate types for H5AD storage.
+#     """
+#     if "ripley_l" not in adata.uns:
+#         return
+
+#     rl = adata.uns.get("ripley_l")
+#     if isinstance(rl, pd.DataFrame):
+#         # Create a copy to avoid modifying the original
+#         clean_df = rl.copy()
+
+#         # Process each object column
+#         for col in clean_df.columns:
+#             if clean_df[col].dtype == "object":
+#                 # Try to convert to numeric
+#                 try:
+#                     clean_df[col] = pd.to_numeric(
+#                         clean_df[col], errors='raise'
+#                     )
+#                 except Exception:
+#                     # If that fails, convert to string
+#                     clean_df[col] = clean_df[col].astype(str)
+
+#         # Replace with cleaned version
+#         adata.uns["ripley_l"] = clean_df
+
+def _prepare_ripley_uns_for_h5ad(adata) -> None:
+    """
+    Enhanced fix for ripley_l results serialization.
+    Ensures proper data types and structure for H5AD storage.
+    """
+    if "ripley_l" not in adata.uns:
+        return
+
+    rl = adata.uns.get("ripley_l")
+    
+    # Handle case where ripley_l might be a string (corrupted data)
+    if isinstance(rl, str):
+        print(f"Warning: ripley_l data appears to be corrupted (string): {rl[:100]}...")
+        # Try to reconstruct or remove corrupted data
+        del adata.uns["ripley_l"]
+        print("Removed corrupted ripley_l data from adata.uns")
+        return
+    
+    # Handle DataFrame case
+    if isinstance(rl, pd.DataFrame):
+        # Create a copy to avoid modifying the original
+        clean_df = rl.copy()
+
+        # Process each object column
+        for col in clean_df.columns:
+            if clean_df[col].dtype == "object":
+                # Check if column contains dictionaries or other complex objects
+                sample_val = clean_df[col].iloc[0] if len(clean_df) > 0 else None
+                
+                if isinstance(sample_val, dict):
+                    # Convert dict columns to JSON strings for H5AD compatibility
+                    clean_df[col] = clean_df[col].apply(lambda x: json.dumps(x) if isinstance(x, dict) else str(x))
+                else:
+                    # Try to convert to numeric first
+                    try:
+                        clean_df[col] = pd.to_numeric(clean_df[col], errors='raise')
+                    except (ValueError, TypeError):
+                        # If that fails, convert to string
+                        clean_df[col] = clean_df[col].astype(str)
+
+        # Replace with cleaned version
+        adata.uns["ripley_l"] = clean_df
+        print(f"Cleaned ripley_l DataFrame with shape {clean_df.shape}")
+        
+    # Handle dictionary case
+    elif isinstance(rl, dict):
+        clean_dict = {}
+        for key, value in rl.items():
+            if isinstance(value, (pd.DataFrame, pd.Series)):
+                # Convert pandas objects to dictionaries
+                clean_dict[key] = value.to_dict() if hasattr(value, 'to_dict') else str(value)
+            elif isinstance(value, (list, tuple)):
+                # Ensure lists contain serializable types
+                clean_dict[key] = [str(item) if not isinstance(item, (int, float, str, bool)) else item for item in value]
+            elif isinstance(value, (np.ndarray,)):
+                # Convert numpy arrays to lists
+                clean_dict[key] = value.tolist()
+            else:
+                clean_dict[key] = value
+        
+        adata.uns["ripley_l"] = clean_dict
+        print(f"Cleaned ripley_l dictionary with keys: {list(clean_dict.keys())}")
+    
+    else:
+        print(f"Warning: Unexpected ripley_l data type: {type(rl)}")
+        # Convert to string representation as fallback
+        adata.uns["ripley_l"] = str(rl)
+
+
+def run_from_json(
+    json_path: Union[str, Path, Dict[str, Any]]
+) -> Dict[str, str]:
+    """
+    Execute Ripley-L analysis with parameters from JSON.
+    Replicates the NIDAP template functionality exactly.
+
+    Parameters
+    ----------
+    json_path : str, Path, or dict
+        Path to JSON file, JSON string, or parameter dictionary
+
+    Returns
+    -------
+    dict
+        Dictionary of saved file paths
+    """
+    # Parse parameters from JSON
+    params = parse_params(json_path)
+
+    # Load the upstream analysis data
+    adata = load_input(params["Upstream_Analysis"])
+
+    # Extract parameters
+    radii = params["Radii"]
+    annotation = params["Annotation"]
+    phenotypes = [params["Center_Phenotype"], params["Neighbor_Phenotype"]]
+    regions = params.get("Stratify_By", "None")
+    n_simulations = params.get("Number_of_Simulations", 1)
+    area = params.get("Area", "None")
+    seed = params.get("Seed", 42)
+    spatial_key = params.get("Spatial_Key", "spatial")
+    edge_correction = params.get("Edge_Correction", True)
+
+    # Process parameters
+    regions = text_to_value(
+        regions,
+        default_none_text="None"
+    )
+
+    area = text_to_value(
+        area,
+        default_none_text="None",
+        value_to_convert_to=None,
+        to_float=True,
+        param_name='Area'
+    )
+
+    # Convert radii to floats
+    radii = _convert_to_floats(radii)
+
+    # Run the analysis
+    ripley_l(
+        adata,
+        annotation=annotation,
+        phenotypes=phenotypes,
+        distances=radii,
+        regions=regions,
+        n_simulations=n_simulations,
+        area=area,
+        seed=seed,
+        spatial_key=spatial_key,
+        edge_correction=edge_correction
+    )
+
+    # Fix ripley_l results before saving
+    _prepare_ripley_uns_for_h5ad(adata)
+
+    # Save outputs
+    outfile = params.get("Output_File", "transform_output.h5ad")
+    saved_files = save_outputs({outfile: adata})
+
+    print(f"Ripley-L completed → {saved_files[outfile]}")
+    print(adata)
+    return saved_files
+
+
+def _convert_to_floats(text_list: List[Any]) -> List[float]:
+    """
+    Convert list of text values to floats.
+    Exact copy from NIDAP template.
+
+    Parameters
+    ----------
+    text_list : list
+        List of values to convert
+
+    Returns
+    -------
+    list
+        List of float values
+
+    Raises
+    ------
+    ValueError
+        If any value cannot be converted to float
+    """
+    float_list = []
+    for value in text_list:
+        try:
+            float_list.append(float(value))
+        except ValueError:
+            msg = f"Failed to convert the radius: '{value}' to float."
+            raise ValueError(msg)
+    return float_list
+
+
+# CLI interface
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python ripley_l_template.py <params.json>")
+        sys.exit(1)
+
+    saved_files = run_from_json(sys.argv[1])
+
+    print("\nOutput files:")
+    for filename, filepath in saved_files.items():
+        print(f"  {filename}: {filepath}")
diff --git a/src/spac/templates/template_utils.py b/src/spac/templates/template_utils.py
new file mode 100644
index 00000000..d1b6511e
--- /dev/null
+++ b/src/spac/templates/template_utils.py
@@ -0,0 +1,312 @@
+from pathlib import Path
+import pickle
+from typing import Any, Dict, Union, Optional
+import json
+import anndata as ad
+
+
+def load_input(file_path: Union[str, Path]):
+    """
+    Load input data from either h5ad or pickle file.
+    
+    Parameters
+    ----------
+    file_path : str or Path
+        Path to input file (h5ad or pickle)
+    
+    Returns
+    -------
+    Loaded data object (typically AnnData)
+    """
+    path = Path(file_path)
+    
+    if not path.exists():
+        raise FileNotFoundError(f"Input file not found: {file_path}")
+
+    # Check file extension
+    suffix = path.suffix.lower()
+
+    if suffix in ['.h5ad', '.h5']:
+        # Load h5ad file
+        try:
+            import anndata as ad
+            return ad.read_h5ad(path)
+        except ImportError:
+            raise ImportError(
+                "anndata package required to read h5ad files"
+            )
+        except Exception as e:
+            raise ValueError(f"Error reading h5ad file: {e}")
+
+    elif suffix in ['.pickle', '.pkl', '.p']:
+        # Load pickle file
+        with path.open('rb') as fh:
+            return pickle.load(fh)
+
+    else:
+        # Try to detect file type by content
+        try:
+            # First try h5ad
+            import anndata as ad
+            return ad.read_h5ad(path)
+        except Exception:
+            # Fall back to pickle
+            try:
+                with path.open('rb') as fh:
+                    return pickle.load(fh)
+            except Exception as e:
+                raise ValueError(
+                    f"Unable to load file '{file_path}'. "
+                    f"Supported formats: h5ad, pickle. Error: {e}"
+                )
+
+
+def save_outputs(outputs: Dict[str, Any],
+                 output_dir: Union[str, Path] = ".") -> Dict[str, str]:
+    """
+    Save multiple outputs to files and return a dict {filename: absolute_path}.
+    (Always a dict, even if just one file.)
+    
+    Parameters
+    ----------
+    outputs : dict
+        Dictionary where:
+        - key: filename (with extension)
+        - value: object to save
+    output_dir : str or Path
+        Directory to save files
+    
+    Returns
+    -------
+    dict
+        Dictionary of saved file paths
+    
+    Example
+    -------
+    >>> outputs = {
+    ...     "adata.h5ad": adata,
+    ...     "results.csv": results_df,
+    ...     "adata.pickle": adata
+    ... }
+    >>> saved = save_outputs(outputs, "results/")
+    """
+    output_dir = Path(output_dir)
+    output_dir.mkdir(parents=True, exist_ok=True)
+    
+    saved_files = {}
+    
+    for filename, obj in outputs.items():
+        filepath = output_dir / filename
+        
+        # Save based on file extension
+        if filename.endswith('.csv'):
+            obj.to_csv(filepath, index=False)
+        elif filename.endswith('.h5ad'):
+            ad.write(filepath)
+        elif filename.endswith(('.pickle', '.pkl')):
+            with open(filepath, 'wb') as f:
+                pickle.dump(obj, f)
+        elif hasattr(obj, "savefig"):
+            obj.savefig(filepath.with_suffix('.png'))
+            filepath = filepath.with_suffix('.png')
+        else:
+            # Default to pickle
+            with open(filepath, 'wb') as f:
+                pickle.dump(obj, f)
+        
+        saved_files[filename] = str(filepath.resolve())
+        print(f"Saved: {filepath}")
+    
+    return saved_files
+
+
+def parse_params(
+    json_input: Union[str, Path, Dict[str, Any]]
+) -> Dict[str, Any]:
+    """
+    Parse parameters from JSON file, string, or dict.
+    
+    Parameters
+    ----------
+    json_input : str, Path, or dict
+        JSON file path, JSON string, or dictionary
+    
+    Returns
+    -------
+    dict
+        Parsed parameters
+    """
+    if isinstance(json_input, dict):
+        return json_input
+    
+    if isinstance(json_input, (str, Path)):
+        path = Path(json_input)
+        
+        # Check if it's a file path
+        if path.exists() or str(json_input).endswith('.json'):
+            with open(path, 'r') as file:
+                return json.load(file)
+        else:
+            # It's a JSON string
+            return json.loads(str(json_input))
+    
+    raise TypeError(
+        "json_input must be dict, JSON string, or path to JSON file"
+    )
+
+
+def text_to_value(
+    var: Any,
+    default_none_text: str = "None",
+    value_to_convert_to: Any = None,
+    to_float: bool = False,
+    to_int: bool = False,
+    param_name: str = ''
+):
+    """
+    Converts a string to a specified value or type. Handles conversion to
+    float or integer and provides a default value if the input string
+    matches a specified 'None' text.
+
+    Parameters
+    ----------
+    var : str
+        The input string to be converted.
+    default_none_text : str, optional
+        The string that represents a 'None' value. If `var` matches this
+        string, it will be converted to `value_to_convert_to`.
+        Default is "None".
+    value_to_convert_to : any, optional
+        The value to assign to `var` if it matches `default_none_text` or
+        is an empty string. Default is None.
+    to_float : bool, optional
+        If True, attempt to convert `var` to a float. Default is False.
+    to_int : bool, optional
+        If True, attempt to convert `var` to an integer. Default is False.
+    param_name : str, optional
+        The name of the parameter, used in error messages for conversion
+        failures. Default is ''.
+
+    Returns
+    -------
+    any
+        The converted value, which may be the original string, a float,
+        an integer, or the specified `value_to_convert_to`.
+
+    Raises
+    ------
+    ValueError
+        If `to_float` or `to_int` is set to True and conversion fails.
+
+    Notes
+    -----
+    - If both `to_float` and `to_int` are set to True, the function will
+      prioritize conversion to float.
+    - If the string `var` matches `default_none_text` or is an empty
+      string, `value_to_convert_to` is returned.
+
+    Examples
+    --------
+    Convert a string representing a float:
+
+    >>> text_to_value("3.14", to_float=True)
+    3.14
+
+    Handle a 'None' string:
+
+    >>> text_to_value("None", value_to_convert_to=None)
+    None
+
+    Convert a string to an integer:
+
+    >>> text_to_value("42", to_int=True)
+    42
+
+    Handle invalid conversion:
+
+    >>> text_to_value("abc", to_int=True, param_name="test_param")
+    Error: can't convert test_param to integer. Received:"abc"
+    'abc'
+    """
+    none_condition = (
+        var.lower().strip() == default_none_text.lower().strip() or
+        var.strip() == ''
+    )
+    
+    if none_condition:
+        var = value_to_convert_to
+
+    elif to_float:
+        try:
+            var = float(var)
+        except ValueError:
+            error_msg = (
+                f'Error: can\'t convert {param_name} to float. '
+                f'Received:"{var}"'
+            )
+            raise ValueError(error_msg)
+
+    elif to_int:
+        try:
+            var = int(var)
+        except ValueError:
+            error_msg = (
+                f'Error: can\'t convert {param_name} to integer. '
+                f'Received:"{var}"'
+            )
+            raise ValueError(error_msg)
+
+    return var
+
+
+def convert_pickle_to_h5ad(
+    pickle_path: Union[str, Path],
+    h5ad_path: Optional[Union[str, Path]] = None
+) -> str:
+    """
+    Convert a pickle file containing AnnData to h5ad format.
+    
+    Parameters
+    ----------
+    pickle_path : str or Path
+        Path to input pickle file
+    h5ad_path : str or Path, optional
+        Path for output h5ad file. If None, uses same name with .h5ad
+        extension
+    
+    Returns
+    -------
+    str
+        Path to saved h5ad file
+    """
+    pickle_path = Path(pickle_path)
+    
+    if not pickle_path.exists():
+        raise FileNotFoundError(f"Pickle file not found: {pickle_path}")
+    
+    # Load from pickle
+    with pickle_path.open('rb') as fh:
+        adata = pickle.load(fh)
+    
+    # Check if it's AnnData
+    try:
+        import anndata as ad
+        if not isinstance(adata, ad.AnnData):
+            raise TypeError(
+                f"Loaded object is not AnnData, got {type(adata)}"
+            )
+    except ImportError:
+        raise ImportError(
+            "anndata package required for conversion to h5ad"
+        )
+    
+    # Determine output path
+    if h5ad_path is None:
+        h5ad_path = pickle_path.with_suffix('.h5ad')
+    else:
+        h5ad_path = Path(h5ad_path)
+    
+    # Save as h5ad
+    adata.write_h5ad(h5ad_path)
+    
+    return str(h5ad_path)
\ No newline at end of file
diff --git a/src/spac/templates/visualize_ripley_template.py b/src/spac/templates/visualize_ripley_template.py
new file mode 100644
index 00000000..5085b039
--- /dev/null
+++ b/src/spac/templates/visualize_ripley_template.py
@@ -0,0 +1,101 @@
+"""
+Platform-agnostic Visualize Ripley L template converted from NIDAP.
+Maintains the exact logic from the NIDAP template.
+
+Usage
+-----
+>>> from spac.templates.visualize_ripley_template import run_from_json
+>>> run_from_json("examples/visualize_ripley_params.json")
+"""
+import json
+import sys
+from pathlib import Path
+from typing import Any, Dict, Union, List, Optional
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Add parent directory to path for imports
+sys.path.append(str(Path(__file__).parent.parent))
+
+from spac.visualization import plot_ripley_l
+from spac.templates.template_utils import (
+    load_input,
+    save_outputs,
+    parse_params,
+    text_to_value,
+)
+
+
+def run_from_json(
+    json_path: Union[str, Path, Dict[str, Any]]
+) -> Dict[str, str]:
+    """
+    Execute Visualize Ripley L analysis with parameters from JSON.
+    Replicates the NIDAP template functionality exactly.
+    
+    Parameters
+    ----------
+    json_path : str, Path, or dict
+        Path to JSON file, JSON string, or parameter dictionary
+    
+    Returns
+    -------
+    dict
+        Dictionary of saved file paths
+    """
+    # Parse parameters from JSON
+    params = parse_params(json_path)
+    
+    # Load the upstream analysis data
+    adata = load_input(params["Upstream_Analysis"])
+    
+    # Extract parameters
+    center_phenotype = params["Center_Phenotype"]
+    neighbor_phenotype = params["Neighbor_Phenotype"]
+    plot_specific_regions = params.get("Plot_Specific_Regions", False)
+    regions_labels = params.get("Regions_Labels", [])
+    plot_simulations = params.get("Plot_Simulations", True)
+    
+    # Process regions parameter exactly as in NIDAP template
+    if plot_specific_regions:
+        if len(regions_labels) == 0:
+            raise ValueError(
+                'Please identify at least one region in the '
+                '"Regions Label(s) parameter'
+            )
+    else:
+        regions_labels = None
+    
+    # Run the visualization exactly as in NIDAP template
+    fig, plots_df = plot_ripley_l(
+        adata,
+        phenotypes=(center_phenotype, neighbor_phenotype),
+        regions=regions_labels,
+        sims=plot_simulations,
+        return_df=True
+    )
+
+    plt.show()
+    
+    # Print the dataframe to console
+    print(plots_df.to_string())
+    
+    # Save outputs
+    output_file = params.get("Output_File", "plots.csv")
+    saved_files = save_outputs({output_file: plots_df})
+    
+    print(f"Visualize Ripley L completed → {saved_files[output_file]}")
+    return saved_files
+
+
+# CLI interface
+if __name__ == "__main__":
+    if len(sys.argv) != 2:
+        print("Usage: python visualize_ripley_template.py <params.json>")
+        sys.exit(1)
+    
+    saved_files = run_from_json(sys.argv[1])
+    
+    print("\nOutput files:")
+    for filename, filepath in saved_files.items():
+        print(f"  {filename}: {filepath}")
\ No newline at end of file
diff --git a/tests/templates/__init__.py b/tests/templates/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/tests/templates/test_ripley_l_template.py b/tests/templates/test_ripley_l_template.py
new file mode 100644
index 00000000..f89bfce0
--- /dev/null
+++ b/tests/templates/test_ripley_l_template.py
@@ -0,0 +1,118 @@
+# tests/templates/test_ripley_l_template.py
+"""Unit tests for the Ripley‑L template."""
+
+import json
+import os
+import pickle
+import sys
+import tempfile
+import unittest
+import warnings
+
+import anndata as ad
+import numpy as np
+import pandas as pd
+from pathlib import Path
+
+sys.path.append(
+    os.path.dirname(os.path.realpath(__file__)) + "/../../src"
+)
+
+from spac.templates.ripley_l_template import (
+    run_from_json, 
+    _convert_to_floats
+)
+
+
+def mock_adata(n_cells: int = 10) -> ad.AnnData:
+    """Return a minimal synthetic AnnData for fast tests."""
+    rng = np.random.default_rng(0)
+    obs = pd.DataFrame({
+        "phenotype": ["B cells", "CD8 T cells"] * (n_cells // 2)
+    })
+    x_mat = rng.normal(size=(n_cells, 2))
+    adata = ad.AnnData(X=x_mat, obs=obs)
+    adata.obsm["spatial"] = rng.random((n_cells, 2)) * 50.0
+    return adata
+
+
+class TestRipleyLTemplate(unittest.TestCase):
+    """Unit tests for the Ripley-L template."""
+
+    def setUp(self) -> None:
+        self.tmp_dir = tempfile.TemporaryDirectory()
+        self.in_file = os.path.join(self.tmp_dir.name, "input.h5ad")
+        self.out_file = "output.h5ad"
+
+        # Save minimal mock data
+        mock_adata().write_h5ad(self.in_file)
+
+        # Minimal parameters - match the exact parameter names from template
+        self.params = {
+            "Upstream_Analysis": self.in_file,
+            "Radii": [10, 20],
+            "Annotation": "phenotype",
+            "Center_Phenotype": "B cells",
+            "Neighbor_Phenotype": "CD8 T cells",
+            "Output_File": self.out_file,
+        }
+
+    def tearDown(self) -> None:
+        self.tmp_dir.cleanup()
+
+    def test_ripley_l_analysis_workflow(self) -> None:
+        """Test Ripley-L specific analysis workflow and output validation."""
+        # Suppress warnings for cleaner test output
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+
+            # Test 1: Basic Ripley-L analysis
+            saved_files = run_from_json(self.params)
+            self.assertIn(self.out_file, saved_files)
+            self.assertTrue(os.path.exists(saved_files[self.out_file]))
+
+            # Load and verify Ripley-L specific output structure
+            adata = ad.read_h5ad(saved_files[self.out_file])
+            
+            # Check that ripley_l results exist (key might vary)
+            ripley_keys = [
+                k for k in adata.uns.keys() if 'ripley' in k.lower()
+            ]
+            self.assertTrue(
+                len(ripley_keys) > 0, "No Ripley-L results found in uns"
+            )
+            self.assertEqual(adata.n_obs, 10)
+
+            # Test 2: JSON file input (Ripley-L specific parameters)
+            json_path = os.path.join(self.tmp_dir.name, "params.json")
+            with open(json_path, "w") as f:
+                json.dump(self.params, f)
+            saved_files_json = run_from_json(json_path)
+            self.assertIn(self.out_file, saved_files_json)
+
+            # Test 3: Parameter conversion (Ripley-L specific string 
+            # parameters)
+            params_str = self.params.copy()
+            params_str["Radii"] = ["10", "20.5"]  # String radii
+            params_str["Area"] = "100.0"  # String area
+            params_str["Stratify_By"] = "None"  # Text none value
+            params_str["Number_of_Simulations"] = 100
+            params_str["Seed"] = 42
+            params_str["Edge_Correction"] = True
+            saved_files_str = run_from_json(params_str)
+            self.assertIn(self.out_file, saved_files_str)
+
+    def test_convert_to_floats_error_message(self) -> None:
+        """Test exact error message for invalid radius conversion."""
+        with self.assertRaises(ValueError) as context:
+            _convert_to_floats(["10", "invalid", "20"])
+
+        expected_msg = (
+            "Failed to convert the radius: 'invalid' to float."
+        )
+        actual_msg = str(context.exception)
+        self.assertEqual(expected_msg, actual_msg)
+
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file
diff --git a/tests/templates/test_ripley_l_template_2.py b/tests/templates/test_ripley_l_template_2.py
new file mode 100644
index 00000000..389486d6
--- /dev/null
+++ b/tests/templates/test_ripley_l_template_2.py
@@ -0,0 +1,136 @@
+"""Unit tests for the Ripley-L template."""
+
+import json
+import os
+import pickle
+import sys
+import tempfile
+import unittest
+import warnings
+
+import anndata as ad
+import numpy as np
+import pandas as pd
+
+sys.path.append(
+    os.path.dirname(os.path.realpath(__file__)) + "/../../src"
+)
+
+from spac.templates.ripley_l_template import run_from_json, _convert_to_floats
+
+
+def mock_adata(n_cells: int = 10) -> ad.AnnData:
+    """Return a minimal synthetic AnnData for fast tests."""
+    rng = np.random.default_rng(0)
+    obs = pd.DataFrame({
+        "phenotype": ["B cells", "CD8 T cells"] * (n_cells // 2)
+    })
+    x_mat = rng.normal(size=(n_cells, 2))
+    adata = ad.AnnData(X=x_mat, obs=obs)
+    adata.obsm["spatial"] = rng.random((n_cells, 2)) * 50.0
+    return adata
+
+
+class TestRipleyLTemplate(unittest.TestCase):
+    """Unit tests for the Ripley-L template."""
+
+    def setUp(self) -> None:
+        self.tmp_dir = tempfile.TemporaryDirectory()
+        self.in_file = os.path.join(self.tmp_dir.name, "input.h5ad")
+        # Change to pickle format to avoid h5ad serialization issues
+        self.out_file = "output.pickle"
+
+        # Save minimal mock data
+        mock_adata().write_h5ad(self.in_file)
+
+        # Minimal parameters - use pickle output to avoid serialization issues
+        self.params = {
+            "Upstream_Analysis": self.in_file,
+            "radii": [10, 20],
+            "annotation": "phenotype",
+            "center_phenotype": "B cells",
+            "neighbor_phenotype": "CD8 T cells",
+            "Output_File": self.out_file,
+        }
+
+    def tearDown(self) -> None:
+        self.tmp_dir.cleanup()
+
+    def test_complete_io_workflow(self) -> None:
+        """Single comprehensive I/O test covering all input/output scenarios."""
+        # Suppress warnings for cleaner test output
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+            
+            # Test 1: Dict input with h5ad file, pickle output
+            saved_files = run_from_json(self.params)
+            self.assertIn(self.out_file, saved_files)
+            self.assertTrue(os.path.exists(saved_files[self.out_file]))
+            
+            # Load and verify output structure from pickle
+            with open(saved_files[self.out_file], 'rb') as f:
+                adata = pickle.load(f)
+            # Check that ripley_l results exist (key might vary)
+            ripley_keys = [k for k in adata.uns.keys() if 'ripley' in k.lower()]
+            self.assertTrue(len(ripley_keys) > 0, "No Ripley-L results found in uns")
+            self.assertEqual(adata.n_obs, 10)
+            
+            # Test 2: JSON file input
+            json_path = os.path.join(self.tmp_dir.name, "params.json")
+            with open(json_path, "w") as f:
+                json.dump(self.params, f)
+            saved_files_json = run_from_json(json_path)
+            self.assertIn(self.out_file, saved_files_json)
+            
+            # Test 3: Pickle input file
+            pickle_file = os.path.join(self.tmp_dir.name, "input.pickle")
+            with open(pickle_file, "wb") as f:
+                pickle.dump(mock_adata(), f)
+            params_pickle = self.params.copy()
+            params_pickle["Upstream_Analysis"] = pickle_file
+            saved_files_pickle = run_from_json(params_pickle)
+            self.assertIn(self.out_file, saved_files_pickle)
+            
+            # Test 4: Parameter conversion (string radii, text values)
+            params_str = self.params.copy()
+            params_str["radii"] = ["10", "20.5"]
+            params_str["area"] = "100.0"
+            params_str["stratify_by"] = "None"
+            saved_files_str = run_from_json(params_str)
+            self.assertIn(self.out_file, saved_files_str)
+
+    def test_convert_to_floats_error_message(self) -> None:
+        """Test exact error message for invalid radius conversion."""
+        with self.assertRaises(ValueError) as context:
+            _convert_to_floats(["10", "invalid", "20"])
+        
+        expected_msg = "Failed to convert the radius: 'invalid' to float."
+        actual_msg = str(context.exception)
+        self.assertEqual(expected_msg, actual_msg)
+
+    def test_missing_input_file_error_message(self) -> None:
+        """Test exact error message for missing input file."""
+        params_bad = self.params.copy()
+        params_bad["Upstream_Analysis"] = "/nonexistent/file.h5ad"
+        
+        with self.assertRaises(FileNotFoundError) as context:
+            run_from_json(params_bad)
+        
+        expected_msg = "Input file not found: /nonexistent/file.h5ad"
+        actual_msg = str(context.exception)
+        self.assertEqual(expected_msg, actual_msg)
+
+    def test_invalid_json_input_error_message(self) -> None:
+        """Test exact error message for invalid JSON input type."""
+        from spac.templates.ripley_l_template import _parse_params
+        
+        with self.assertRaises(TypeError) as context:
+            _parse_params(123)  # Invalid type
+        
+        expected_msg = "json_input must be dict, JSON string, or path to JSON file"
+        actual_msg = str(context.exception)
+        self.assertEqual(expected_msg, actual_msg)
+
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file
diff --git a/tests/templates/test_template_utils.py b/tests/templates/test_template_utils.py
new file mode 100644
index 00000000..3472f8d9
--- /dev/null
+++ b/tests/templates/test_template_utils.py
@@ -0,0 +1,256 @@
+# tests/utils/test_template_utils.py
+"""Unit tests for template utilities."""
+
+import json
+import os
+import pickle
+import sys
+import tempfile
+import unittest
+import warnings
+
+import anndata as ad
+import numpy as np
+import pandas as pd
+from pathlib import Path
+
+sys.path.append(
+    os.path.dirname(os.path.realpath(__file__)) + "/../../src"
+)
+
+from spac.templates.template_utils import (
+    load_input,
+    save_outputs,
+    text_to_value,
+    convert_pickle_to_h5ad
+)
+
+
+def mock_adata(n_cells: int = 10) -> ad.AnnData:
+    """Return a minimal synthetic AnnData for fast tests."""
+    rng = np.random.default_rng(0)
+    obs = pd.DataFrame({
+        "cell_type": ["TypeA", "TypeB"] * (n_cells // 2)
+    })
+    x_mat = rng.normal(size=(n_cells, 2))
+    adata = ad.AnnData(X=x_mat, obs=obs)
+    return adata
+
+
+def mock_dataframe(n_rows: int = 5) -> pd.DataFrame:
+    """Return a minimal DataFrame for fast tests."""
+    return pd.DataFrame({
+        "col1": range(n_rows),
+        "col2": [f"value_{i}" for i in range(n_rows)]
+    })
+
+
+class TestTemplateUtils(unittest.TestCase):
+    """Unit tests for template utility functions."""
+
+    def setUp(self) -> None:
+        self.tmp_dir = tempfile.TemporaryDirectory()
+        self.test_adata = mock_adata()
+        self.test_df = mock_dataframe()
+
+    def tearDown(self) -> None:
+        self.tmp_dir.cleanup()
+
+    def test_complete_io_workflow(self) -> None:
+        """Single I/O test covering all input/output scenarios."""
+        # Suppress warnings for cleaner test output
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+
+            # Test 1: Load h5ad file
+            h5ad_path = os.path.join(self.tmp_dir.name, "test.h5ad")
+            self.test_adata.write_h5ad(h5ad_path)
+            loaded_h5ad = load_input(h5ad_path)
+            self.assertEqual(loaded_h5ad.n_obs, 10)
+            self.assertIn("cell_type", loaded_h5ad.obs.columns)
+
+            # Test 2: Load pickle file
+            pickle_path = os.path.join(self.tmp_dir.name, "test.pickle")
+            with open(pickle_path, "wb") as f:
+                pickle.dump(self.test_adata, f)
+            loaded_pickle = load_input(pickle_path)
+            self.assertEqual(loaded_pickle.n_obs, 10)
+
+            # Test 3: Load .pkl extension
+            pkl_path = os.path.join(self.tmp_dir.name, "test.pkl")
+            with open(pkl_path, "wb") as f:
+                pickle.dump(self.test_adata, f)
+            loaded_pkl = load_input(pkl_path)
+            self.assertEqual(loaded_pkl.n_obs, 10)
+
+            # Test 4: Load .p extension
+            p_path = os.path.join(self.tmp_dir.name, "test.p")
+            with open(p_path, "wb") as f:
+                pickle.dump(self.test_adata, f)
+            loaded_p = load_input(p_path)
+            self.assertEqual(loaded_p.n_obs, 10)
+
+            # Test 5: Save outputs - multiple formats
+            outputs = {
+                "result.h5ad": self.test_adata,
+                "data.csv": self.test_df,
+                "adata.pickle": self.test_adata,
+                "adata.pkl": self.test_adata,
+                "other_data": {"key": "value"}  # Defaults to h5ad
+            }
+            saved_files = save_outputs(outputs, self.tmp_dir.name)
+            
+            # Verify all files were saved
+            self.assertEqual(len(saved_files), 5)
+            for filename, filepath in saved_files.items():
+                self.assertTrue(os.path.exists(filepath))
+                self.assertIn(filename, saved_files)
+
+            # Verify CSV content
+            csv_path = saved_files["data.csv"]
+            loaded_df = pd.read_csv(csv_path)
+            self.assertEqual(len(loaded_df), 5)
+            self.assertIn("col1", loaded_df.columns)
+
+            # Test 6: Convert pickle to h5ad
+            pickle_src = os.path.join(self.tmp_dir.name, "convert_src.pickle")
+            with open(pickle_src, "wb") as f:
+                pickle.dump(self.test_adata, f)
+            
+            h5ad_dest = convert_pickle_to_h5ad(pickle_src)
+            self.assertTrue(os.path.exists(h5ad_dest))
+            self.assertTrue(h5ad_dest.endswith(".h5ad"))
+            
+            # Test with custom output path
+            custom_dest = os.path.join(
+                self.tmp_dir.name, "custom_output.h5ad"
+            )
+            h5ad_custom = convert_pickle_to_h5ad(pickle_src, custom_dest)
+            self.assertEqual(h5ad_custom, custom_dest)
+            self.assertTrue(os.path.exists(custom_dest))
+
+            # Test 7: Load file with no extension (content detection)
+            no_ext_path = os.path.join(self.tmp_dir.name, "noextension")
+            with open(no_ext_path, "wb") as f:
+                pickle.dump(self.test_adata, f)
+            loaded_no_ext = load_input(no_ext_path)
+            self.assertEqual(loaded_no_ext.n_obs, 10)
+
+    def test_text_to_value_conversions(self) -> None:
+        """Test all text_to_value conversion scenarios."""
+        # Test 1: Convert to float
+        result = text_to_value("3.14", to_float=True)
+        self.assertEqual(result, 3.14)
+        self.assertIsInstance(result, float)
+
+        # Test 2: Convert to int
+        result = text_to_value("42", to_int=True)
+        self.assertEqual(result, 42)
+        self.assertIsInstance(result, int)
+
+        # Test 3: None text handling
+        result = text_to_value("None", value_to_convert_to=None)
+        self.assertIsNone(result)
+
+        # Test 4: Empty string handling
+        result = text_to_value("", value_to_convert_to=-1)
+        self.assertEqual(result, -1)
+
+        # Test 5: Case insensitive None
+        result = text_to_value("none", value_to_convert_to=0)
+        self.assertEqual(result, 0)
+
+        # Test 6: Custom none text
+        result = text_to_value(
+            "NA", default_none_text="NA", value_to_convert_to=999
+        )
+        self.assertEqual(result, 999)
+
+        # Test 7: No conversion
+        result = text_to_value("keep_as_string")
+        self.assertEqual(result, "keep_as_string")
+        self.assertIsInstance(result, str)
+
+        # Test 8: Whitespace handling
+        result = text_to_value("  None  ", value_to_convert_to=None)
+        self.assertIsNone(result)
+
+    def test_load_input_missing_file_error_message(self) -> None:
+        """Test exact error message for missing input file."""
+        missing_path = "/nonexistent/path/file.h5ad"
+        
+        with self.assertRaises(FileNotFoundError) as context:
+            load_input(missing_path)
+        
+        expected_msg = f"Input file not found: {missing_path}"
+        actual_msg = str(context.exception)
+        self.assertEqual(expected_msg, actual_msg)
+
+    def test_load_input_unsupported_format_error_message(self) -> None:
+        """Test exact error message for unsupported file format."""
+        # Create a text file with unsupported content
+        txt_path = os.path.join(self.tmp_dir.name, "test.txt")
+        with open(txt_path, "w") as f:
+            f.write("This is not a valid data file")
+        
+        with self.assertRaises(ValueError) as context:
+            load_input(txt_path)
+        
+        actual_msg = str(context.exception)
+        self.assertTrue(actual_msg.startswith("Unable to load file"))
+        self.assertIn("Supported formats: h5ad, pickle", actual_msg)
+
+    def test_text_to_value_float_conversion_error_message(self) -> None:
+        """Test exact error message for invalid float conversion."""
+        with self.assertRaises(ValueError) as context:
+            text_to_value(
+                "not_a_number", to_float=True, param_name="test_param"
+            )
+        
+        expected_msg = (
+            'Error: can\'t convert test_param to float. '
+            'Received:"not_a_number"'
+        )
+        actual_msg = str(context.exception)
+        self.assertEqual(expected_msg, actual_msg)
+
+    def test_text_to_value_int_conversion_error_message(self) -> None:
+        """Test exact error message for invalid integer conversion."""
+        with self.assertRaises(ValueError) as context:
+            text_to_value("3.14", to_int=True, param_name="count")
+        
+        expected_msg = (
+            'Error: can\'t convert count to integer. '
+            'Received:"3.14"'
+        )
+        actual_msg = str(context.exception)
+        self.assertEqual(expected_msg, actual_msg)
+
+    def test_convert_pickle_to_h5ad_missing_file_error_message(self) -> None:
+        """Test exact error message for missing pickle file."""
+        missing_pickle = "/nonexistent/file.pickle"
+        
+        with self.assertRaises(FileNotFoundError) as context:
+            convert_pickle_to_h5ad(missing_pickle)
+        
+        expected_msg = f"Pickle file not found: {missing_pickle}"
+        actual_msg = str(context.exception)
+        self.assertEqual(expected_msg, actual_msg)
+
+    def test_convert_pickle_to_h5ad_wrong_type_error_message(self) -> None:
+        """Test exact error message when pickle doesn't contain AnnData."""
+        # Create pickle with wrong type
+        wrong_pickle = os.path.join(self.tmp_dir.name, "wrong_type.pickle")
+        with open(wrong_pickle, "wb") as f:
+            pickle.dump({"not": "anndata"}, f)
+        
+        with self.assertRaises(TypeError) as context:
+            convert_pickle_to_h5ad(wrong_pickle)
+        
+        expected_msg = "Loaded object is not AnnData, got <class 'dict'>"
+        actual_msg = str(context.exception)
+        self.assertEqual(expected_msg, actual_msg)
+
+
+if __name__ == "__main__":
+    unittest.main()
\ No newline at end of file
diff --git a/tests/templates/test_visualize_ripley_template.py b/tests/templates/test_visualize_ripley_template.py
new file mode 100644
index 00000000..2b6037d3
--- /dev/null
+++ b/tests/templates/test_visualize_ripley_template.py
@@ -0,0 +1,189 @@
+# tests/templates/test_visualize_ripley_template.py
+"""Unit tests for the Visualize Ripley L template."""
+
+import json
+import os
+import pickle
+import sys
+import tempfile
+import unittest
+import warnings
+
+import matplotlib
+matplotlib.use("Agg")  # Headless backend for CI
+
+import anndata as ad
+import numpy as np
+import pandas as pd
+from pathlib import Path
+from unittest.mock import patch, MagicMock
+
+sys.path.append(
+    os.path.dirname(os.path.realpath(__file__)) + "/../../src"
+)
+
+from spac.templates.visualize_ripley_template import run_from_json
+
+
+def mock_adata_with_ripley(n_cells: int = 10) -> ad.AnnData:
+    """Return a minimal synthetic AnnData with Ripley L results for tests."""
+    rng = np.random.default_rng(0)
+    obs = pd.DataFrame({
+        "phenotype": ["B cells", "CD8 T cells"] * (n_cells // 2)
+    })
+    x_mat = rng.normal(size=(n_cells, 2))
+    adata = ad.AnnData(X=x_mat, obs=obs)
+    adata.obsm["spatial"] = rng.random((n_cells, 2)) * 50.0
+    
+    # Add mock Ripley L results in the expected format
+    # The key format is important: "ripley_l_phenotype1_phenotype2"
+    adata.uns["ripley_l_B cells_CD8 T cells"] = {
+        "radius": [0, 50, 100],
+        "ripley_l": [0, 1.2, 2.5],
+        "simulations": np.array([
+            [0, 0.8, 1.9], [0, 1.1, 2.3], [0, 1.3, 2.7]
+        ])
+    }
+    return adata
+
+
+class TestVisualizeRipleyTemplate(unittest.TestCase):
+    """Unit tests for the Visualize Ripley L template."""
+
+    def setUp(self) -> None:
+        self.tmp_dir = tempfile.TemporaryDirectory()
+        self.in_file = os.path.join(
+            self.tmp_dir.name, "ripley_output.h5ad"
+        )
+        self.out_file = "plots.csv"
+
+        # Save minimal mock data with Ripley results
+        mock_adata_with_ripley().write_h5ad(self.in_file)
+
+        # Minimal parameters
+        self.params = {
+            "Upstream_Analysis": self.in_file,
+            "Center_Phenotype": "B cells",
+            "Neighbor_Phenotype": "CD8 T cells",
+            "Plot_Specific_Regions": False,
+            "Regions_Label_s_": [],
+            "Plot_Simulations": True,
+            "Output_File": self.out_file,
+        }
+
+    def tearDown(self) -> None:
+        self.tmp_dir.cleanup()
+
+    @patch('spac.templates.visualize_ripley_template.plot_ripley_l')
+    def test_complete_io_workflow(self, mock_plot_ripley) -> None:
+        """Single I/O test covering all input/output scenarios."""
+        # Mock the plot_ripley_l function to return a figure and dataframe
+        mock_fig = MagicMock()
+        mock_df = pd.DataFrame({
+            'radius': [0, 50, 100],
+            'ripley_l': [0, 1.2, 2.5],
+            'lower_ci': [0, 0.8, 1.9],
+            'upper_ci': [0, 1.6, 3.1]
+        })
+        mock_plot_ripley.return_value = (mock_fig, mock_df)
+        
+        # Suppress warnings for cleaner test output
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore")
+
+            # Test 1: Basic workflow with dict input
+            saved_files = run_from_json(self.params)
+            self.assertIn(self.out_file, saved_files)
+            self.assertTrue(os.path.exists(saved_files[self.out_file]))
+            
+            # Verify plot_ripley_l was called with correct parameters
+            mock_plot_ripley.assert_called_once()
+            call_args = mock_plot_ripley.call_args
+            # Check that adata was passed as first argument
+            self.assertEqual(call_args[0][0].n_obs, 10)
+            # Check keyword arguments
+            self.assertEqual(
+                call_args[1]['phenotypes'], ("B cells", "CD8 T cells")
+            )
+            self.assertEqual(call_args[1]['regions'], None)
+            self.assertEqual(call_args[1]['sims'], True)
+            self.assertEqual(call_args[1]['return_df'], True)
+            
+            # Verify CSV output structure
+            df_output = pd.read_csv(saved_files[self.out_file])
+            self.assertEqual(len(df_output), 3)  # 3 radius points
+            self.assertIn('radius', df_output.columns)
+            self.assertIn('ripley_l', df_output.columns)
+
+            # Test 2: With specific regions enabled
+            params_regions = self.params.copy()
+            params_regions["Plot_Specific_Regions"] = True
+            params_regions["Regions_Label_s_"] = ["Region1", "Region2"]
+            mock_plot_ripley.reset_mock()
+            saved_files_regions = run_from_json(params_regions)
+            # Verify regions parameter was passed correctly
+            call_args = mock_plot_ripley.call_args
+            self.assertEqual(
+                call_args[1]['regions'], ["Region1", "Region2"]
+            )
+
+            # Test 3: Different output filename
+            params_custom = self.params.copy()
+            params_custom["Output_File"] = "custom_plots.csv"
+            mock_plot_ripley.reset_mock()
+            saved_files_custom = run_from_json(params_custom)
+            self.assertIn("custom_plots.csv", saved_files_custom)
+            self.assertTrue(
+                os.path.exists(saved_files_custom["custom_plots.csv"])
+            )
+
+    def test_regions_validation_error_message(self) -> None:
+        """
+        Test exact error message for empty regions 
+        when Plot_Specific_Regions is True.
+        """
+        params_bad = self.params.copy()
+        params_bad["Plot_Specific_Regions"] = True
+        params_bad["Regions_Label_s_"] = []
+
+        with self.assertRaises(ValueError) as context:
+            run_from_json(params_bad)
+
+        expected_msg = (
+            'Please identify at least one region in the '
+            '"Regions Label(s) parameter'
+        )
+        actual_msg = str(context.exception)
+        self.assertEqual(expected_msg, actual_msg)
+
+    @patch('spac.templates.visualize_ripley_template.plot_ripley_l')
+    def test_console_output(self, mock_plot_ripley) -> None:
+        """Test that dataframe is printed to console."""
+        # Mock the plot_ripley_l function
+        mock_fig = MagicMock()
+        mock_df = pd.DataFrame({
+            'radius': [0, 50, 100],
+            'ripley_l': [0, 1.2, 2.5]
+        })
+        mock_plot_ripley.return_value = (mock_fig, mock_df)
+        
+        # Capture console output
+        with patch('builtins.print') as mock_print:
+            run_from_json(self.params)
+            
+            # Verify dataframe was printed
+            print_calls = mock_print.call_args_list
+            # Check that to_string() output was printed
+            df_printed = False
+            for call in print_calls:
+                if (len(call[0]) > 0 and 
+                    call[0][0] == mock_df.to_string()):
+                    df_printed = True
+                    break
+            self.assertTrue(
+                df_printed, "DataFrame was not printed to console"
+            )
+
+
+if __name__ == "__main__":
+    unittest.main()
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