diff --git a/src/spyglass/spikesorting/v1/figurl_curation.py b/src/spyglass/spikesorting/v1/figurl_curation.py
index e32354235..dbfc26c95 100644
--- a/src/spyglass/spikesorting/v1/figurl_curation.py
+++ b/src/spyglass/spikesorting/v1/figurl_curation.py
@@ -102,7 +102,7 @@ def make(self, key: Dict):
         unit_metrics = _reformat_metrics(metric_dict)
 
         # Generate the figURL
-        url = _generate_figurl(
+        key["url"] = _generate_figurl(
             R=recording,
             S=sorting,
             initial_curation_uri=curation_uri,
@@ -110,10 +110,9 @@ def make(self, key: Dict):
             sorting_label=sorting_label,
             unit_metrics=unit_metrics,
         )
-        key["url"] = url
 
         # INSERT
-        self.insert1(key)
+        self.insert1(key, skip_duplicates=True)
 
 
 def _generate_figurl(
@@ -122,7 +121,6 @@ def _generate_figurl(
     initial_curation_uri: str,
     recording_label: str,
     sorting_label: str,
-    new_curation_uri: str,
     unit_metrics: Union[List[Any], None] = None,
     segment_duration_sec=1200,
     snippet_ms_before=1,
diff --git a/src/spyglass/spikesorting/v1/recording.py b/src/spyglass/spikesorting/v1/recording.py
index b7861b042..909c0d0f8 100644
--- a/src/spyglass/spikesorting/v1/recording.py
+++ b/src/spyglass/spikesorting/v1/recording.py
@@ -176,34 +176,22 @@ class SpikeSortingPreprocessingParameter(dj.Lookup):
     ---
     preproc_param: blob
     """
-    freq_min = 300  # high pass filter value
-    freq_max = 6000  # low pass filter value
-    margin_ms = 5  # margin in ms on border to avoid border effect
-    seed = 0  # random seed for whitening
 
     contents = [
         [
             "default",
             {
-                "frequency_min": freq_min,
-                "frequency_max": freq_max,
-                "margin_ms": margin_ms,
-                "seed": seed,
+                "frequency_min": 300,  # high pass filter value
+                "frequency_max": 6000,  # low pass filter value
+                "margin_ms": 5,  # margin in ms on border to avoid border effect
+                "seed": 0,  # random seed for whitening
             },
         ]
     ]
 
     @classmethod
     def insert_default(cls):
-        key = dict()
-        key["preproc_params_name"] = "default"
-        key["preproc_params"] = {
-            "frequency_min": cls.freq_min,
-            "frequency_max": cls.freq_max,
-            "margin_ms": cls.margin_ms,
-            "seed": cls.seed,
-        }
-        cls.insert1(key, skip_duplicates=True)
+        cls.insert1(cls.contents, skip_duplicates=True)
 
 
 @schema
diff --git a/src/spyglass/spikesorting/v1/sorting.py b/src/spyglass/spikesorting/v1/sorting.py
index 6f30f2f8e..89af141cc 100644
--- a/src/spyglass/spikesorting/v1/sorting.py
+++ b/src/spyglass/spikesorting/v1/sorting.py
@@ -3,7 +3,6 @@
 import os
 import tempfile
 import time
-from pathlib import Path
 
 import datajoint as dj
 import numpy as np
@@ -94,73 +93,9 @@ class SpikeSorterParameter(dj.Lookup):
         ]
     )
 
-    def insert_default(self):
-        """Default params from spike sorters available via spikeinterface"""
-        sorters = sis.available_sorters()
-        for sorter in sorters:
-            sorter_params = sis.get_default_sorter_params(sorter)
-            self.insert1(
-                [sorter, "default", sorter_params], skip_duplicates=True
-            )
-
-        # Insert Frank lab defaults
-        # Hippocampus tetrode default
-        sorter = "mountainsort4"
-        sorter_params_name = "franklab_tetrode_hippocampus_30KHz"
-        sorter_params = {
-            "detect_sign": -1,
-            "adjacency_radius": 100,
-            "freq_min": 600,
-            "freq_max": 6000,
-            "filter": False,
-            "whiten": True,
-            "num_workers": 1,
-            "clip_size": 40,
-            "detect_threshold": 3,
-            "detect_interval": 10,
-        }
-        self.insert1(
-            [sorter, sorter_params_name, sorter_params], skip_duplicates=True
-        )
-
-        # Cortical probe default
-        sorter = "mountainsort4"
-        sorter_params_name = "franklab_probe_ctx_30KHz"
-        sorter_params = {
-            "detect_sign": -1,
-            "adjacency_radius": 100,
-            "freq_min": 300,
-            "freq_max": 6000,
-            "filter": False,
-            "whiten": True,
-            "num_workers": 1,
-            "clip_size": 40,
-            "detect_threshold": 3,
-            "detect_interval": 10,
-        }
-        self.insert1(
-            [sorter, sorter_params_name, sorter_params], skip_duplicates=True
-        )
-
-        # clusterless defaults
-        sorter = "clusterless_thresholder"
-        sorter_params_name = "default_clusterless"
-        sorter_params = dict(
-            detect_threshold=100.0,  # uV
-            # Locally exclusive means one unit per spike detected
-            method="locally_exclusive",
-            peak_sign="neg",
-            exclude_sweep_ms=0.1,
-            local_radius_um=100,
-            # noise levels needs to be 1.0 so the units are in uV and not MAD
-            noise_levels=np.asarray([1.0]),
-            random_chunk_kwargs={},
-            # output needs to be set to sorting for the rest of the pipeline
-            outputs="sorting",
-        )
-        self.insert1(
-            [sorter, sorter_params_name, sorter_params], skip_duplicates=True
-        )
+    @classmethod
+    def insert_default(cls):
+        cls.insert1(cls.contents, skip_duplicates=True)
 
 
 @schema
@@ -251,7 +186,7 @@ def make(self, key: dict):
             # turn off whitening by sorter because
             # recording will be whitened before feeding it to sorter
             sorter_params["whiten"] = False
-            recording = sip.whiten(recording, dtype="float64")
+            recording = sip.whiten(recording, dtype=np.float64)
             sorting = sis.run_sorter(
                 sorter,
                 recording,