diff --git a/deeplabcut/compat.py b/deeplabcut/compat.py
index ab0920a11..1b44968ce 100644
--- a/deeplabcut/compat.py
+++ b/deeplabcut/compat.py
@@ -21,6 +21,7 @@
 import deeplabcut.core.visualization as visualization
 from deeplabcut.core.engine import Engine
 from deeplabcut.generate_training_dataset.metadata import get_shuffle_engine
+from deeplabcut.utils.deprecation import renamed_parameter
 
 DEFAULT_ENGINE = Engine.PYTORCH
 
@@ -59,14 +60,17 @@ def get_available_aug_methods(engine: Engine) -> tuple[str, ...]:
     raise RuntimeError(f"Unknown augmentation for engine: {engine}")
 
 
+@renamed_parameter(old="maxiters", new="max_iters", since="3.0.0")
+@renamed_parameter(old="saveiters", new="save_iters", since="3.0.0")
+@renamed_parameter(old="displayiters", new="display_iters", since="3.0.0")
 def train_network(
     config: str | Path,
     shuffle: int = 1,
     trainingsetindex: int = 0,
     max_snapshots_to_keep: int | None = None,
-    displayiters: int | None = None,
-    saveiters: int | None = None,
-    maxiters: int | None = None,
+    display_iters: int | None = None,
+    save_iters: int | None = None,
+    max_iters: int | None = None,
     epochs: int | None = None,
     save_epochs: int | None = None,
     allow_growth: bool = True,
@@ -108,13 +112,13 @@ def train_network(
         are kept.
         See: https://github.com/DeepLabCut/DeepLabCut/issues/8#issuecomment-387404835
 
-    displayiters: optional, default=None
+    display_iters: optional, default=None
         This variable is actually set in ``pose_config.yaml``. However, you can
         overwrite it with this hack. Don't use this regularly, just if you are too lazy
         to dig out the ``pose_config.yaml`` file for the corresponding project. If
         ``None``, the value from there is used, otherwise it is overwritten!
 
-    saveiters: optional, default=None
+    save_iters: optional, default=None
         Only for the TensorFlow engine (for the PyTorch engine see the ``torch_kwargs``:
         you can use ``save_epochs``).
         This variable is actually set in ``pose_config.yaml``. However, you can
@@ -122,7 +126,7 @@ def train_network(
         to dig out the ``pose_config.yaml`` file for the corresponding project.
         If ``None``, the value from there is used, otherwise it is overwritten!
 
-    maxiters: optional, default=None
+    max_iters: optional, default=None
         Only for the TensorFlow engine (for the PyTorch engine see the ``torch_kwargs``:
         you can use ``epochs``).
         This variable is actually set in ``pose_config.yaml``. However, you can
@@ -131,7 +135,7 @@ def train_network(
         If ``None``, the value from there is used, otherwise it is overwritten!
 
     epochs: optional, default=None
-        Only for the PyTorch engine (equivalent to the `maxiters` parameter for the
+        Only for the PyTorch engine (equivalent to the `max_iters` parameter for the
         TensorFlow engine). The maximum number of epochs to train the model for. If
         None, the value will be read from the `pytorch_config.yaml` file. An epoch is a
         single pass through the training dataset, which means your model has seen each
@@ -140,7 +144,7 @@ def train_network(
         16 with batch size 4, etc.).
 
     save_epochs: optional, default=None
-        Only for the PyTorch engine (equivalent to the `saveiters` parameter for the
+        Only for the PyTorch engine (equivalent to the `save_iters` parameter for the
         TensorFlow engine). The number of epochs between each snapshot save. If
         None, the value will be read from the `pytorch_config.yaml` file.
 
@@ -249,7 +253,7 @@ def train_network(
             batch_size=8,
             epochs=100,
             save_epochs=10,
-            displayiters=50,
+            display_iters=50,
         )
     """
     if engine is None:
@@ -270,9 +274,9 @@ def train_network(
             shuffle=shuffle,
             trainingsetindex=trainingsetindex,
             max_snapshots_to_keep=max_snapshots_to_keep,
-            displayiters=displayiters,
-            saveiters=saveiters,
-            maxiters=maxiters,
+            displayiters=display_iters,
+            saveiters=save_iters,
+            maxiters=max_iters,
             allow_growth=allow_growth,
             gputouse=gputouse,
             autotune=autotune,
@@ -299,7 +303,7 @@ def train_network(
             detector_batch_size=detector_batch_size,
             detector_epochs=detector_epochs,
             detector_save_epochs=detector_save_epochs,
-            display_iters=displayiters,
+            display_iters=display_iters,
             max_snapshots_to_keep=max_snapshots_to_keep,
             pose_threshold=pose_threshold,
             pytorch_cfg_updates=pytorch_cfg_updates,
@@ -373,13 +377,15 @@ def return_train_network_path(
     raise NotImplementedError(f"This function is not implemented for {engine}")
 
 
+@renamed_parameter(old="comparisonbodyparts", new="comparison_bodyparts", since="3.0.0")
+@renamed_parameter(old="Shuffles", new="shuffles", since="3.0.0")
 def evaluate_network(
     config: str | Path,
-    Shuffles: Iterable[int] = (1,),
+    shuffles: Iterable[int] = (1,),
     trainingsetindex: int | str = 0,
     plotting: bool | str = False,
     show_errors: bool = True,
-    comparisonbodyparts: str | list[str] = "all",
+    comparison_bodyparts: str | list[str] = "all",
     gputouse: str | None = None,
     rescale: bool = False,
     modelprefix: str = "",
@@ -401,7 +407,7 @@ def evaluate_network(
     config : string
         Full path of the config.yaml file.
 
-    Shuffles: list, optional, default=[1]
+    shuffles: list, optional, default=[1]
         List of integers specifying the shuffle indices of the training dataset.
 
     trainingsetindex: int or str, optional, default=0
@@ -419,7 +425,7 @@ def evaluate_network(
     show_errors: bool, optional, default=True
         Display train and test errors.
 
-    comparisonbodyparts: str or list, optional, default="all"
+    comparison_bodyparts: str or list, optional, default="all"
         The average error will be computed for those body parts only.
         The provided list has to be a subset of the defined body parts.
 
@@ -481,14 +487,14 @@ def evaluate_network(
     If you do not want to plot and evaluate with shuffle set to 1.
 
     >>> deeplabcut.evaluate_network(
-            '/analysis/project/reaching-task/config.yaml', Shuffles=[1],
+            '/analysis/project/reaching-task/config.yaml', shuffles=[1],
         )
 
     If you want to plot and evaluate with shuffle set to 0 and 1.
 
     >>> deeplabcut.evaluate_network(
             '/analysis/project/reaching-task/config.yaml',
-            Shuffles=[0, 1],
+            shuffles=[0, 1],
             plotting=True,
         )
 
@@ -496,7 +502,7 @@ def evaluate_network(
 
     >>> deeplabcut.evaluate_network(
             '/analysis/project/reaching-task/config.yaml',
-            Shuffles=[1],
+            shuffles=[1],
             plotting="individual",
         )
 
@@ -506,7 +512,7 @@ def evaluate_network(
 
     >>> deeplabcut.evaluate_network(
     >>>     "/analysis/project/reaching-task/config.yaml",
-    >>>     Shuffles=[0, 1],
+    >>>     shuffles=[0, 1],
     >>>     pcutoff={"left_ear": 0.8, "right_ear": 0.8},
     >>> )
 
@@ -515,7 +521,7 @@ def evaluate_network(
     if engine is None:
         cfg = _load_config(config)
         engines = set()
-        for shuffle in Shuffles:
+        for shuffle in shuffles:
             engines.add(
                 get_shuffle_engine(
                     cfg,
@@ -525,7 +531,7 @@ def evaluate_network(
                 )
             )
         if len(engines) == 0:
-            raise ValueError(f"You must pass at least one shuffle to evaluate (had {list(Shuffles)})")
+            raise ValueError(f"You must pass at least one shuffle to evaluate (had {list(shuffles)})")
         elif len(engines) > 1:
             raise ValueError(f"All shuffles must have the same engine (found {list(engines)})")
         engine = engines.pop()
@@ -535,11 +541,11 @@ def evaluate_network(
 
         return evaluate_network(
             str(config),
-            Shuffles=Shuffles,
+            Shuffles=shuffles,
             trainingsetindex=trainingsetindex,
             plotting=plotting,
             show_errors=show_errors,
-            comparisonbodyparts=comparisonbodyparts,
+            comparisonbodyparts=comparison_bodyparts,
             gputouse=gputouse,
             rescale=rescale,
             modelprefix=modelprefix,
@@ -552,11 +558,11 @@ def evaluate_network(
         _update_device(gputouse, torch_kwargs)
         return evaluate_network(
             config,
-            shuffles=Shuffles,
+            shuffles=shuffles,
             trainingsetindex=trainingsetindex,
             plotting=plotting,
             show_errors=show_errors,
-            comparison_bodyparts=comparisonbodyparts,
+            comparison_bodyparts=comparison_bodyparts,
             snapshots_to_evaluate=snapshots_to_evaluate,
             per_keypoint_evaluation=per_keypoint_evaluation,
             modelprefix=modelprefix,
@@ -567,12 +573,14 @@ def evaluate_network(
     raise NotImplementedError(f"This function is not implemented for {engine}")
 
 
+@renamed_parameter(old="comparisonbodyparts", new="comparison_bodyparts", since="3.0.0")
+@renamed_parameter(old="Snapindex", new="snapshotindex", since="3.0.0")
 def return_evaluate_network_data(
     config: str,
     shuffle: int = 0,
     trainingsetindex: int = 0,
-    comparisonbodyparts: str | list[str] = "all",
-    Snapindex: str | int | None = None,
+    comparison_bodyparts: str | list[str] = "all",
+    snapshotindex: str | int | None = None,
     rescale: bool = False,
     fulldata: bool = False,
     show_errors: bool = True,
@@ -583,7 +591,7 @@ def return_evaluate_network_data(
     """Returns the results for (previously evaluated) network.
     deeplabcut.evaluate_network(..) Returns list of (per model): [trainingsiterations,tr
     ainfraction,shuffle,trainerror,testerror,pcutoff,trainerrorpcutoff,testerrorpcutoff,
-    Snapshots[snapindex],scale,net_type]
+    Snapshots[snapshotindex],scale,net_type]
 
     This function is only implemented for tensorflow models/shuffles, and will throw
     an error if called with a PyTorch shuffle.
@@ -592,7 +600,7 @@ def return_evaluate_network_data(
     Returns list of:
        (DataMachine, Data, data, trainIndices,
        testIndices, trainFraction, DLCscorer,
-       comparisonbodyparts, cfg, Snapshots[snapindex]
+       comparison_bodyparts, cfg, Snapshots[snapshotindex]
        )
     ----------
     config : string
@@ -607,7 +615,7 @@ def return_evaluate_network_data(
         By default the first (note that TrainingFraction is a list in config.yaml).
         This variable can also be set to "all".
 
-    comparisonbodyparts: list of bodyparts, Default is "all".
+    comparison_bodyparts: list of bodyparts, Default is "all".
         The average error will be computed for those body parts only
         (Has to be a subset of the body parts).
 
@@ -649,8 +657,8 @@ def return_evaluate_network_data(
             config,
             shuffle=shuffle,
             trainingsetindex=trainingsetindex,
-            comparisonbodyparts=comparisonbodyparts,
-            Snapindex=Snapindex,
+            comparisonbodyparts=comparison_bodyparts,
+            Snapindex=snapshotindex,
             rescale=rescale,
             fulldata=fulldata,
             show_errors=show_errors,
@@ -661,17 +669,19 @@ def return_evaluate_network_data(
     raise NotImplementedError(f"This function is not implemented for {engine}")
 
 
+@renamed_parameter(old="batchsize", new="batch_size", since="3.0.0")
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def analyze_videos(
     config: str,
     videos: list[str],
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle: int = 1,
     trainingsetindex: int = 0,
     gputouse: str | None = None,
     save_as_csv: bool = False,
     in_random_order: bool = True,
     destfolder: str | None = None,
-    batchsize: int = None,
+    batch_size: int = None,
     cropping: list[int] | None = None,
     TFGPUinference: bool = True,
     dynamic: tuple[bool, float, int] = (False, 0.5, 10),
@@ -710,7 +720,7 @@ def analyze_videos(
         A list of strings containing the full paths to videos for analysis or a path to
         the directory, where all the videos with same extension are stored.
 
-    videotype : str | Sequence[str] | None, optional, default=None
+    extensions : str | Sequence[str] | None, optional, default=None
         Controls how ``videos`` are filtered, based on file extension.
         File paths and directory contents are treated differently:
         - ``None`` (default): file paths are accepted as-is; directories are
@@ -746,7 +756,7 @@ def analyze_videos(
         the video is used. Note that for subsequent analysis this folder also needs to
         be passed.
 
-    batchsize: int or None, optional, default=None
+    batch_size: int or None, optional, default=None
         Currently not supported by the PyTorch engine.
         Change batch size for inference; if given overwrites value in ``pose_cfg.yaml``.
 
@@ -869,7 +879,7 @@ def analyze_videos(
     >>> deeplabcut.analyze_videos(
             '/analysis/project/reaching-task/config.yaml',
             ['/analysis/project/videos'],
-            videotype='.avi',
+            extensions='.avi',
         )
 
     Analyze multiple videos
@@ -923,14 +933,14 @@ def analyze_videos(
         return analyze_videos(
             config,
             videos,
-            videotype=videotype,
+            extensions=extensions,
             shuffle=shuffle,
             trainingsetindex=trainingsetindex,
             gputouse=gputouse,
             save_as_csv=save_as_csv,
             in_random_order=in_random_order,
             destfolder=destfolder,
-            batchsize=batchsize,
+            batchsize=batch_size,
             cropping=cropping,
             TFGPUinference=TFGPUinference,
             dynamic=dynamic,
@@ -950,20 +960,20 @@ def analyze_videos(
 
         _update_device(gputouse, torch_kwargs)
 
-        if batchsize is not None:
+        if batch_size is not None:
             if "batch_size" in torch_kwargs:
                 print(
-                    f"You called analyze_videos with parameters ``batchsize={batchsize}"
+                    f"You called analyze_videos with parameters ``batch_size={batch_size}"
                     f"`` and batch_size={torch_kwargs['batch_size']}. Only one is "
                     f"needed/used. Using batch size {torch_kwargs['batch_size']}"
                 )
             else:
-                torch_kwargs["batch_size"] = batchsize
+                torch_kwargs["batch_size"] = batch_size
 
         return analyze_videos(
             config,
             videos=videos,
-            videotype=videotype,
+            extensions=extensions,
             shuffle=shuffle,
             trainingsetindex=trainingsetindex,
             save_as_csv=save_as_csv,
@@ -986,16 +996,18 @@ def analyze_videos(
     raise NotImplementedError(f"This function is not implemented for {engine}")
 
 
+@renamed_parameter(old="batchsize", new="batch_size", since="3.0.0")
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def create_tracking_dataset(
     config: str,
     videos: list[str],
     track_method: str,
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle: int = 1,
     trainingsetindex: int = 0,
     gputouse: int | None = None,
     destfolder: str | None = None,
-    batchsize: int | None = None,
+    batch_size: int | None = None,
     cropping: list[int] | None = None,
     TFGPUinference: bool = True,
     modelprefix: str = "",
@@ -1019,7 +1031,7 @@ def create_tracking_dataset(
         Specifies the tracker used to generate the pose estimation data. Must be either
         'box', 'skeleton', or 'ellipse'.
 
-    videotype : str | Sequence[str] | None, optional, default=None
+    extensions : str | Sequence[str] | None, optional, default=None
         Controls how ``videos`` are filtered, based on file extension.
         File paths and directory contents are treated differently:
         - ``None`` (default): file paths are accepted as-is; directories are
@@ -1091,12 +1103,12 @@ def create_tracking_dataset(
             config,
             videos,
             track_method,
-            videotype=videotype,
+            extensions=extensions,
             shuffle=shuffle,
             trainingsetindex=trainingsetindex,
             gputouse=gputouse,
             destfolder=destfolder,
-            batchsize=batchsize,
+            batchsize=batch_size,
             cropping=cropping,
             TFGPUinference=TFGPUinference,
             modelprefix=modelprefix,
@@ -1110,11 +1122,11 @@ def create_tracking_dataset(
             config,
             videos,
             track_method,
-            videotype=videotype,
+            extensions=extensions,
             shuffle=shuffle,
             trainingsetindex=trainingsetindex,
             destfolder=destfolder,
-            batch_size=batchsize,
+            batch_size=batch_size,
             cropping=cropping,
             modelprefix=modelprefix,
             robust_nframes=robust_nframes,
@@ -1413,10 +1425,11 @@ def analyze_time_lapse_frames(
     raise NotImplementedError(f"This function is not implemented for {engine}")
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def convert_detections2tracklets(
     config: str,
     videos: list[str],
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle: int = 1,
     trainingsetindex: int = 0,
     overwrite: bool = False,
@@ -1443,7 +1456,7 @@ def convert_detections2tracklets(
         A list of strings containing the full paths to videos for analysis or a path to the directory,
         where all the videos with same extension are stored.
 
-    videotype : str | Sequence[str] | None, optional, default=None
+    extensions : str | Sequence[str] | None, optional, default=None
         Controls how ``videos`` are filtered, based on file extension.
         File paths and directory contents are treated differently:
         - ``None`` (default): file paths are accepted as-is; directories are
@@ -1509,7 +1522,7 @@ def convert_detections2tracklets(
     >>> deeplabcut.convert_detections2tracklets(
     >>>    "/analysis/project/reaching-task/config.yaml",
     >>>    ["/analysis/project/video1.mp4"],
-    >>>    videotype='.mp4',
+    >>>    extensions='.mp4',
     >>> )
 
     If you want to convert detections to tracklets based on box_tracker:
@@ -1517,7 +1530,7 @@ def convert_detections2tracklets(
     >>> deeplabcut.convert_detections2tracklets(
     >>>    "/analysis/project/reaching-task/config.yaml",
     >>>    ["/analysis/project/video1.mp4"],
-    >>>    videotype=".mp4",
+    >>>    extensions=".mp4",
     >>>    track_method="box",
     >>> )
 
@@ -1537,7 +1550,7 @@ def convert_detections2tracklets(
         return convert_detections2tracklets(
             config,
             videos,
-            videotype=videotype,
+            extensions=extensions,
             shuffle=shuffle,
             trainingsetindex=trainingsetindex,
             overwrite=overwrite,
@@ -1563,7 +1576,7 @@ def convert_detections2tracklets(
         return convert_detections2tracklets(
             config,
             videos,
-            videotype=videotype,
+            extensions=extensions,
             shuffle=shuffle,
             trainingsetindex=trainingsetindex,
             overwrite=overwrite,
@@ -1730,11 +1743,12 @@ def visualize_paf(
     return visualization.visualize_paf(image, paf, step=step, colors=colors)
 
 
+@renamed_parameter(old="comparisonbodyparts", new="comparison_bodyparts", since="3.0.0")
 def extract_save_all_maps(
     config,
     shuffle: int = 1,
     trainingsetindex: int = 0,
-    comparisonbodyparts: str | list[str] = "all",
+    comparison_bodyparts: str | list[str] = "all",
     extract_paf: bool = True,
     all_paf_in_one: bool = True,
     gputouse: int | None = None,
@@ -1763,7 +1777,7 @@ def extract_save_all_maps(
         By default the first (note that TrainingFraction is a list in config.yaml).
         This variable can also be set to "all".
 
-    comparisonbodyparts: list of bodyparts, Default is "all".
+    comparison_bodyparts: list of bodyparts, Default is "all".
         The average error will be computed for those body parts only (Has to be a subset of the body parts).
 
     extract_paf : bool
@@ -1824,7 +1838,7 @@ def extract_save_all_maps(
             config,
             shuffle=shuffle,
             trainingsetindex=trainingsetindex,
-            comparisonbodyparts=comparisonbodyparts,
+            comparisonbodyparts=comparison_bodyparts,
             extract_paf=extract_paf,
             all_paf_in_one=all_paf_in_one,
             gputouse=gputouse,
@@ -1840,7 +1854,7 @@ def extract_save_all_maps(
             config,
             shuffle=shuffle,
             trainingsetindex=trainingsetindex,
-            comparison_bodyparts=comparisonbodyparts,
+            comparison_bodyparts=comparison_bodyparts,
             extract_paf=extract_paf,
             all_paf_in_one=all_paf_in_one,
             device=_gpu_to_use_to_device(gputouse, device),
diff --git a/deeplabcut/pose_estimation_pytorch/apis/tracking_dataset.py b/deeplabcut/pose_estimation_pytorch/apis/tracking_dataset.py
index b031e8618..4995e8a50 100644
--- a/deeplabcut/pose_estimation_pytorch/apis/tracking_dataset.py
+++ b/deeplabcut/pose_estimation_pytorch/apis/tracking_dataset.py
@@ -26,6 +26,7 @@
 from deeplabcut.pose_estimation_pytorch.task import Task
 from deeplabcut.pose_tracking_pytorch import create_triplets_dataset
 from deeplabcut.utils.auxfun_videos import collect_video_paths
+from deeplabcut.utils.deprecation import renamed_parameter
 
 
 def build_feature_extraction_runner(
@@ -125,11 +126,12 @@ def extract_features_for_video(
     shelf_writer.close()
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def create_tracking_dataset(
     config: str,
     videos: list[str] | list[Path],
     track_method: str,
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle: int = 1,
     trainingsetindex: int = 0,
     destfolder: str | None = None,
@@ -149,7 +151,7 @@ def create_tracking_dataset(
             the videos with same extension are stored.
         track_method: Specifies the tracker used to generate the pose estimation data.
             Must be either 'box', 'skeleton', or 'ellipse'.
-        videotype: Controls how ``videos`` are filtered, based on file extension.
+        extensions: Controls how ``videos`` are filtered, based on file extension.
             File paths and directory contents are treated differently:
             - ``None`` (default): file paths are accepted as-is; directories are
               scanned for files with a recognized video extension.
@@ -245,7 +247,7 @@ def create_tracking_dataset(
         modelprefix=modelprefix,
     )
 
-    videos = collect_video_paths(videos, extensions=videotype)
+    videos = collect_video_paths(videos, extensions=extensions)
     for video_path in videos:
         print(f"Loading {video_path}")
         video = VideoIterator(video_path, cropping=cropping)
diff --git a/deeplabcut/pose_estimation_pytorch/apis/tracklets.py b/deeplabcut/pose_estimation_pytorch/apis/tracklets.py
index 121a11e1e..5c69432da 100644
--- a/deeplabcut/pose_estimation_pytorch/apis/tracklets.py
+++ b/deeplabcut/pose_estimation_pytorch/apis/tracklets.py
@@ -31,12 +31,14 @@
 )
 from deeplabcut.pose_estimation_pytorch.data.dlcloader import DLCLoader
 from deeplabcut.utils.auxfun_videos import collect_video_paths
+from deeplabcut.utils.deprecation import renamed_parameter
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def convert_detections2tracklets(
     config: str,
     videos: str | list[str],
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle: int = 1,
     trainingsetindex: int = 0,
     overwrite: bool = False,
@@ -126,7 +128,7 @@ def convert_detections2tracklets(
     )
 
     paths_input = videos
-    videos = collect_video_paths(videos, extensions=videotype)
+    videos = collect_video_paths(videos, extensions=extensions)
     if len(videos) == 0:
         print(f"No videos were found in {paths_input}")
         return
diff --git a/deeplabcut/pose_estimation_pytorch/apis/utils.py b/deeplabcut/pose_estimation_pytorch/apis/utils.py
index 879e449e3..07c96f14e 100644
--- a/deeplabcut/pose_estimation_pytorch/apis/utils.py
+++ b/deeplabcut/pose_estimation_pytorch/apis/utils.py
@@ -66,7 +66,7 @@
 from deeplabcut.pose_estimation_pytorch.utils import resolve_device
 from deeplabcut.utils import auxiliaryfunctions
 from deeplabcut.utils.auxfun_videos import SUPPORTED_VIDEOS, collect_video_paths
-from deeplabcut.utils.deprecation import deprecated
+from deeplabcut.utils.deprecation import deprecated, renamed_parameter
 
 
 def parse_snapshot_index_for_analysis(
@@ -295,14 +295,15 @@ def get_scorer_name(
 
 
 @deprecated(replacement="deeplabcut.collect_video_paths", since="3.0.0")
+@renamed_parameter(old="video_type", new="extensions", since="3.0.0")
 def list_videos_in_folder(
     data_path: str | Path | list[str | Path],
-    video_type: str | Sequence[str] | None = SUPPORTED_VIDEOS,
+    extensions: str | Sequence[str] | None = SUPPORTED_VIDEOS,
     shuffle: bool = False,
 ) -> list[Path]:
     return collect_video_paths(
         data_path=data_path,
-        extensions=video_type,
+        extensions=extensions,
         shuffle=shuffle,
     )
 
diff --git a/deeplabcut/pose_estimation_pytorch/apis/videos.py b/deeplabcut/pose_estimation_pytorch/apis/videos.py
index c41f7c775..4dffd1dc1 100644
--- a/deeplabcut/pose_estimation_pytorch/apis/videos.py
+++ b/deeplabcut/pose_estimation_pytorch/apis/videos.py
@@ -46,6 +46,7 @@
 from deeplabcut.refine_training_dataset.stitch import stitch_tracklets
 from deeplabcut.utils import VideoReader, auxiliaryfunctions
 from deeplabcut.utils.auxfun_videos import collect_video_paths
+from deeplabcut.utils.deprecation import renamed_parameter
 
 
 class VideoIterator(VideoReader):
@@ -241,10 +242,11 @@ def video_inference(
     return predictions
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def analyze_videos(
     config: str,
     videos: str | list[str],
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle: int = 1,
     trainingsetindex: int = 0,
     save_as_csv: bool = False,
@@ -284,7 +286,7 @@ def analyze_videos(
         videos: a str (or list of strings) containing the full paths to videos for
             analysis or a path to the directory, where all the videos with same
             extension are stored.
-        videotype: Controls how ``videos`` are filtered, based on file extension.
+        extensions: Controls how ``videos`` are filtered, based on file extension.
             File paths and directory contents are treated differently:
             - ``None`` (default): file paths are accepted as-is; directories are
               scanned for files with a recognized video extension.
@@ -546,7 +548,7 @@ def analyze_videos(
     print(f"Using scorer: {dlc_scorer}")
 
     # Reading video and init variables
-    videos = collect_video_paths(videos, extensions=videotype, shuffle=in_random_order)
+    videos = collect_video_paths(videos, extensions=extensions, shuffle=in_random_order)
     h5_files_created = False  # Track if any .h5 files were created
 
     for video in videos:
@@ -676,7 +678,7 @@ def analyze_videos(
                     convert_detections2tracklets(
                         config=config,
                         videos=str(video),
-                        videotype=videotype,
+                        extensions=extensions,
                         shuffle=shuffle,
                         trainingsetindex=trainingsetindex,
                         overwrite=False,
@@ -688,7 +690,7 @@ def analyze_videos(
                     stitch_tracklets(
                         config,
                         [str(video)],
-                        videotype,
+                        extensions,
                         shuffle,
                         trainingsetindex,
                         n_tracks=n_tracks,
diff --git a/deeplabcut/pose_estimation_tensorflow/modelzoo/api/superanimal_inference.py b/deeplabcut/pose_estimation_tensorflow/modelzoo/api/superanimal_inference.py
index 9d0386490..f57897c3e 100644
--- a/deeplabcut/pose_estimation_tensorflow/modelzoo/api/superanimal_inference.py
+++ b/deeplabcut/pose_estimation_tensorflow/modelzoo/api/superanimal_inference.py
@@ -28,6 +28,7 @@
 from deeplabcut.pose_estimation_tensorflow.core import predict_multianimal as predict
 from deeplabcut.utils import auxiliaryfunctions
 from deeplabcut.utils.auxfun_videos import VideoWriter, collect_video_paths
+from deeplabcut.utils.deprecation import renamed_parameter
 
 warnings.simplefilter("ignore", category=RuntimeWarning)
 
@@ -243,12 +244,13 @@ def _video_inference(
     return PredicteData, nframes
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def video_inference(
     videos,
     project_name,
     model_name,
     scale_list=None,
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     destfolder=None,
     batchsize=1,
     robust_nframes=False,
@@ -307,7 +309,7 @@ def video_inference(
 
     sess, inputs, outputs = single_predict.setup_pose_prediction(test_cfg, allow_growth=allow_growth)
     DLCscorer = "DLC_" + Path(test_cfg["init_weights"]).stem
-    videos = collect_video_paths(videos, extensions=videotype)
+    videos = collect_video_paths(videos, extensions=extensions)
 
     datafiles = []
     for video in videos:
diff --git a/deeplabcut/pose_estimation_tensorflow/predict_videos.py b/deeplabcut/pose_estimation_tensorflow/predict_videos.py
index d40fa68d1..d63d6025d 100644
--- a/deeplabcut/pose_estimation_tensorflow/predict_videos.py
+++ b/deeplabcut/pose_estimation_tensorflow/predict_videos.py
@@ -42,17 +42,19 @@
 from deeplabcut.refine_training_dataset.stitch import stitch_tracklets
 from deeplabcut.utils import auxfun_models, auxfun_multianimal, auxiliaryfunctions
 from deeplabcut.utils.auxfun_videos import collect_video_paths
+from deeplabcut.utils.deprecation import renamed_parameter
 
 ####################################################
 # Loading data, and defining model folder
 ####################################################
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def create_tracking_dataset(
     config,
     videos,
     track_method,
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle=1,
     trainingsetindex=0,
     gputouse=None,
@@ -200,7 +202,7 @@ def create_tracking_dataset(
     ##################################################
     # Looping over videos
     ##################################################
-    Videos = collect_video_paths(videos, extensions=videotype)
+    Videos = collect_video_paths(videos, extensions=extensions)
     if len(Videos) > 0:
         if "multi-animal" in dlc_cfg["dataset_type"]:
             for video in Videos:
@@ -249,14 +251,15 @@ def create_tracking_dataset(
             )
         return DLCscorer  # note: this is either DLCscorer or DLCscorerlegacy depending on what was used!
     else:
-        print("No video(s) were found. Please check your paths and/or 'videotype'.")
+        print("No video(s) were found. Please check your paths and/or extensions filter.")
         return DLCscorer
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def analyze_videos(
     config,
     videos,
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle=1,
     trainingsetindex=0,
     gputouse=None,
@@ -300,7 +303,7 @@ def analyze_videos(
         A list of strings containing the full paths to videos for analysis or a path to
         the directory, where all the videos with same extension are stored.
 
-    videotype : str | Sequence[str] | None, optional, default=None
+    extensions : str | Sequence[str] | None, optional, default=None
         Controls how ``videos`` are filtered, based on file extension.
         File paths and directory contents are treated differently:
         - ``None`` (default): file paths are accepted as-is; directories are
@@ -445,7 +448,7 @@ def analyze_videos(
     >>> deeplabcut.analyze_videos(
             '/analysis/project/reaching-task/config.yaml',
             ['/analysis/project/videos'],
-            videotype='.avi',
+            extensions='.avi',
         )
 
     Analyze multiple videos
@@ -600,7 +603,7 @@ def analyze_videos(
     ##################################################
     # Looping over videos
     ##################################################
-    Videos = collect_video_paths(videos, extensions=videotype, shuffle=in_random_order)
+    Videos = collect_video_paths(videos, extensions=extensions, shuffle=in_random_order)
     if len(Videos) > 0:
         if "multi-animal" in dlc_cfg["dataset_type"]:
             from deeplabcut.pose_estimation_tensorflow.predict_multianimal import (
@@ -625,7 +628,7 @@ def analyze_videos(
                     convert_detections2tracklets(
                         config,
                         [video],
-                        videotype,
+                        extensions,
                         shuffle,
                         trainingsetindex,
                         destfolder=destfolder,
@@ -636,7 +639,7 @@ def analyze_videos(
                     stitch_tracklets(
                         config,
                         [video],
-                        videotype,
+                        extensions,
                         shuffle,
                         trainingsetindex,
                         destfolder=destfolder,
@@ -686,7 +689,7 @@ def analyze_videos(
             )
         return DLCscorer  # note: this is either DLCscorer or DLCscorerlegacy depending on what was used!
     else:
-        print("No video(s) were found. Please check your paths and/or 'videotype'.")
+        print("No video(s) were found. Please check your paths and/or extensions filter.")
         return DLCscorer
 
 
@@ -1481,10 +1484,11 @@ def _convert_detections_to_tracklets(
         pickle.dump(tracklets, f, pickle.HIGHEST_PROTOCOL)
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def convert_detections2tracklets(
     config,
     videos,
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle=1,
     trainingsetindex=0,
     overwrite=False,
@@ -1510,7 +1514,7 @@ def convert_detections2tracklets(
         A list of strings containing the full paths to videos for analysis
         or a path to the directory, where all the videos with same extension are stored.
 
-    videotype : str | Sequence[str] | None, optional, default=None
+    extensions : str | Sequence[str] | None, optional, default=None
         Controls how ``videos`` are filtered, based on file extension.
         File paths and directory contents are treated differently:
         - ``None`` (default): file paths are accepted as-is; directories are
@@ -1570,14 +1574,14 @@ def convert_detections2tracklets(
     >>> deeplabcut.convert_detections2tracklets(
         '/analysis/project/reaching-task/config.yaml',
         ['/analysis/project/video1.mp4'],
-        videotype='.mp4'
+        extensions='.mp4'
         )
 
     If you want to convert detections to tracklets based on box_tracker:
     >>> deeplabcut.convert_detections2tracklets(
         '/analysis/project/reaching-task/config.yaml',
         ['/analysis/project/video1.mp4'],
-        videotype='.mp4',
+        extensions='.mp4',
         track_method='box'
         )
 
@@ -1661,7 +1665,7 @@ def convert_detections2tracklets(
     ##################################################
     # Looping over videos
     ##################################################
-    Videos = collect_video_paths(videos, extensions=videotype)
+    Videos = collect_video_paths(videos, extensions=extensions)
     if len(Videos) > 0:
         for video in Videos:
             print("Processing... ", video)
diff --git a/deeplabcut/pose_tracking_pytorch/apis.py b/deeplabcut/pose_tracking_pytorch/apis.py
index 166e0509c..9bff6c0d1 100644
--- a/deeplabcut/pose_tracking_pytorch/apis.py
+++ b/deeplabcut/pose_tracking_pytorch/apis.py
@@ -11,11 +11,14 @@
 
 from collections.abc import Sequence
 
+from deeplabcut.utils.deprecation import renamed_parameter
 
+
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def transformer_reID(
     config: str,
     videos: list[str],
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle: int = 1,
     trainingsetindex: int = 0,
     track_method: str = "ellipse",
@@ -46,7 +49,7 @@ def transformer_reID(
         A list of strings containing the full paths to videos for analysis or a path to
         the directory, where all the videos with same extension are stored.
 
-    videotype : str | Sequence[str] | None, optional, default=None
+    extensions : str | Sequence[str] | None, optional, default=None
         Controls how ``videos`` are filtered, based on file extension.
         File paths and directory contents are treated differently:
         - ``None`` (default): file paths are accepted as-is; directories are
@@ -112,7 +115,7 @@ def transformer_reID(
         config,
         videos,
         track_method,
-        videotype=videotype,
+        extensions=extensions,
         shuffle=shuffle,
         trainingsetindex=trainingsetindex,
         modelprefix=modelprefix,
@@ -135,7 +138,7 @@ def transformer_reID(
         config,
         DLCscorer,
         videos,
-        videotype=videotype,
+        extensions=extensions,
         train_frac=train_frac,
         modelprefix=modelprefix,
         train_epochs=train_epochs,
@@ -151,7 +154,7 @@ def transformer_reID(
     deeplabcut.stitch_tracklets(
         config,
         videos,
-        videotype=videotype,
+        extensions=extensions,
         shuffle=shuffle,
         trainingsetindex=trainingsetindex,
         track_method=track_method,
diff --git a/deeplabcut/pose_tracking_pytorch/train_dlctransreid.py b/deeplabcut/pose_tracking_pytorch/train_dlctransreid.py
index b1145dc93..12f284135 100644
--- a/deeplabcut/pose_tracking_pytorch/train_dlctransreid.py
+++ b/deeplabcut/pose_tracking_pytorch/train_dlctransreid.py
@@ -23,6 +23,7 @@
 import numpy as np
 
 from deeplabcut.utils.auxfun_videos import collect_video_paths
+from deeplabcut.utils.deprecation import renamed_parameter
 
 from .config import cfg
 from .datasets import make_dlc_dataloader
@@ -67,11 +68,12 @@ def split_train_test(npy_list, train_frac):
     return train_list, test_list
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def train_tracking_transformer(
     path_config_file,
     dlcscorer,
     videos,
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     train_frac=0.8,
     modelprefix="",
     train_epochs=100,
@@ -80,7 +82,7 @@ def train_tracking_transformer(
     destfolder=None,
 ):
     npy_list = []
-    videos = collect_video_paths(videos, extensions=videotype)
+    videos = collect_video_paths(videos, extensions=extensions)
     for video in videos:
         videofolder = str(Path(video).parents[0])
         if destfolder is None:
diff --git a/deeplabcut/post_processing/analyze_skeleton.py b/deeplabcut/post_processing/analyze_skeleton.py
index 14d5ab50d..5389919fd 100644
--- a/deeplabcut/post_processing/analyze_skeleton.py
+++ b/deeplabcut/post_processing/analyze_skeleton.py
@@ -25,6 +25,7 @@
 
 from deeplabcut.utils import auxfun_multianimal, auxiliaryfunctions
 from deeplabcut.utils.auxfun_videos import collect_video_paths
+from deeplabcut.utils.deprecation import renamed_parameter
 
 
 # utility functions
@@ -166,10 +167,11 @@ def analyzebone(bp1, bp2):
 
 
 # MAIN FUNC
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def analyzeskeleton(
     config,
     videos,
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle=1,
     trainingsetindex=0,
     filtered=False,
@@ -193,7 +195,7 @@ def analyzeskeleton(
         The full paths to videos for analysis or a path to the directory, where all the
         videos with same extension are stored.
 
-    videotype : str | Sequence[str] | None, optional, default=None
+    extensions : str | Sequence[str] | None, optional, default=None
         Controls how ``videos`` are filtered, based on file extension.
         File paths and directory contents are treated differently:
         - ``None`` (default): file paths are accepted as-is; directories are
@@ -266,7 +268,7 @@ def analyzeskeleton(
         **kwargs,
     )
 
-    Videos = collect_video_paths(videos, extensions=videotype)
+    Videos = collect_video_paths(videos, extensions=extensions)
     for video in Videos:
         print(f"Processing {video}")
         if destfolder is None:
diff --git a/deeplabcut/post_processing/filtering.py b/deeplabcut/post_processing/filtering.py
index f0ed95595..463803f4f 100644
--- a/deeplabcut/post_processing/filtering.py
+++ b/deeplabcut/post_processing/filtering.py
@@ -21,6 +21,7 @@
 from deeplabcut.refine_training_dataset.outlier_frames import FitSARIMAXModel
 from deeplabcut.utils import auxfun_multianimal, auxiliaryfunctions
 from deeplabcut.utils.auxfun_videos import collect_video_paths
+from deeplabcut.utils.deprecation import renamed_parameter
 
 
 def columnwise_spline_interp(data, max_gap=0):
@@ -64,10 +65,11 @@ def columnwise_spline_interp(data, max_gap=0):
     return temp
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def filterpredictions(
     config,
     video,
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle=1,
     trainingsetindex=0,
     filtertype="median",
@@ -97,7 +99,7 @@ def filterpredictions(
         Full path of the video to extract the frame from. Make sure that this video is
         already analyzed.
 
-    videotype : str | Sequence[str] | None, optional, default=None
+    extensions : str | Sequence[str] | None, optional, default=None
         Controls how ``videos`` are filtered, based on file extension.
         File paths and directory contents are treated differently:
         - ``None`` (default): file paths are accepted as-is; directories are
@@ -223,12 +225,12 @@ def filterpredictions(
         modelprefix=modelprefix,
         **kwargs,
     )
-    Videos = collect_video_paths(video, extensions=videotype)
+    Videos = collect_video_paths(video, extensions=extensions)
 
     video_to_filtered_df = {}
 
     if not len(Videos):
-        print("No video(s) were found. Please check your paths and/or 'videotype'.")
+        print("No video(s) were found. Please check your paths and/or extensions filter.")
         if return_data:
             return video_to_filtered_df
 
diff --git a/deeplabcut/refine_training_dataset/outlier_frames.py b/deeplabcut/refine_training_dataset/outlier_frames.py
index fbaa7d663..f2c6a57f7 100644
--- a/deeplabcut/refine_training_dataset/outlier_frames.py
+++ b/deeplabcut/refine_training_dataset/outlier_frames.py
@@ -32,6 +32,7 @@
     visualization,
 )
 from deeplabcut.utils.auxfun_videos import VideoWriter, collect_video_paths
+from deeplabcut.utils.deprecation import renamed_parameter
 
 
 def find_outliers_in_raw_data(
@@ -197,10 +198,11 @@ def _read_video_specific_cropping_margins(config: str | Path | dict, video_path:
     return x1, y1
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def extract_outlier_frames(
     config,
     videos,
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle=1,
     trainingsetindex=0,
     outlieralgorithm="jump",
@@ -240,7 +242,7 @@ def extract_outlier_frames(
         The full paths to videos for analysis or a path to the directory, where all the
         videos with same extension are stored.
 
-    videotype : str | Sequence[str] | None, optional, default=None
+    extensions : str | Sequence[str] | None, optional, default=None
         Controls how ``videos`` are filtered, based on file extension.
         File paths and directory contents are treated differently:
         - ``None`` (default): file paths are accepted as-is; directories are
@@ -406,7 +408,7 @@ def extract_outlier_frames(
         **kwargs,
     )
 
-    Videos = collect_video_paths(videos, extensions=videotype)
+    Videos = collect_video_paths(videos, extensions=extensions)
     if len(Videos) == 0:
         print("No suitable videos found in", videos)
 
diff --git a/deeplabcut/refine_training_dataset/stitch.py b/deeplabcut/refine_training_dataset/stitch.py
index 049c72d2b..188b60eb7 100644
--- a/deeplabcut/refine_training_dataset/stitch.py
+++ b/deeplabcut/refine_training_dataset/stitch.py
@@ -37,6 +37,7 @@
 )
 from deeplabcut.utils import auxfun_multianimal, auxiliaryfunctions
 from deeplabcut.utils.auxfun_videos import VideoWriter, collect_video_paths
+from deeplabcut.utils.deprecation import renamed_parameter
 
 
 class Tracklet:
@@ -958,10 +959,11 @@ def reconstruct_path(self, source):
                 return path
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def stitch_tracklets(
     config_path,
     videos,
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle=1,
     trainingsetindex=0,
     n_tracks=None,
@@ -991,7 +993,7 @@ def stitch_tracklets(
         A list of strings containing the full paths to videos for analysis or a path to the directory, where all the
         videos with same extension are stored.
 
-    videotype : str | Sequence[str] | None, optional, default=None
+    extensions : str | Sequence[str] | None, optional, default=None
         Controls how ``videos`` are filtered, based on file extension.
         File paths and directory contents are treated differently:
         - ``None`` (default): file paths are accepted as-is; directories are
@@ -1081,7 +1083,7 @@ def stitch_tracklets(
     -------
     A TrackletStitcher object
     """
-    vids = collect_video_paths(videos, extensions=videotype)
+    vids = collect_video_paths(videos, extensions=extensions)
     if not vids:
         print("No video(s) found. Please check your path!")
         return
diff --git a/deeplabcut/utils/auxiliaryfunctions.py b/deeplabcut/utils/auxiliaryfunctions.py
index 73b0c97cd..abf27ae09 100644
--- a/deeplabcut/utils/auxiliaryfunctions.py
+++ b/deeplabcut/utils/auxiliaryfunctions.py
@@ -35,7 +35,7 @@
 from deeplabcut.core.trackingutils import TRACK_METHODS
 from deeplabcut.utils import auxfun_multianimal
 from deeplabcut.utils.auxfun_videos import SUPPORTED_VIDEOS, collect_video_paths
-from deeplabcut.utils.deprecation import deprecated
+from deeplabcut.utils.deprecation import deprecated, renamed_parameter
 
 
 def create_config_template(multianimal=False):
@@ -391,14 +391,15 @@ def write_pickle(filename, data):
 
 
 @deprecated(replacement="deeplabcut.collect_video_paths", since="3.0.0")
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def get_list_of_videos(
     videos: list[str] | str,
-    videotype: str | Sequence[str] | None = SUPPORTED_VIDEOS,
+    extensions: str | Sequence[str] | None = SUPPORTED_VIDEOS,
     in_random_order: bool = True,
 ) -> list[str]:
     video_paths = collect_video_paths(
         data_path=videos,
-        extensions=videotype,
+        extensions=extensions,
         shuffle=in_random_order,
     )
     return [str(path) for path in video_paths]
diff --git a/deeplabcut/utils/deprecation.py b/deeplabcut/utils/deprecation.py
index 8551929c5..5610e3ef1 100644
--- a/deeplabcut/utils/deprecation.py
+++ b/deeplabcut/utils/deprecation.py
@@ -11,6 +11,7 @@
 from __future__ import annotations
 
 import functools
+import inspect
 import warnings
 from collections.abc import Callable
 from typing import Literal, ParamSpec, TypeVar
@@ -128,9 +129,43 @@ def renamed_parameter(
         old: The old parameter name that callers may still pass.
         new: The current parameter name the function actually accepts.
         since: Version when the rename happened.
+
+    Rules:
+        - ``new`` must be the name used in the function signature and all
+          internal call-sites.  ``old`` must **not** appear in the signature.
+        - Do **not** chain renames.  If ``A`` was renamed to ``B`` and ``B``
+          is later renamed to ``C``, replace the ``A→B`` decorator with
+          ``A→C`` directly rather than stacking a second decorator.
+        - Multiple independent renames on the same function (e.g.
+          ``batchsize→batch_size`` *and* ``videotype→extensions``) are fine
+          as long as they do not form a chain.
+        - This decorator only intercepts **keyword** arguments.  Positional
+          arguments are passed through unchanged; renaming a parameter that
+          callers commonly pass positionally will not be caught.
     """
 
     def decorator(fn: Callable[P, R]) -> Callable[P, R]:
+        # Guard: 'new' must actually exist in the function's signature.
+        sig = inspect.signature(fn)
+        if new not in sig.parameters:
+            raise ValueError(
+                f"@renamed_parameter: '{new}' is not a parameter of "
+                f"{fn.__qualname__}. "
+                f"Available parameters: {list(sig.parameters)}"
+            )
+
+        # Guard: disallow chaining renames (A→B stacked on top of B→C).
+        existing = getattr(fn, "__deprecated_params__", ())
+        for prev in existing:
+            if prev.old_parameter == new:
+                raise ValueError(
+                    f"@renamed_parameter: chaining renames is not allowed. "
+                    f"'{old}' → '{new}' would chain with the existing "
+                    f"'{prev.old_parameter}' → '{prev.new_parameter}' rename "
+                    f"on {fn.__qualname__}. "
+                    f"Use '{old}' → '{prev.new_parameter}' directly instead."
+                )
+
         info = DeprecationInfo(
             kind="parameter",
             target=fn.__qualname__,
@@ -149,7 +184,6 @@ def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
                 kwargs[new] = kwargs.pop(old)
             return fn(*args, **kwargs)
 
-        existing = getattr(fn, "__deprecated_params__", ())
         wrapper.__deprecated_params__ = (*existing, info)
         return wrapper
 
diff --git a/deeplabcut/utils/make_labeled_video.py b/deeplabcut/utils/make_labeled_video.py
index c83778ba0..d8936ceb4 100644
--- a/deeplabcut/utils/make_labeled_video.py
+++ b/deeplabcut/utils/make_labeled_video.py
@@ -49,6 +49,7 @@
 from deeplabcut.core.engine import Engine
 from deeplabcut.utils import auxfun_multianimal, auxiliaryfunctions, visualization
 from deeplabcut.utils.auxfun_videos import VideoWriter, collect_video_paths
+from deeplabcut.utils.deprecation import renamed_parameter
 from deeplabcut.utils.video_processor import (
     VideoProcessorCV as vp,
 )  # used to CreateVideo
@@ -390,10 +391,11 @@ def CreateVideoSlow(
     plt.switch_backend(prev_backend)
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def create_labeled_video(
     config: str,
     videos: list[str],
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle: int = 1,
     trainingsetindex: int = 0,
     filtered: bool = False,
@@ -441,7 +443,7 @@ def create_labeled_video(
         A list of strings containing the full paths to videos for analysis or a path
         to the directory, where all the videos with same extension are stored.
 
-    videotype : str | Sequence[str] | None, optional, default=None
+    extensions : str | Sequence[str] | None, optional, default=None
         Controls how ``videos`` are filtered, based on file extension.
         File paths and directory contents are treated differently:
         - ``None`` (default): file paths are accepted as-is; directories are
@@ -633,7 +635,7 @@ def create_labeled_video(
     >>> deeplabcut.create_labeled_video(
             '/analysis/project/reaching-task/config.yaml',
             ['/analysis/project/videos/'],
-            videotype='mp4',
+            extensions='mp4',
         )
     """
     if skeleton is None:
@@ -736,7 +738,7 @@ def create_labeled_video(
         skeleton_color = None
 
     start_path = os.getcwd()
-    Videos = collect_video_paths(videos, extensions=videotype)
+    Videos = collect_video_paths(videos, extensions=extensions)
 
     if not Videos:
         return []
@@ -1147,10 +1149,11 @@ def create_video_with_keypoints_only(
     plt.switch_backend(prev_backend)
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def create_video_with_all_detections(
     config,
     videos,
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle=1,
     trainingsetindex=0,
     displayedbodyparts="all",
@@ -1172,7 +1175,7 @@ def create_video_with_all_detections(
         A list of strings containing the full paths to videos for analysis or a path to the directory,
         where all the videos with same extension are stored.
 
-    videotype : str | Sequence[str] | None, optional, default=None
+    extensions : str | Sequence[str] | None, optional, default=None
         Controls how ``videos`` are filtered, based on file extension.
         File paths and directory contents are treated differently:
         - ``None`` (default): file paths are accepted as-is; directories are
@@ -1232,7 +1235,7 @@ def create_video_with_all_detections(
         **kwargs,
     )
 
-    videos = collect_video_paths(videos, extensions=videotype)
+    videos = collect_video_paths(videos, extensions=extensions)
     if not videos:
         return
 
diff --git a/deeplabcut/utils/plotting.py b/deeplabcut/utils/plotting.py
index 51a8b575e..4ab6bcf25 100644
--- a/deeplabcut/utils/plotting.py
+++ b/deeplabcut/utils/plotting.py
@@ -38,6 +38,7 @@
 from deeplabcut.core import crossvalutils
 from deeplabcut.utils import auxfun_multianimal, auxiliaryfunctions, visualization
 from deeplabcut.utils.auxfun_videos import collect_video_paths
+from deeplabcut.utils.deprecation import renamed_parameter
 
 
 def Histogram(vector, color, bins, ax=None, linewidth=1.0):
@@ -169,10 +170,11 @@ def PlottingResults(
 ##################################################
 
 
+@renamed_parameter(old="videotype", new="extensions", since="3.0.0")
 def plot_trajectories(
     config,
     videos,
-    videotype: str | Sequence[str] | None = None,
+    extensions: str | Sequence[str] | None = None,
     shuffle=1,
     trainingsetindex=0,
     filtered=False,
@@ -199,7 +201,7 @@ def plot_trajectories(
         Full paths to videos for analysis or a path to the directory, where all the
         videos with same extension are stored.
 
-    videotype : str | Sequence[str] | None, optional, default=None
+    extensions : str | Sequence[str] | None, optional, default=None
         Controls how ``videos`` are filtered, based on file extension.
         File paths and directory contents are treated differently:
         - ``None`` (default): file paths are accepted as-is; directories are
@@ -293,9 +295,9 @@ def plot_trajectories(
     )  # automatically loads corresponding model (even training iteration based on snapshot index)
     bodyparts = auxiliaryfunctions.intersection_of_body_parts_and_ones_given_by_user(cfg, displayedbodyparts)
     individuals = auxfun_multianimal.IntersectionofIndividualsandOnesGivenbyUser(cfg, displayedindividuals)
-    Videos = collect_video_paths(videos, extensions=videotype)
+    Videos = collect_video_paths(videos, extensions=extensions)
     if not len(Videos):
-        print("No videos found. Make sure you passed a list of videos and that *videotype* is right.")
+        print("No videos found. Make sure you passed a list of videos and that extensions filter is right.")
         return
 
     failures, multianimal_errors = [], []
diff --git a/tests/utils/test_deprecation.py b/tests/utils/test_deprecation.py
index 5938619f7..1c189e32a 100644
--- a/tests/utils/test_deprecation.py
+++ b/tests/utils/test_deprecation.py
@@ -211,3 +211,57 @@ def test_renamed_parameter_invalid_since_raises():
         @renamed_parameter(old="videotype", new="extensions", since="invalid-version")
         def fn(extensions=None):
             return extensions
+
+
+def test_renamed_parameter_new_not_in_signature_raises():
+    with pytest.raises(ValueError, match="not a parameter"):
+
+        @renamed_parameter(old="foo", new="nonexistent")
+        def fn(bar=None):
+            return bar
+
+
+def test_new_not_in_signature_raises():
+    """Applying a rename whose 'new' is not in the signature raises an error."""
+    with pytest.raises(ValueError, match="not a parameter"):
+
+        @renamed_parameter(old="videotype", new="video_type")
+        def fn(extensions=None):
+            return extensions
+
+
+def test_renamed_parameter_chaining_raises():
+    """Chaining renames A→B→C raises an error."""
+    with pytest.raises(ValueError, match="chaining renames is not allowed"):
+
+        @renamed_parameter(old="A", new="B")  # outer: A→B, but B is already deprecated to C
+        @renamed_parameter(old="B", new="C")  # inner: B→C
+        def fn(B=None, C=None):
+            return C or B
+
+
+def test_renamed_parameter_multiple_independent_renames():
+    @renamed_parameter(old="batchsize", new="batch_size")
+    @renamed_parameter(old="videotype", new="extensions")
+    def fn(extensions=None, batch_size=None):
+        return extensions, batch_size
+
+    with pytest.warns(DLCDeprecationWarning):
+        result = fn(videotype="mp4")
+    assert result == ("mp4", None)
+
+    with pytest.warns(DLCDeprecationWarning):
+        result = fn(batchsize=4)
+    assert result == (None, 4)
+
+
+def test_renamed_parameter_positional_arg_unaffected():
+    @renamed_parameter(old="in_random_order", new="shuffle")
+    def fn(shuffle=False):
+        return shuffle
+
+    with warnings.catch_warnings():
+        warnings.simplefilter("error", DLCDeprecationWarning)
+        result = fn(True)
+
+    assert result is True