diff --git a/README.md b/README.md
index fe42de6cc..e8e15f78e 100644
--- a/README.md
+++ b/README.md
@@ -39,7 +39,7 @@ Current testing and maintenance work is focused on modern Linux, macOS, and Wind
 
 ## Installation
 
-Install phy in a fresh Python 3.10+ environment:
+Install phy in a fresh Python 3.10-3.13 environment:
 
 ```bash
 python -m pip install --upgrade pip
@@ -48,12 +48,82 @@ pip install phy
 
 This installs the GUI runtime dependencies as part of the main package.
 
-If you plan to use the legacy Kwik GUI, also install:
+phy runs on numpy 2 and Python 3.13.
+
+### Reclustering
+
+phy can recluster the selected clusters from their PC features, using ISO-SPLIT
+(the algorithm MountainSort uses) or a Gaussian mixture. No spike detection is
+re-run, so this needs no legacy dependencies: scikit-learn and isosplit are pure
+Python and numpy 2 compatible, and it works on Python 3.13.
 
 ```bash
-pip install klusta klustakwik2
+pip install "phy[kwik]"
+```
+
+Reclustering is **opt-in**: pass `--recluster` to enable it for a run. Without the
+flag the GUI behaves exactly as before, so it never changes a normal session.
+
+```bash
+phy template-gui path/to/params.py              # no reclustering
+phy template-gui path/to/params.py --recluster  # reclustering enabled
+```
+
+No configuration file is needed -- the plugin ships inside the phy package
+(`phy/plugins/recluster.py`) and the flag enables it directly. It adds three
+actions:
+
+| shortcut | action |
+| --- | --- |
+| `alt+k` | ISO-SPLIT, which picks the number of subclusters itself |
+| `shift+alt+k` | split into a number of subclusters you give, using a Gaussian mixture |
+| `ctrl+alt+k` | ISO-SPLIT at a given dip threshold; lower it below 2 to split more readily |
+
+If you want it on for every run without typing the flag, enable it in
+`~/.phy/phy_config.py` instead:
+
+```python
+c = get_config()
+c.TemplateGUI.plugins = ['ExampleReclusterPlugin']
 ```
 
+Running `--recluster` without the optional dependencies installed leaves the
+actions out and logs how to get them, rather than failing.
+
+### Installing from a git checkout
+
+To run phy from this repository rather than from PyPI:
+
+```bash
+conda create -n phy python=3.13 -y
+conda activate phy
+
+git clone https://github.com/cortex-lab/phy.git
+cd phy
+pip install -e .
+```
+
+`-e` (editable) means `git pull` updates your install with no reinstall step.
+Drop the `-e` for a plain copy. To install straight from GitHub without a local
+clone:
+
+```bash
+pip install "phy @ git+https://github.com/cortex-lab/phy.git"
+```
+
+### The legacy Kwik GUI (`.kwik` files)
+
+**Not supported.** The Kwik GUI needs `klusta` and `klustakwik2`, which are
+unmaintained: klusta's spike detection uses `np.bool`/`np.int`/`np.object`
+(removed in numpy 1.24) and klustakwik2 compiles against the numpy 1.x C API.
+Together they cap an install at numpy 1.23 and Python 3.11, which is
+incompatible with the numpy 2 and Python 3.13 support above.
+
+The `kwik` extra therefore installs the reclustering dependencies only, not the
+legacy stack. Use `phy template-gui params.py` on a Kilosort output directory.
+If you need to open `.kwik` files, use an older phy release in a separate
+Python 3.11 environment.
+
 ## Quick start
 
 Open the Template GUI on a spike sorting output directory containing `params.py`:
diff --git a/phy/apps/__init__.py b/phy/apps/__init__.py
index db51602ea..38e2dc500 100644
--- a/phy/apps/__init__.py
+++ b/phy/apps/__init__.py
@@ -149,12 +149,23 @@ def cli_trace_gui(ctx, dat_path, **kwargs):
 
 @phycli.command('template-gui')  # pragma: no cover
 @click.argument('params-path', type=click.Path(exists=True))
+@click.option(
+    '--recluster',
+    is_flag=True,
+    default=False,
+    help='Enable the recluster actions (alt+k). Requires `pip install "phy[kwik]"`.',
+)
 @_gui_command
 @click.pass_context
-def cli_template_gui(ctx, params_path, **kwargs):
+def cli_template_gui(ctx, params_path, recluster=False, **kwargs):
     """Launch the template GUI on a params.py file."""
     from .template.gui import template_gui
 
+    # The recluster plugin is bundled with phy but never attached on its own, so that
+    # a plain `phy template-gui` run behaves exactly as before.
+    if recluster:
+        kwargs['plugins'] = list(kwargs.get('plugins') or []) + ['ExampleReclusterPlugin']
+
     prof = __builtins__.get('profile', None)
     with capture_exceptions():
         if prof:
diff --git a/phy/plugins/__init__.py b/phy/plugins/__init__.py
new file mode 100644
index 000000000..90f9f8098
--- /dev/null
+++ b/phy/plugins/__init__.py
@@ -0,0 +1,13 @@
+"""Plugins bundled with phy.
+
+Importing this package imports every bundled plugin module, which is what
+registers the plugin classes in `IPluginRegistry`. `attach_plugins()` imports it
+before resolving plugin names, so a bundled plugin listed in a controller's
+`default_plugins` is found without any user configuration.
+
+Bundled plugin modules must stay cheap to import: heavy optional dependencies
+(scikit-learn, isosplit, ...) are imported inside functions, never at module
+level, so that `import phy` does not pull them in.
+"""
+
+from . import recluster  # noqa: F401
diff --git a/phy/plugins/recluster.py b/phy/plugins/recluster.py
new file mode 100644
index 000000000..4e19523f5
--- /dev/null
+++ b/phy/plugins/recluster.py
@@ -0,0 +1,194 @@
+"""Recluster action based on the PC features.
+
+This is the Template GUI counterpart of the Kwik GUI's `recluster` action. No
+spike detection is re-run: the spikes and their PC features already exist in the
+sorting output, and only the cluster assignment is recomputed.
+
+The default algorithm is ISO-SPLIT, the one MountainSort uses, if the `isosplit`
+package is installed. It is non-parametric: it decides how many subclusters there
+are by testing each candidate split for unimodality, so it does not assume the
+clusters are Gaussian. Bursting and drifting units are not, which is why a
+Gaussian mixture tends to cut them in half. The mixture is kept as a fallback and
+for the case where you want to impose the number of subclusters yourself.
+
+This plugin is bundled with phy and attached by default (see
+`TemplateController.default_plugins`). Its dependencies are optional and install
+with `pip install "phy[recluster]"`; without them the actions are not added and a
+message says how to get them.
+"""
+
+import importlib.util
+import logging
+
+import numpy as np
+
+from phy import IPlugin, connect
+
+logger = logging.getLogger('phy')
+
+# Number of subclusters tried when the count is chosen automatically by the mixture.
+MAX_CLUSTERS = 8
+# The GMM is fit on at most this many spikes, then applied to all of them.
+MAX_SPIKES_FIT = 20000
+# Features are reduced to at most this many dimensions before clustering.
+MAX_DIMS = 10
+# Passed to isosplit(). LOWERING it yields more subclusters -- note that this is the
+# opposite of what the isosplit 0.1.4 docstring claims. Lowering it is a blunt
+# instrument: on synthetic data, 1.5 already splits a genuinely single unit in two,
+# and the count is not monotonic in the threshold. To split a cluster ISO-SPLIT
+# considers unimodal, imposing the number of subclusters is the controlled option.
+DIP_THRESHOLD = 2.0
+
+
+def _has(name):
+    """Whether a module is importable, without importing it."""
+    try:
+        return importlib.util.find_spec(name) is not None
+    except (ImportError, ValueError):  # pragma: no cover
+        return False
+
+
+def _reduce(x):
+    """Cut the features down to a few dimensions.
+
+    Deliberately not whitened. Only the leading components carry the cluster
+    separation; the rest are noise, and rescaling them all to unit variance
+    amplifies the noise directions until the separation is buried.
+    """
+    from sklearn.decomposition import PCA
+
+    n_components = min(MAX_DIMS, x.shape[1], x.shape[0])
+    return PCA(n_components=n_components, whiten=False, random_state=0).fit_transform(x)
+
+
+def _isosplit(x, dip_threshold=None):
+    """Cluster an array with ISO-SPLIT. Returns None if the package is missing."""
+    try:
+        from isosplit import isosplit
+    except ImportError:
+        return None
+    dip = DIP_THRESHOLD if dip_threshold is None else dip_threshold
+    # isosplit() labels from 1; phy wants them relative to the selection.
+    return isosplit(x, dip_threshold=dip) - 1
+
+
+def _fit_predict(x, n_clusters=None):
+    """Cluster an array with a Gaussian mixture, choosing the number of components by BIC."""
+    from sklearn.mixture import GaussianMixture
+
+    # Fit on a subsample of large clusters, so that the action stays responsive.
+    if len(x) > MAX_SPIKES_FIT:
+        rng = np.random.default_rng(0)
+        x_fit = x[rng.choice(len(x), MAX_SPIKES_FIT, replace=False)]
+    else:
+        x_fit = x
+
+    def _gmm(n):
+        return GaussianMixture(n_components=n, covariance_type='full', random_state=0).fit(x_fit)
+
+    if n_clusters:
+        best = _gmm(n_clusters)
+    else:
+        # The number of components is capped by the sample size, as a full covariance
+        # matrix cannot be estimated from fewer spikes than dimensions.
+        max_clusters = max(1, min(MAX_CLUSTERS, len(x_fit) // (x.shape[1] + 1)))
+        candidates = [_gmm(n) for n in range(1, max_clusters + 1)]
+        best = min(candidates, key=lambda gmm: gmm.bic(x_fit))
+        logger.info('Selected %d subclusters by BIC.', best.n_components)
+
+    return best.predict(x)
+
+
+class ExampleReclusterPlugin(IPlugin):
+    def attach_to_controller(self, controller):
+        # scikit-learn drives both the PCA reduction and the mixture fallback, so
+        # without it there is no working code path: skip rather than register
+        # actions that would raise when pressed.
+        if not _has('sklearn'):
+            logger.debug(
+                'scikit-learn is not installed: the recluster actions are disabled. '
+                'Install them with `pip install "phy[recluster]"`.'
+            )
+            return
+
+        def _get_spike_ids(cluster_ids):
+            """Return all spikes of the selected clusters that have features."""
+            spike_ids = controller.supervisor.clustering.spikes_in_clusters(cluster_ids)
+            # Some models only store features for a subset of the spikes.
+            features_rows = getattr(controller.model, 'features_rows', None)
+            if features_rows is not None:  # pragma: no cover
+                spike_ids = np.intersect1d(spike_ids, features_rows)
+            return spike_ids
+
+        def _get_channel_ids(cluster_ids):
+            """Return the union of the best channels of the selected clusters."""
+            channel_ids = [controller.get_best_channels(c) for c in cluster_ids]
+            return np.unique(np.concatenate(channel_ids))
+
+        def _recluster(n_clusters=None, dip_threshold=None):
+            cluster_ids = controller.supervisor.selected
+            if not cluster_ids:
+                logger.warning('No cluster selected, cannot recluster.')
+                return
+
+            spike_ids = _get_spike_ids(cluster_ids)
+            if len(spike_ids) < 2:
+                logger.warning('Not enough spikes with features, cannot recluster.')
+                return
+
+            # Same features as the ones the feature view is showing. We call
+            # _get_spike_features() rather than _get_features(), because the latter is
+            # disk-cached and we would be caching every cluster we ever recluster.
+            channel_ids = _get_channel_ids(cluster_ids)
+            bunch = controller._get_spike_features(spike_ids, channel_ids)
+
+            # (n_spikes, n_channels, n_pcs) -> (n_spikes, n_channels * n_pcs)
+            x = _reduce(bunch.data.reshape((len(spike_ids), -1)))
+            logger.info('Reclustering %d spikes on %d channels.', len(spike_ids), len(channel_ids))
+
+            # ISO-SPLIT picks the number of subclusters itself, so it cannot honour an
+            # explicit n_clusters: fall back to the mixture in that case.
+            labels = None if n_clusters else _isosplit(x, dip_threshold=dip_threshold)
+            if labels is None:
+                labels = _fit_predict(x, n_clusters=n_clusters)
+            assert labels.shape == spike_ids.shape
+
+            if len(np.unique(labels)) == 1:
+                logger.info(
+                    'Reclustering found a single cluster, nothing to split. Lower the dip '
+                    'threshold, or impose the number of subclusters, to split it anyway.'
+                )
+                return
+            controller.supervisor.actions.split(spike_ids, labels)
+
+        @connect
+        def on_gui_ready(sender, gui):
+            @controller.supervisor.actions.add(shortcut='alt+k', set_busy=True)
+            def recluster():
+                """Recluster the selected clusters with ISO-SPLIT, which picks the number
+                of subclusters itself."""
+                _recluster()
+
+            @controller.supervisor.actions.add(
+                shortcut='shift+alt+k',
+                set_busy=True,
+                prompt=True,
+                n_args=1,
+                prompt_default=lambda: 2,
+            )
+            def recluster_n(n_clusters):
+                """Recluster the selected clusters into a given number of subclusters,
+                using a Gaussian mixture."""
+                _recluster(n_clusters=int(n_clusters))
+
+            @controller.supervisor.actions.add(
+                shortcut='ctrl+alt+k',
+                set_busy=True,
+                prompt=True,
+                n_args=1,
+                prompt_default=lambda: DIP_THRESHOLD,
+            )
+            def recluster_dip(dip_threshold):
+                """Recluster with ISO-SPLIT at a given dip threshold. Lower it below the
+                default of 2 to make it split more readily."""
+                _recluster(dip_threshold=float(dip_threshold))
diff --git a/phy/utils/plugin.py b/phy/utils/plugin.py
index 813bffb0c..0d487058f 100644
--- a/phy/utils/plugin.py
+++ b/phy/utils/plugin.py
@@ -137,12 +137,23 @@ class name of the Controller instance, plus those specified in the plugins keywo
     config = load_master_config(config_dir=config_dir)
     name = getattr(controller, 'gui_name', None) or controller.__class__.__name__
     c = config.get(name)
+    # Import the plugins bundled with phy, so that they register themselves in
+    # IPluginRegistry and can be enabled by name -- by the `--recluster` command-line
+    # flag, or from the user config -- without configuring Plugins.dirs. Bundled
+    # plugins are never attached on their own: they have to be asked for.
+    try:
+        importlib.import_module('phy.plugins')
+    except Exception as e:  # pragma: no cover
+        logger.warning('Could not import the bundled plugins: %s.', e)
     # Discover plugin files in the plugin directories, as specified in the phy config file.
     dirs = (dirs or []) + config.get('Plugins', {}).get('dirs', [])
     discover_plugins(dirs)
     default_plugins = c.plugins if c else []
     if len(default_plugins):
         plugins = default_plugins + plugins
+    # Collapse duplicates, so that a plugin named both in the user config and on the
+    # command line is attached once.
+    plugins = list(dict.fromkeys(plugins))
     logger.debug('Loading %d plugins.', len(plugins))
     attached = []
     for plugin in plugins:
diff --git a/plugins/README.md b/plugins/README.md
index f47dfbcb3..96dd9d46f 100644
--- a/plugins/README.md
+++ b/plugins/README.md
@@ -1,5 +1,12 @@
 # phy plugin examples
 
+To enable any of these, phy needs both the plugin *directory* (`c.Plugins.dirs`)
+and the plugin *name* (`c.<GUIName>.plugins`) in `~/.phy/phy_config.py`.
+
+The recluster plugin is an exception: it is no longer an example but ships inside
+the phy package at `phy/plugins/recluster.py`, and the Template GUI attaches it
+automatically. See [Reclustering](../README.md#reclustering).
+
 * [ExampleActionPlugin](action_status_bar.py): Show how to create new actions in the GUI.
 * [ExampleClusterMetadataPlugin](cluster_metadata.py): Show how to save the best channel of every cluster in a cluster_channel.tsv file when saving.
 * [ExampleClusterMetricsPlugin](cluster_metrics.py): Show how to add a custom cluster metrics.
diff --git a/pyproject.toml b/pyproject.toml
index 79c604d5b..d4a466455 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -22,7 +22,10 @@ classifiers = [
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
     "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
 ]
+# The legacy numpy<2 / numpy<1.24 pins live in the `kwik` extra, not here, so that
+# regular phy users get numpy 2 and Python 3.13.
 requires-python = ">=3.10"
 
 # specific version required for pyopengl >=3.1.9
@@ -34,7 +37,7 @@ dependencies = [
     "joblib",
     "matplotlib",
     "mtscomp",
-    "numpy",
+    "numpy>=1.23",
     "pillow",
     "pip",
     "PyQt5>=5.15.11,<5.16",
@@ -55,6 +58,22 @@ Repository = "https://github.com/cortex-lab/phy"
 Documentation = "https://phy.readthedocs.io/en/latest/"
 
 [project.optional-dependencies]
+# Reclustering. Installs the optional dependencies of the recluster plugin
+# (phy/plugins/recluster.py), which both GUIs attach by default.
+# scikit-learn provides the PCA reduction and the Gaussian-mixture fallback;
+# isosplit 0.1.4 is the version the plugin targets -- its signature
+# isosplit(data, *, dip_threshold=2, ...) matches the plugin's call exactly.
+# Both are pure Python and declare no numpy upper bound, so this extra resolves
+# on Python 3.13 with numpy 2.
+#
+# NOTE: this extra no longer installs the legacy Kwik stack (klusta,
+# klustakwik2). Those cap the install at numpy<1.24 and Python 3.11, which is
+# incompatible with the numpy 2 / Python 3.13 support above. Opening `.kwik`
+# files is therefore not supported; use `phy template-gui params.py`.
+kwik = [
+    "scikit-learn>=1.9.0",
+    "isosplit==0.1.4",
+]
 dev = [
     "Cython>=3.0",
     "pytest",