diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py
index c15948ce877a8..b0f8a0b7a96af 100644
--- a/pandas/core/groupby/groupby.py
+++ b/pandas/core/groupby/groupby.py
@@ -2004,8 +2004,6 @@ def mean(
             return result.__finalize__(self.obj, method="groupby")
 
     @final
-    @Substitution(name="groupby")
-    @Appender(_common_see_also)
     def median(self, numeric_only: bool = False):
         """
         Compute median of groups, excluding missing values.
@@ -2315,8 +2313,6 @@ def _value_counts(
             return result.__finalize__(self.obj, method="value_counts")
 
     @final
-    @Substitution(name="groupby")
-    @Appender(_common_see_also)
     def sem(self, ddof: int = 1, numeric_only: bool = False):
         """
         Compute standard error of the mean of groups, excluding missing values.
@@ -2471,7 +2467,6 @@ def max(
             )
 
     @final
-    @Substitution(name="groupby")
     def first(self, numeric_only: bool = False, min_count: int = -1):
         """
         Compute the first non-null entry of each column.
@@ -2542,7 +2537,6 @@ def first(x: Series):
         )
 
     @final
-    @Substitution(name="groupby")
     def last(self, numeric_only: bool = False, min_count: int = -1):
         """
         Compute the last non-null entry of each column.
@@ -2602,8 +2596,6 @@ def last(x: Series):
         )
 
     @final
-    @Substitution(name="groupby")
-    @Appender(_common_see_also)
     def ohlc(self) -> DataFrame:
         """
         Compute open, high, low and close values of a group, excluding missing values.
diff --git a/pandas/core/resample.py b/pandas/core/resample.py
index 907d6522d3236..ac303e4b1f0bf 100644
--- a/pandas/core/resample.py
+++ b/pandas/core/resample.py
@@ -895,6 +895,74 @@ def asfreq(self, fill_value=None):
         """
         return self._upsample("asfreq", fill_value=fill_value)
 
+    def sum(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        nv.validate_resampler_func("sum", args, kwargs)
+        return self._downsample("sum", numeric_only=numeric_only, min_count=min_count)
+
+    @doc(GroupBy.prod)
+    def prod(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        nv.validate_resampler_func("prod", args, kwargs)
+        return self._downsample("prod", numeric_only=numeric_only, min_count=min_count)
+
+    def min(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        nv.validate_resampler_func("min", args, kwargs)
+        return self._downsample("min", numeric_only=numeric_only, min_count=min_count)
+
+    def max(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        nv.validate_resampler_func("max", args, kwargs)
+        return self._downsample("max", numeric_only=numeric_only, min_count=min_count)
+
+    @doc(GroupBy.first)
+    def first(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        nv.validate_resampler_func("first", args, kwargs)
+        return self._downsample("first", numeric_only=numeric_only, min_count=min_count)
+
+    @doc(GroupBy.last)
+    def last(
+        self,
+        numeric_only: bool = False,
+        min_count: int = 0,
+        *args,
+        **kwargs,
+    ):
+        nv.validate_resampler_func("last", args, kwargs)
+        return self._downsample("last", numeric_only=numeric_only, min_count=min_count)
+
+    @doc(GroupBy.median)
+    def median(self, numeric_only: bool = False, *args, **kwargs):
+        nv.validate_resampler_func("median", args, kwargs)
+        return self._downsample("median", numeric_only=numeric_only)
+
     def mean(
         self,
         numeric_only: bool = False,
@@ -984,6 +1052,35 @@ def var(
         nv.validate_resampler_func("var", args, kwargs)
         return self._downsample("var", ddof=ddof, numeric_only=numeric_only)
 
+    @doc(GroupBy.sem)
+    def sem(
+        self,
+        ddof: int = 1,
+        numeric_only: bool = False,
+        *args,
+        **kwargs,
+    ):
+        nv.validate_resampler_func("sem", args, kwargs)
+        return self._downsample("sem", ddof=ddof, numeric_only=numeric_only)
+
+    @doc(GroupBy.ohlc)
+    def ohlc(
+        self,
+        *args,
+        **kwargs,
+    ):
+        nv.validate_resampler_func("ohlc", args, kwargs)
+        return self._downsample("ohlc")
+
+    @doc(SeriesGroupBy.nunique)
+    def nunique(
+        self,
+        *args,
+        **kwargs,
+    ):
+        nv.validate_resampler_func("nunique", args, kwargs)
+        return self._downsample("nunique")
+
     @doc(GroupBy.size)
     def size(self):
         result = self._downsample("size")
@@ -1047,87 +1144,6 @@ def quantile(self, q: float | AnyArrayLike = 0.5, **kwargs):
         return self._downsample("quantile", q=q, **kwargs)
 
 
-def _add_downsample_kernel(
-    name: str, args: tuple[str, ...], docs_class: type = GroupBy
-) -> None:
-    """
-    Add a kernel to Resampler.
-
-    Arguments
-    ---------
-    name : str
-        Name of the kernel.
-    args : tuple
-        Arguments of the method.
-    docs_class : type
-        Class to get kernel docstring from.
-    """
-    assert args in (
-        ("numeric_only", "min_count"),
-        ("numeric_only",),
-        ("ddof", "numeric_only"),
-        (),
-    )
-
-    # Explicitly provide args rather than args/kwargs for API docs
-    if args == ("numeric_only", "min_count"):
-
-        def f(
-            self,
-            numeric_only: bool = False,
-            min_count: int = 0,
-            *args,
-            **kwargs,
-        ):
-            nv.validate_resampler_func(name, args, kwargs)
-            return self._downsample(
-                name, numeric_only=numeric_only, min_count=min_count
-            )
-
-    elif args == ("numeric_only",):
-        # error: All conditional function variants must have identical signatures
-        def f(self, numeric_only: bool = False, *args, **kwargs):  # type: ignore[misc]
-            nv.validate_resampler_func(name, args, kwargs)
-            return self._downsample(name, numeric_only=numeric_only)
-
-    elif args == ("ddof", "numeric_only"):
-        # error: All conditional function variants must have identical signatures
-        def f(  # type: ignore[misc]
-            self,
-            ddof: int = 1,
-            numeric_only: bool = False,
-            *args,
-            **kwargs,
-        ):
-            nv.validate_resampler_func(name, args, kwargs)
-            return self._downsample(name, ddof=ddof, numeric_only=numeric_only)
-
-    else:
-        # error: All conditional function variants must have identical signatures
-        def f(  # type: ignore[misc]
-            self,
-            *args,
-            **kwargs,
-        ):
-            nv.validate_resampler_func(name, args, kwargs)
-            return self._downsample(name)
-
-    f.__doc__ = getattr(docs_class, name).__doc__
-    setattr(Resampler, name, f)
-
-
-for _method in ["sum", "prod", "min", "max", "first", "last"]:
-    _add_downsample_kernel(_method, ("numeric_only", "min_count"))
-for _method in ["median"]:
-    _add_downsample_kernel(_method, ("numeric_only",))
-for _method in ["sem"]:
-    _add_downsample_kernel(_method, ("ddof", "numeric_only"))
-for _method in ["ohlc"]:
-    _add_downsample_kernel(_method, ())
-for _method in ["nunique"]:
-    _add_downsample_kernel(_method, (), SeriesGroupBy)
-
-
 class _GroupByMixin(PandasObject):
     """
     Provide the groupby facilities.