diff --git a/nwb_linkml/src/nwb_linkml/adapters/adapter.py b/nwb_linkml/src/nwb_linkml/adapters/adapter.py
index 561df65..6823bb0 100644
--- a/nwb_linkml/src/nwb_linkml/adapters/adapter.py
+++ b/nwb_linkml/src/nwb_linkml/adapters/adapter.py
@@ -26,7 +26,7 @@ from linkml_runtime.linkml_model import (
 )
 from pydantic import BaseModel
 
-from nwb_schema_language import Attribute, Dataset, Group, Schema
+from nwb_schema_language import Attribute, Dataset, Group, Schema, CompoundDtype
 
 if sys.version_info.minor >= 11:
     from typing import TypeVarTuple, Unpack
@@ -238,3 +238,36 @@ class Adapter(BaseModel):
         for item in self.walk(input):
             if any([type(item) is atype for atype in get_type]):
                 yield item
+
+
+def is_1d(cls: Dataset | Attribute) -> bool:
+    """
+    Check if the values of a dataset are 1-dimensional.
+
+    Specifically:
+    * a single-layer dim/shape list of length 1, or
+    * a nested dim/shape list where every nested spec is of length 1
+    """
+    return (
+        not any([isinstance(dim, list) for dim in cls.dims]) and len(cls.dims) == 1
+    ) or (  # nested list
+        all([isinstance(dim, list) for dim in cls.dims])
+        and len(cls.dims) == 1
+        and len(cls.dims[0]) == 1
+    )
+
+
+def is_compound(cls: Dataset) -> bool:
+    """Check if dataset has a compound dtype"""
+    return (
+        isinstance(cls.dtype, list)
+        and len(cls.dtype) > 0
+        and isinstance(cls.dtype[0], CompoundDtype)
+    )
+
+
+def has_attrs(cls: Dataset) -> bool:
+    """
+    Check if a dataset has any attributes at all without defaults
+    """
+    return len(cls.attributes) > 0 and all([not a.value for a in cls.attributes])
diff --git a/nwb_linkml/src/nwb_linkml/adapters/attribute.py b/nwb_linkml/src/nwb_linkml/adapters/attribute.py
new file mode 100644
index 0000000..c7ebd19
--- /dev/null
+++ b/nwb_linkml/src/nwb_linkml/adapters/attribute.py
@@ -0,0 +1,194 @@
+"""
+Adapters for attribute types
+"""
+
+from abc import abstractmethod
+from typing import ClassVar, Optional, TypedDict, Type
+
+from linkml_runtime.linkml_model.meta import SlotDefinition
+
+from nwb_linkml.adapters.array import ArrayAdapter
+from nwb_linkml.adapters.adapter import BuildResult, is_1d, Adapter
+from nwb_linkml.maps import Map
+from nwb_linkml.maps.dtype import handle_dtype
+from nwb_schema_language import Attribute
+
+
+def _make_ifabsent(val: str | int | float | None) -> str | None:
+    if val is None:
+        return None
+    elif isinstance(val, str):
+        return f"string({val})"
+    elif isinstance(val, int):
+        return f"integer({val})"
+    elif isinstance(val, float):
+        return f"float({val})"
+    else:
+        return str(value)
+
+
+class AttrDefaults(TypedDict):
+    equals_string: str | None
+    equals_number: float | int | None
+    ifabsent: str | None
+
+
+class AttributeMap(Map):
+
+    @classmethod
+    def handle_defaults(cls, attr: Attribute) -> AttrDefaults:
+        """
+        Construct arguments for linkml slot default metaslots from nwb schema lang attribute props
+        """
+        equals_string = None
+        equals_number = None
+        default_value = None
+        if attr.value:
+            if isinstance(attr.value, (int, float)):
+                equals_number = attr.value
+            elif attr.value:
+                equals_string = str(attr.value)
+
+        if equals_number:
+            default_value = _make_ifabsent(equals_number)
+        elif equals_string:
+            default_value = _make_ifabsent(equals_string)
+        elif attr.default_value:
+            default_value = _make_ifabsent(attr.default_value)
+
+        return AttrDefaults(
+            equals_string=equals_string, equals_number=equals_number, ifabsent=default_value
+        )
+
+    @classmethod
+    @abstractmethod
+    def check(cls, attr: Attribute) -> bool:
+        """
+        Check if this map applies
+        """
+        pass  # pragma: no cover
+
+    @classmethod
+    @abstractmethod
+    def apply(
+        cls, attr: Attribute, res: Optional[BuildResult] = None, name: Optional[str] = None
+    ) -> BuildResult:
+        """
+        Apply this mapping
+        """
+        pass  # pragma: no cover
+
+
+class MapScalar(AttributeMap):
+    """
+    Map a simple scalar value
+    """
+
+    @classmethod
+    def check(cls, attr: Attribute) -> bool:
+        """
+        Check if we are a scalar value!
+        """
+        return not attr.dims and not attr.shape
+
+    @classmethod
+    def apply(cls, attr: Attribute, res: Optional[BuildResult] = None) -> BuildResult:
+        """
+        Make a slot for us!
+        """
+        slot = SlotDefinition(
+            name=attr.name,
+            range=handle_dtype(attr.dtype),
+            description=attr.doc,
+            required=attr.required,
+            **cls.handle_defaults(attr),
+        )
+        return BuildResult(slots=[slot])
+
+
+class MapArray(AttributeMap):
+    """
+    Map an array value!
+    """
+
+    @classmethod
+    def check(cls, attr: Attribute) -> bool:
+        """
+        Check that we have some array specification!
+        """
+        return bool(attr.dims) or attr.shape
+
+    @classmethod
+    def apply(cls, attr: Attribute, res: Optional[BuildResult] = None) -> BuildResult:
+        """
+        Make a slot with an array expression!
+
+        If we're just a 1D array, use a list (set multivalued: true).
+        If more than that, make an array descriptor
+        """
+        expressions = {}
+        multivalued = False
+        if is_1d(attr):
+            multivalued = True
+        else:
+            # ---------------------------------
+            # SPECIAL CASE: Some old versions of HDMF don't have ``dims``, only shape
+            # ---------------------------------
+            shape = attr.shape
+            dims = attr.dims
+            if shape and not dims:
+                dims = ["null"] * len(shape)
+
+            array_adapter = ArrayAdapter(dims, shape)
+            expressions = array_adapter.make_slot()
+
+        slot = SlotDefinition(
+            name=attr.name,
+            range=handle_dtype(attr.dtype),
+            multivalued=multivalued,
+            description=attr.doc,
+            required=attr.required,
+            **expressions,
+            **cls.handle_defaults(attr),
+        )
+        return BuildResult(slots=[slot])
+
+
+class AttributeAdapter(Adapter):
+    """
+    Create slot definitions from nwb schema language attributes
+    """
+
+    TYPE: ClassVar[Type] = Attribute
+
+    cls: Attribute
+
+    def build(self) -> "BuildResult":
+        """
+        Build the slot definitions, every attribute should have a map.
+        """
+        map = self.match()
+        return map.apply(self.cls)
+
+    def match(self) -> Optional[Type[AttributeMap]]:
+        """
+        Find the map class that applies to this attribute
+
+        Returns:
+            :class:`.AttributeMap`
+
+        Raises:
+            RuntimeError - if more than one map matches
+        """
+        # find a map to use
+        matches = [m for m in AttributeMap.__subclasses__() if m.check(self.cls)]
+
+        if len(matches) > 1:  # pragma: no cover
+            raise RuntimeError(
+                "Only one map should apply to a dataset, you need to refactor the maps! Got maps:"
+                f" {matches}"
+            )
+        elif len(matches) == 0:
+            return None
+        else:
+            return matches[0]
diff --git a/nwb_linkml/src/nwb_linkml/adapters/classes.py b/nwb_linkml/src/nwb_linkml/adapters/classes.py
index fcdf43c..054a401 100644
--- a/nwb_linkml/src/nwb_linkml/adapters/classes.py
+++ b/nwb_linkml/src/nwb_linkml/adapters/classes.py
@@ -9,9 +9,10 @@ from linkml_runtime.linkml_model import ClassDefinition, SlotDefinition
 from pydantic import field_validator
 
 from nwb_linkml.adapters.adapter import Adapter, BuildResult
+from nwb_linkml.adapters.attribute import AttributeAdapter
 from nwb_linkml.maps import QUANTITY_MAP
 from nwb_linkml.maps.naming import camel_to_snake
-from nwb_schema_language import CompoundDtype, Dataset, DTypeType, FlatDtype, Group, ReferenceDtype
+from nwb_schema_language import Dataset, Group
 
 T = TypeVar("T", bound=Type[Dataset] | Type[Group])
 TI = TypeVar("TI", bound=Dataset | Group)
@@ -118,16 +119,9 @@ class ClassAdapter(Adapter):
         Returns:
             list[:class:`.SlotDefinition`]
         """
-        attrs = [
-            SlotDefinition(
-                name=attr.name,
-                description=attr.doc,
-                range=self.handle_dtype(attr.dtype),
-            )
-            for attr in cls.attributes
-        ]
-
-        return attrs
+        results = [AttributeAdapter(cls=attr).build() for attr in cls.attributes]
+        slots = [r.slots[0] for r in results]
+        return slots
 
     def _get_full_name(self) -> str:
         """The full name of the object in the generated linkml
@@ -205,37 +199,6 @@ class ClassAdapter(Adapter):
 
         return name
 
-    @classmethod
-    def handle_dtype(cls, dtype: DTypeType | None) -> str:
-        """
-        Get the string form of a dtype
-
-        Args:
-            dtype (:class:`.DTypeType`): Dtype to stringify
-
-        Returns:
-            str
-        """
-        if isinstance(dtype, ReferenceDtype):
-            return dtype.target_type
-        elif dtype is None or dtype == []:
-            # Some ill-defined datasets are "abstract" despite that not being in the schema language
-            return "AnyType"
-        elif isinstance(dtype, FlatDtype):
-            return dtype.value
-        elif isinstance(dtype, list) and isinstance(dtype[0], CompoundDtype):
-            # there is precisely one class that uses compound dtypes:
-            # TimeSeriesReferenceVectorData
-            # compoundDtypes are able to define a ragged table according to the schema
-            # but are used in this single case equivalently to attributes.
-            # so we'll... uh... treat them as slots.
-            # TODO
-            return "AnyType"
-
-        else:
-            # flat dtype
-            return dtype
-
     def build_name_slot(self) -> SlotDefinition:
         """
         If a class has a name, then that name should be a slot with a
diff --git a/nwb_linkml/src/nwb_linkml/adapters/dataset.py b/nwb_linkml/src/nwb_linkml/adapters/dataset.py
index e9268cb..bcbd109 100644
--- a/nwb_linkml/src/nwb_linkml/adapters/dataset.py
+++ b/nwb_linkml/src/nwb_linkml/adapters/dataset.py
@@ -7,13 +7,13 @@ from typing import ClassVar, Optional, Type
 
 from linkml_runtime.linkml_model.meta import ArrayExpression, SlotDefinition
 
-from nwb_linkml.adapters.adapter import BuildResult
+from nwb_linkml.adapters.adapter import BuildResult, is_1d, is_compound, has_attrs
 from nwb_linkml.adapters.array import ArrayAdapter
 from nwb_linkml.adapters.classes import ClassAdapter
 from nwb_linkml.maps import QUANTITY_MAP, Map
-from nwb_linkml.maps.dtype import flat_to_linkml
+from nwb_linkml.maps.dtype import flat_to_linkml, handle_dtype
 from nwb_linkml.maps.naming import camel_to_snake
-from nwb_schema_language import CompoundDtype, Dataset
+from nwb_schema_language import Dataset
 
 
 class DatasetMap(Map):
@@ -106,7 +106,7 @@ class MapScalar(DatasetMap):
         this_slot = SlotDefinition(
             name=cls.name,
             description=cls.doc,
-            range=ClassAdapter.handle_dtype(cls.dtype),
+            range=handle_dtype(cls.dtype),
             **QUANTITY_MAP[cls.quantity],
         )
         res = BuildResult(slots=[this_slot])
@@ -203,9 +203,7 @@ class MapScalarAttributes(DatasetMap):
         """
         Map to a scalar attribute with an adjoining "value" slot
         """
-        value_slot = SlotDefinition(
-            name="value", range=ClassAdapter.handle_dtype(cls.dtype), required=True
-        )
+        value_slot = SlotDefinition(name="value", range=handle_dtype(cls.dtype), required=True)
         res.classes[0].attributes["value"] = value_slot
         return res
 
@@ -271,7 +269,7 @@ class MapListlike(DatasetMap):
             * - ``dtype``
               - ``Class``
         """
-        dtype = ClassAdapter.handle_dtype(cls.dtype)
+        dtype = handle_dtype(cls.dtype)
         return (
             cls.neurodata_type_inc != "VectorData"
             and is_1d(cls)
@@ -289,7 +287,7 @@ class MapListlike(DatasetMap):
         slot = SlotDefinition(
             name="value",
             multivalued=True,
-            range=ClassAdapter.handle_dtype(cls.dtype),
+            range=handle_dtype(cls.dtype),
             description=cls.doc,
             required=cls.quantity not in ("*", "?"),
             annotations=[{"source_type": "reference"}],
@@ -378,7 +376,7 @@ class MapArraylike(DatasetMap):
               - ``False``
 
         """
-        dtype = ClassAdapter.handle_dtype(cls.dtype)
+        dtype = handle_dtype(cls.dtype)
         return (
             cls.name
             and (all([cls.dims, cls.shape]) or cls.neurodata_type_inc == "VectorData")
@@ -409,7 +407,7 @@ class MapArraylike(DatasetMap):
                 SlotDefinition(
                     name=name,
                     multivalued=False,
-                    range=ClassAdapter.handle_dtype(cls.dtype),
+                    range=handle_dtype(cls.dtype),
                     description=cls.doc,
                     required=cls.quantity not in ("*", "?"),
                     **expressions,
@@ -513,7 +511,7 @@ class MapArrayLikeAttributes(DatasetMap):
         """
         Check that we're an array with some additional metadata
         """
-        dtype = ClassAdapter.handle_dtype(cls.dtype)
+        dtype = handle_dtype(cls.dtype)
         return (
             all([cls.dims, cls.shape])
             and cls.neurodata_type_inc != "VectorData"
@@ -532,9 +530,7 @@ class MapArrayLikeAttributes(DatasetMap):
         array_adapter = ArrayAdapter(cls.dims, cls.shape)
         expressions = array_adapter.make_slot()
         # make a slot for the arraylike class
-        array_slot = SlotDefinition(
-            name="value", range=ClassAdapter.handle_dtype(cls.dtype), **expressions
-        )
+        array_slot = SlotDefinition(name="value", range=handle_dtype(cls.dtype), **expressions)
         res.classes[0].attributes.update({"value": array_slot})
         return res
 
@@ -596,7 +592,7 @@ class MapVectorClassRange(DatasetMap):
         Check that we are a VectorData object without any additional attributes
         with a dtype that refers to another class
         """
-        dtype = ClassAdapter.handle_dtype(cls.dtype)
+        dtype = handle_dtype(cls.dtype)
         return (
             cls.neurodata_type_inc == "VectorData"
             and cls.name
@@ -617,7 +613,7 @@ class MapVectorClassRange(DatasetMap):
             name=cls.name,
             description=cls.doc,
             multivalued=True,
-            range=ClassAdapter.handle_dtype(cls.dtype),
+            range=handle_dtype(cls.dtype),
             required=cls.quantity not in ("*", "?"),
         )
         res = BuildResult(slots=[this_slot])
@@ -672,7 +668,7 @@ class MapVectorClassRange(DatasetMap):
 #         this_slot = SlotDefinition(
 #             name=cls.name,
 #             description=cls.doc,
-#             range=ClassAdapter.handle_dtype(cls.dtype),
+#             range=handle_dtype(cls.dtype),
 #             multivalued=True,
 #         )
 #         # No need to make a class for us, so we replace the existing build results
@@ -783,7 +779,7 @@ class MapCompoundDtype(DatasetMap):
             slots[a_dtype.name] = SlotDefinition(
                 name=a_dtype.name,
                 description=a_dtype.doc,
-                range=ClassAdapter.handle_dtype(a_dtype.dtype),
+                range=handle_dtype(a_dtype.dtype),
                 **QUANTITY_MAP[cls.quantity],
             )
         res.classes[0].attributes.update(slots)
@@ -836,36 +832,3 @@ class DatasetAdapter(ClassAdapter):
             return None
         else:
             return matches[0]
-
-
-def is_1d(cls: Dataset) -> bool:
-    """
-    Check if the values of a dataset are 1-dimensional.
-
-    Specifically:
-    * a single-layer dim/shape list of length 1, or
-    * a nested dim/shape list where every nested spec is of length 1
-    """
-    return (
-        not any([isinstance(dim, list) for dim in cls.dims]) and len(cls.dims) == 1
-    ) or (  # nested list
-        all([isinstance(dim, list) for dim in cls.dims])
-        and len(cls.dims) == 1
-        and len(cls.dims[0]) == 1
-    )
-
-
-def is_compound(cls: Dataset) -> bool:
-    """Check if dataset has a compound dtype"""
-    return (
-        isinstance(cls.dtype, list)
-        and len(cls.dtype) > 0
-        and isinstance(cls.dtype[0], CompoundDtype)
-    )
-
-
-def has_attrs(cls: Dataset) -> bool:
-    """
-    Check if a dataset has any attributes at all without defaults
-    """
-    return len(cls.attributes) > 0 and all([not a.value for a in cls.attributes])
diff --git a/nwb_linkml/src/nwb_linkml/maps/dtype.py b/nwb_linkml/src/nwb_linkml/maps/dtype.py
index 9a7756f..0cc7c79 100644
--- a/nwb_linkml/src/nwb_linkml/maps/dtype.py
+++ b/nwb_linkml/src/nwb_linkml/maps/dtype.py
@@ -7,6 +7,7 @@ from typing import Any, Type
 
 import nptyping
 import numpy as np
+from nwb_schema_language import CompoundDtype, DTypeType, FlatDtype, ReferenceDtype
 
 flat_to_linkml = {
     "float": "float",
@@ -185,3 +186,34 @@ def struct_from_dtype(dtype: np.dtype) -> Type[nptyping.Structure]:
     struct_pieces = [f"{k}: {flat_to_nptyping[v[0].name]}" for k, v in dtype.fields.items()]
     struct_dtype = ", ".join(struct_pieces)
     return nptyping.Structure[struct_dtype]
+
+
+def handle_dtype(dtype: DTypeType | None) -> str:
+    """
+    Get the string form of a dtype
+
+    Args:
+        dtype (:class:`.DTypeType`): Dtype to stringify
+
+    Returns:
+        str
+    """
+    if isinstance(dtype, ReferenceDtype):
+        return dtype.target_type
+    elif dtype is None or dtype == []:
+        # Some ill-defined datasets are "abstract" despite that not being in the schema language
+        return "AnyType"
+    elif isinstance(dtype, FlatDtype):
+        return dtype.value
+    elif isinstance(dtype, list) and isinstance(dtype[0], CompoundDtype):
+        # there is precisely one class that uses compound dtypes:
+        # TimeSeriesReferenceVectorData
+        # compoundDtypes are able to define a ragged table according to the schema
+        # but are used in this single case equivalently to attributes.
+        # so we'll... uh... treat them as slots.
+        # TODO
+        return "AnyType"
+
+    else:
+        # flat dtype
+        return dtype
diff --git a/scripts/generate_core.py b/scripts/generate_core.py
index 826bf30..6cbb83f 100644
--- a/scripts/generate_core.py
+++ b/scripts/generate_core.py
@@ -1,6 +1,8 @@
 import shutil
 import os
+import sys
 import traceback
+from pdb import post_mortem
 
 from argparse import ArgumentParser
 from pathlib import Path
@@ -53,6 +55,7 @@ def generate_versions(
     dry_run: bool = False,
     repo: GitRepo = NWB_CORE_REPO,
     hdmf_only=False,
+    pdb=False,
 ):
     """
     Generate linkml models for all versions
@@ -128,6 +131,11 @@ def generate_versions(
                     build_progress.update(pydantic_task, action="Built Pydantic")
 
                 except Exception as e:
+                    if pdb:
+                        live.stop()
+                        post_mortem()
+                        sys.exit(1)
+
                     build_progress.stop_task(linkml_task)
                     if linkml_task is not None:
                         build_progress.update(linkml_task, action="[bold red]LinkML Build Failed")
@@ -205,6 +213,7 @@ def parser() -> ArgumentParser:
         ),
         action="store_true",
     )
+    parser.add_argument("--pdb", help="Launch debugger on an error", action="store_true")
     return parser
 
 
@@ -222,7 +231,7 @@ def main():
         generate_core_yaml(args.yaml, args.dry_run, args.hdmf)
         generate_core_pydantic(args.yaml, args.pydantic, args.dry_run)
     else:
-        generate_versions(args.yaml, args.pydantic, args.dry_run, repo, args.hdmf)
+        generate_versions(args.yaml, args.pydantic, args.dry_run, repo, args.hdmf, pdb=args.pdb)
 
 
 if __name__ == "__main__":