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diff --git a/src/mlia/cli/helpers.py b/src/mlia/cli/helpers.py
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+# SPDX-FileCopyrightText: Copyright 2022, Arm Limited and/or its affiliates.
+# SPDX-License-Identifier: Apache-2.0
+"""Module for various helper classes."""
+from typing import Any
+from typing import Dict
+from typing import List
+from typing import Optional
+from typing import Tuple
+
+from mlia.cli.options import get_target_profile_opts
+from mlia.core.helpers import ActionResolver
+from mlia.nn.tensorflow.optimizations.select import OptimizationSettings
+from mlia.nn.tensorflow.utils import is_keras_model
+from mlia.utils.types import is_list_of
+
+
+class CLIActionResolver(ActionResolver):
+    """Helper class for generating cli commands."""
+
+    def __init__(self, args: Dict[str, Any]) -> None:
+        """Init action resolver."""
+        self.args = args
+
+    @staticmethod
+    def _general_optimization_command(model_path: Optional[str]) -> List[str]:
+        """Return general optimization command description."""
+        keras_note = []
+        if model_path is None or not is_keras_model(model_path):
+            model_path = "/path/to/keras_model"
+            keras_note = ["Note: you will need a Keras model for that."]
+
+        return [
+            *keras_note,
+            "For example: mlia optimization --optimization-type "
+            f"pruning,clustering --optimization-target 0.5,32 {model_path}",
+            "For more info: mlia optimization --help",
+        ]
+
+    @staticmethod
+    def _specific_optimization_command(
+        model_path: str,
+        device_opts: str,
+        opt_settings: List[OptimizationSettings],
+    ) -> List[str]:
+        """Return specific optimization command description."""
+        opt_types = ",".join(opt.optimization_type for opt in opt_settings)
+        opt_targs = ",".join(str(opt.optimization_target) for opt in opt_settings)
+
+        return [
+            "For more info: mlia optimization --help",
+            "Optimization command: "
+            f"mlia optimization --optimization-type {opt_types} "
+            f"--optimization-target {opt_targs}{device_opts} {model_path}",
+        ]
+
+    def apply_optimizations(self, **kwargs: Any) -> List[str]:
+        """Return command details for applying optimizations."""
+        model_path, device_opts = self._get_model_and_device_opts()
+
+        if (opt_settings := kwargs.pop("opt_settings", None)) is None:
+            return self._general_optimization_command(model_path)
+
+        if is_list_of(opt_settings, OptimizationSettings) and model_path:
+            return self._specific_optimization_command(
+                model_path, device_opts, opt_settings
+            )
+
+        return []
+
+    def supported_operators_info(self) -> List[str]:
+        """Return command details for generating supported ops report."""
+        return [
+            "For guidance on supported operators, run: mlia operators "
+            "--supported-ops-report",
+        ]
+
+    def check_performance(self) -> List[str]:
+        """Return command details for checking performance."""
+        model_path, device_opts = self._get_model_and_device_opts()
+        if not model_path:
+            return []
+
+        return [
+            "Check the estimated performance by running the following command: ",
+            f"mlia performance{device_opts} {model_path}",
+        ]
+
+    def check_operator_compatibility(self) -> List[str]:
+        """Return command details for op compatibility."""
+        model_path, device_opts = self._get_model_and_device_opts()
+        if not model_path:
+            return []
+
+        return [
+            "Try running the following command to verify that:",
+            f"mlia operators{device_opts} {model_path}",
+        ]
+
+    def operator_compatibility_details(self) -> List[str]:
+        """Return command details for op compatibility."""
+        return ["For more details, run: mlia operators --help"]
+
+    def optimization_details(self) -> List[str]:
+        """Return command details for optimization."""
+        return ["For more info, see: mlia optimization --help"]
+
+    def _get_model_and_device_opts(
+        self, separate_device_opts: bool = True
+    ) -> Tuple[Optional[str], str]:
+        """Get model and device options."""
+        device_opts = " ".join(get_target_profile_opts(self.args))
+        if separate_device_opts and device_opts:
+            device_opts = f" {device_opts}"
+
+        model_path = self.args.get("model")
+        return model_path, device_opts