diff --git a/src/workflows/run_benchmark/config.vsh.yaml b/src/workflows/run_benchmark/config.vsh.yaml
index 9d1d3aa..f49776e 100644
--- a/src/workflows/run_benchmark/config.vsh.yaml
+++ b/src/workflows/run_benchmark/config.vsh.yaml
@@ -73,6 +73,8 @@ resources:
   - type: file
     path: /_viash.yaml
   - path: /common/nextflow_helpers/helper.nf
+  - path: /common/nextflow_helpers/benchmarkHelper.nf
+  - path: /common/nextflow_helpers/workflowHelper.nf
 
 dependencies:
   - name: utils/extract_uns_metadata
diff --git a/src/workflows/run_benchmark/main.nf b/src/workflows/run_benchmark/main.nf
index e7fe17f..e58581c 100644
--- a/src/workflows/run_benchmark/main.nf
+++ b/src/workflows/run_benchmark/main.nf
@@ -1,11 +1,5 @@
 include { checkItemAllowed } from "${meta.resources_dir}/helper.nf"
-
-workflow auto {
-  findStates(params, meta.config)
-    | meta.workflow.run(
-      auto: [publish: "state"]
-    )
-}
+include { run_methods; run_metrics; extract_scores; create_metadata_files } from "${meta.resources_dir}/BenchmarkHelper.nf"
 
 methods = [
   majority_vote,
@@ -47,36 +41,24 @@ workflow run_wf {
 
   main:
 
-  /****************************
-   * EXTRACT DATASET METADATA *
-   ****************************/
-  dataset_ch = input_ch
-    // store join id
-    | map{ id, state ->
-      [id, state + ["_meta": [join_id: id]]]
-    }
+  /* RUN METHODS AND METRICS */
+  score_ch = input_ch
 
-    // extract the dataset metadata
+    // extract the uns metadata from the dataset
     | extract_uns_metadata.run(
       fromState: [input: "input_solution"],
       toState: { id, output, state ->
-        state + [
-          dataset_uns: readYaml(output.output).uns
-        ]
+        def outputYaml = readYaml(output.output)
+        if (!outputYaml.uns) {
+          throw new Exception("id '$id': No uns found in provided dataset")
+        }
+        state + [ dataset_uns: outputYaml.uns ]
       }
     )
 
-  /***************************
-   * RUN METHODS AND METRICS *
-   ***************************/
-  score_ch = dataset_ch
-
-    // run all methods
-    | runEach(
-      components: methods,
-
-      // use the 'filter' argument to only run a method on the normalisation the component is asking for
-      filter: { id, state, comp ->
+    | run_methods(
+      methods: methods,
+      filter: {id, state, comp ->
         def norm = state.dataset_uns.normalization_id
         def pref = comp.config.info.preferred_normalization
         // if the preferred normalisation is none at all,
@@ -91,14 +73,7 @@ workflow run_wf {
         )
         method_check && norm_check
       },
-
-      // define a new 'id' by appending the method name to the dataset id
-      id: { id, state, comp ->
-        id + "." + comp.config.name
-      },
-
-      // use 'fromState' to fetch the arguments the component requires from the overall state
-      fromState: { id, state, comp ->
+      fromState: {id, state, comp ->
         def new_args = [
           input_train: state.input_train,
           input_test: state.input_test
@@ -108,9 +83,7 @@ workflow run_wf {
         }
         new_args
       },
-
-      // use 'toState' to publish that component's outputs to the overall state
-      toState: { id, output, state, comp ->
+      toState: {id, output, state, comp ->
         state + [
           method_id: comp.config.name,
           method_output: output.output
@@ -118,99 +91,49 @@ workflow run_wf {
       }
     )
 
-    // run all metrics
-    | runEach(
-      components: metrics,
-      id: { id, state, comp ->
-        id + "." + comp.config.name
-      },
-      // use 'fromState' to fetch the arguments the component requires from the overall state
+    | run_metrics(
+      metrics: metrics,
       fromState: [
         input_solution: "input_solution",
         input_prediction: "method_output"
       ],
-      // use 'toState' to publish that component's outputs to the overall state
       toState: { id, output, state, comp ->
         state + [
           metric_id: comp.config.name,
           metric_output: output.output
         ]
-      }
+      }  
     )
 
-
-  /******************************
-   * GENERATE OUTPUT YAML FILES *
-   ******************************/
-  // TODO: can we store everything below in a separate helper function?
-
-  // extract the dataset metadata
-  dataset_meta_ch = dataset_ch
-    // only keep one of the normalization methods
-    | filter{ id, state ->
-      state.dataset_uns.normalization_id == "log_cp10k"
-    }
-    | joinStates { ids, states ->
-      // store the dataset metadata in a file
-      def dataset_uns = states.collect{state ->
-        def uns = state.dataset_uns.clone()
-        uns.remove("normalization_id")
-        uns
-      }
-      def dataset_uns_yaml_blob = toYamlBlob(dataset_uns)
-      def dataset_uns_file = tempFile("dataset_uns.yaml")
-      dataset_uns_file.write(dataset_uns_yaml_blob)
-
-      ["output", [output_dataset_info: dataset_uns_file]]
-    }
-
-  output_ch = score_ch
-
-    // extract the scores
-    | extract_uns_metadata.run(
-      key: "extract_scores",
-      fromState: [input: "metric_output"],
-      toState: { id, output, state ->
-        state + [
-          score_uns: readYaml(output.output).uns
-        ]
-      }
+    | extract_scores(
+      extract_uns_metadata_component: extract_uns_metadata
     )
 
-    | joinStates { ids, states ->
-      // store the method configs in a file
-      def method_configs = methods.collect{it.config}
-      def method_configs_yaml_blob = toYamlBlob(method_configs)
-      def method_configs_file = tempFile("method_configs.yaml")
-      method_configs_file.write(method_configs_yaml_blob)
-
-      // store the metric configs in a file
-      def metric_configs = metrics.collect{it.config}
-      def metric_configs_yaml_blob = toYamlBlob(metric_configs)
-      def metric_configs_file = tempFile("metric_configs.yaml")
-      metric_configs_file.write(metric_configs_yaml_blob)
-
-      def task_info_file = meta.resources_dir.resolve("_viash.yaml")
+  /* GENERATE METADATA FILES */
+  metadata_ch = input_ch
 
-      // store the scores in a file
-      def score_uns = states.collect{it.score_uns}
-      def score_uns_yaml_blob = toYamlBlob(score_uns)
-      def score_uns_file = tempFile("score_uns.yaml")
-      score_uns_file.write(score_uns_yaml_blob)
-
-      def new_state = [
-        output_method_configs: method_configs_file,
-        output_metric_configs: metric_configs_file,
-        output_task_info: task_info_file,
-        output_scores: score_uns_file,
-        _meta: states[0]._meta
-      ]
+    | create_metadata_files(
+      datasetFile: "input_solution",
+      // only keep one of the normalization methods
+      // for generating the dataset metadata files
+      filter: {id, state ->
+        state.dataset_uns.normalization_id == "log_cp10k"
+      },
+      datasetUnsModifier: { uns ->
+        def uns_ = uns.clone()
+        uns_.remove("normalization_id")
+        uns_
+      },
+      methods: methods,
+      metrics: metrics,
+      meta: meta,
+      extract_uns_metadata_component: extract_uns_metadata
+    )
 
-      ["output", new_state]
-    }
 
-    // merge all of the output data
-    | mix(dataset_meta_ch)
+  /* JOIN SCORES AND METADATA */
+  output_ch = score_ch
+    | mix(metadata_ch)
     | joinStates{ ids, states ->
       def mergedStates = states.inject([:]) { acc, m -> acc + m }
       [ids[0], mergedStates]
@@ -219,3 +142,12 @@ workflow run_wf {
   emit:
   output_ch
 }
+
+// Helper workflow to look for 'state.yaml' files recursively and
+// use it to run the benchmark.
+workflow auto {
+  findStates(params, meta.config)
+    | meta.workflow.run(
+      auto: [publish: "state"]
+    )
+}
\ No newline at end of file