diff --git a/nextflow/bootstrap/classifier_source.config b/nextflow/bootstrap/classifier_source.config
new file mode 100644
index 0000000..d064d36
--- /dev/null
+++ b/nextflow/bootstrap/classifier_source.config
@@ -0,0 +1,103 @@
+// Example configuration for the classifier bootstrap pipeline.
+// Update the paths and classifiers to match your environment before running.
+
+params {
+    // Root directory where classifier artifacts will be written.
+    classifier_base_path = "/projects/kumar-lab/data/jabs/classifiers"
+
+    // Directory containing source JABS project folders referenced below.
+    classifier_project_folders = "/projects/kumar-lab/data/jabs/projects"
+    
+    // Version label for this bootstrap build.
+    jabs_version = "v0.37.0"
+
+    // Map of behaviors to their source projects and metadata.
+    single_mouse_classifiers = [
+        // "grooming": [
+        //     "project_folder_name": "grooming",
+        //     "stitch_value": 10*30,
+        //     "filter_value": 3*30,
+        // ],
+        "scratch": [
+            "project_folder_name": "jabs-paper",
+            "stitch_value": 5,
+            "filter_value": 5,
+        ],
+        // "Leg_splaying": [  // Currently can't be exported successfully
+        //     "project_folder_name": "ptz",
+        //     "stitch_value": 5,
+        //     "filter_value": 5,
+        // ],
+        "Side seizure": [
+            "project_folder_name": "ptz",
+            "stitch_value": 5,
+            "filter_value": 5,
+        ],
+        "Tail jerk": [
+            "project_folder_name": "ptz",
+            "stitch_value": 5,
+            "filter_value": 5,
+        ],
+        "Wild jumping": [
+            "project_folder_name": "ptz",
+            "stitch_value": 5,
+            "filter_value": 5,
+        ],
+        "Escape": [
+            "project_folder_name": "jabs-vivek",
+            "stitch_value": 5,
+            "filter_value": 5,
+        ],
+        "Rearing (supported)": [
+            "project_folder_name": "jabs-vivek",
+            "stitch_value": 5,
+            "filter_value": 5,
+        ],
+        "Rearing (unsupported)": [
+            "project_folder_name": "jabs-vivek",
+            "stitch_value": 5,
+            "filter_value": 5,
+        ],
+        "Turn left": [
+            "project_folder_name": "jabs-vivek",
+            "stitch_value": 5,
+            "filter_value": 5,
+        ],
+        "Turn right": [
+            "project_folder_name": "jabs-vivek",
+            "stitch_value": 5,
+            "filter_value": 5,
+        ],
+    ]
+}
+
+apptainer {
+   enabled = true
+   autoMounts = true
+}
+
+process {
+    withLabel: "jabs_classify" {
+        container = "/projects/kumar-lab/meta/images/JABS-behavior-classifier/headless/v0.37.0/latest.sif"
+    }
+    withLabel: "cpu" {
+        queue = "compute"
+        cpus = 1
+        memory = 24.GB
+        time = 24.h
+    }
+    withLabel: "highcpu" {
+        queue = "compute"
+        cpus = 16
+        memory = 64.GB
+        time = 24.h
+    }
+}
+
+executor {
+    name = 'slurm'
+    // The number of tasks the executor will handle in a parallel manner
+    queueSize = 24
+    submitRateLimit = '1 s'
+    // Determines the max rate of job submission per time unit, for example '10sec' eg. max 10 jobs per second or '1/2 s' i.e. 1 job submissions every 2 seconds.
+}
diff --git a/nextflow/bootstrap/main.nf b/nextflow/bootstrap/main.nf
new file mode 100644
index 0000000..308e11d
--- /dev/null
+++ b/nextflow/bootstrap/main.nf
@@ -0,0 +1,303 @@
+nextflow.enable.dsl=2
+
+/*
+ * This bootstrap workflow generates JABS classifiers and their associated metadata.
+ * It produces versioned, content-hashed artifacts and a Nextflow configuration
+ * file that can be used by the main analysis pipeline.
+ */
+
+/**
+ * Creates a version directory and a JSON config file with metadata about the build.
+ *
+ * @return jabs.config.json A JSON configuration file containing the JABS version and creation timestamp
+ */
+process CREATE_VERSION_CONFIG {
+    tag "config_${params.jabs_version}"
+    label 'cpu'
+    publishDir "${params.classifier_base_path}/${params.jabs_version}", mode: 'copy', overwrite: true
+
+    output:
+    path "jabs.config.json"
+
+    script:
+    """
+    printf '{
+' > "jabs.config.json"
+    printf '  "jabs_version": "${params.jabs_version}",
+' >> "jabs.config.json"
+    printf '  "creation_timestamp_utc": "%s"
+' "\$(date -u +%Y-%m-%dT%H:%M:%SZ)" >> "jabs.config.json"
+    printf '}
+' >> "jabs.config.json"
+    """
+}
+
+/**
+ * Calculates the unique window sizes required for a JABS project by parsing the project.json file.
+ * Window sizes are extracted from behavior definitions and formatted as command-line arguments.
+ *
+ * @param project_folder_name The name of the JABS project folder to analyze
+ *
+ * @return project_with_windows A tuple containing the project folder name and formatted window size arguments (e.g., "-w 5 -w 10")
+ */
+process CALCULTE_PROJECT_WINDOW_SIZES {
+    label 'cpu'
+
+   input:
+    val(project_folder_name)
+
+    output:
+    tuple val(project_folder_name), env('WINDOW_SIZES'), emit: project_with_windows
+
+    script:
+    def project_path = "${params.classifier_project_folders}/${project_folder_name}"
+    def project_file = "${project_path}/jabs/project.json"
+    """
+    export WINDOW_SIZES=\$(jq -r '[.behavior[] | .window_size] | unique | map("-w \\(.)") | join(" ")' ${project_file})
+    """
+}
+
+/**
+ * Initializes a JABS project by running the jabs-init command with the specified window sizes.
+ * This process prepares the project for training data export and classifier generation.
+ *
+ * @param project_folder_name The name of the JABS project folder to initialize
+ * @param window_sizes Formatted window size arguments (e.g., "-w 5 -w 10") to use for initialization
+ *
+ * @return initialized_project The project folder name after successful initialization
+ */
+process INIT_JABS_PROJECTS {
+    label 'jabs_classify'
+    label 'highcpu'
+
+    input:
+    tuple val(project_folder_name), val(window_sizes)
+
+    output:
+    val(project_folder_name), emit: initialized_project
+
+    script:
+    def project_path = "${params.classifier_project_folders}/${project_folder_name}"
+    """
+    jabs-init "${project_path}" ${window_sizes} -p 16
+    """
+}
+
+/**
+ * Exports training data for a specific behavior from a JABS project to an HDF5 file.
+ * Creates content-addressed files using SHA256 hashing and generates metadata manifests.
+ *
+ * @param behavior_name The name of the behavior to export training data for
+ * @param behavior_path The filesystem-safe path derived from the behavior name
+ * @param project_folder_name The name of the JABS project folder containing the training data
+ *
+ * @return h5_file_with_hash A tuple containing behavior info, the content-addressed HDF5 file, and its SHA256 hash
+ * @return *.h5.manifest.json A JSON manifest file containing metadata about the training data export
+ */
+process EXPORT_TRAINING_DATA {
+    tag "export_${behavior_path}"
+    label 'jabs_classify'
+    label 'cpu'
+
+    publishDir path: "${params.classifier_base_path}/${params.jabs_version}/${behavior_path}",
+               mode: 'copy'
+
+    input:
+    tuple val(behavior_name), val(behavior_path), val(project_folder_name)
+
+    output:
+    tuple val(behavior_name), val(behavior_path), val(project_folder_name), path("*.h5"), env('HASH'), emit: h5_file_with_hash
+    path "*.h5.manifest.json"
+
+    script:
+    def project_path = "${params.classifier_project_folders}/${project_folder_name}"
+    """
+    # 1. Export to a local file
+    jabs-cli export-training --behavior "${behavior_name}" --outfile "training.h5"  "${project_path}"
+
+    # 2. Calculate hash and export it
+    export HASH=\$(sha256sum "training.h5" | awk '{ print \$1 }')
+
+    # 3. Create the content-addressed files
+    mv training.h5 "\${HASH}.h5"
+
+    # 4. Create the manifest file
+    cat > "\${HASH}.h5.manifest.json" <<EOF
+{
+  "behavior": "${behavior_name}",
+  "jabs_version": "${params.jabs_version}",
+  "file_hash_sha256": "\${HASH}",
+  "timestamp_utc": "\$(date -u +%Y-%m-%dT%H:%M:%SZ)",
+  "source_jabs_project": "${project_folder_name}"
+}
+EOF
+    """
+}
+
+/**
+ * Trains a JABS classifier from the exported training data HDF5 file.
+ * Creates content-addressed classifier files using SHA256 hashing and generates metadata manifests
+ * that link back to the source training data.
+ *
+ * @param behavior_name The name of the behavior being classified
+ * @param behavior_path The filesystem-safe path derived from the behavior name
+ * @param project_folder_name The name of the source JABS project folder
+ * @param training_h5 The HDF5 file containing training data
+ * @param training_hash The SHA256 hash of the training data file
+ *
+ * @return classifier_file A tuple containing behavior info, the content-addressed classifier pickle file, and its SHA256 hash
+ * @return *.pickle.manifest.json A JSON manifest file containing metadata about the classifier and its training provenance
+ */
+process TRAIN_CLASSIFIER {
+    tag "train_${behavior_path}"
+    label 'jabs_classify'
+    label 'cpu'
+
+    publishDir path: "${params.classifier_base_path}/${params.jabs_version}/${behavior_path}",
+               mode: 'copy'
+
+    input:
+    tuple val(behavior_name), val(behavior_path), val(project_folder_name), path(training_h5), val(training_hash)
+
+    output:
+    tuple val(behavior_name), val(behavior_path), path("*.pickle"), env('HASH'), emit: classifier_file
+    path "*.pickle.manifest.json"
+
+    script:
+    """
+    # 1. Train the classifier
+    jabs-classify train "${training_h5}" "classifier.pickle"
+
+    # 2. Calculate hash of the new classifier
+    export HASH=\$(sha256sum "classifier.pickle" | awk '{ print \$1 }')
+
+    # 3. Create content-addressed files
+    mv classifier.pickle "\${HASH}.pickle"
+
+    # 4. Create the classifier manifest file
+    cat > "\${HASH}.pickle.manifest.json" <<EOF
+{
+  "behavior": "${behavior_name}",
+  "jabs_version": "${params.jabs_version}",
+  "file_hash_sha256": "\${HASH}",
+  "timestamp_utc": "\$(date -u +%Y-%m-%dT%H:%M:%SZ)",
+  "source_training_data_hash": "${training_hash}",
+  "training_file": "${training_h5}",
+  "training_manifest": "${training_h5}.manifest.json",
+}
+EOF
+    """
+}
+
+/**
+ * Generates a Nextflow configuration file that maps behaviors to their
+ * generated classifier artifacts for the main analysis pipeline.
+ * The generated config includes classifier paths, stitch values, and filter values
+ * for each behavior, creating a complete single_mouse_classifiers parameter structure.
+ *
+ * @param collected_behaviors A list of tuples containing [behavior_name, behavior_path, classifier_hash] for all successfully trained classifiers
+ *
+ * @return generated_classifiers.config A Nextflow configuration file that can be included in the main pipeline to reference the generated classifiers
+ */
+process GENERATE_PIPELINE_CONFIG {
+    tag "generate_config"
+    label 'cpu'
+    publishDir "${params.classifier_base_path}/${params.jabs_version}", mode: 'copy', overwrite: true
+
+    input:
+    val collected_behaviors // list of [behavior_name, behavior_path, classifier_hash]
+
+    output:
+    path "generated_classifiers.config"
+
+    script:
+    def behaviors_json  = groovy.json.JsonOutput.toJson(collected_behaviors)
+    def classifiers_json = groovy.json.JsonOutput.toJson(params.single_mouse_classifiers)
+    """
+#!/usr/bin/env python3
+import json
+from pathlib import Path
+
+behaviors = json.loads('''${behaviors_json}''')
+
+classifiers = json.loads('''${classifiers_json}''')
+
+with open("generated_classifiers.config", "w") as f:
+    f.write("params {\\n")
+    f.write("    single_mouse_classifiers = [\\n")
+    
+    # Iterate through behaviors in triples (behavior_name, behavior_path, classifier_file)
+    for row in behaviors:
+        b_name, b_path, c_hash = row
+        details = classifiers[b_name]
+    
+        classifier_path = Path("${params.classifier_base_path}/${params.jabs_version}/") / b_path / Path(c_hash + ".pickle")
+        
+        f.write(f"        '{b_name}': [\\n")
+        f.write(f"            classifier_path: '{classifier_path}',\\n")
+        f.write(f"            stitch_value: {details['stitch_value']},\\n")
+        f.write(f"            filter_value: {details['filter_value']}\\n")
+        f.write(f"        ],\\n")
+    
+    f.write("    ]\\n")
+    f.write("}\\n")
+"""
+}
+
+
+/**
+ * Main workflow for generating classifiers and the pipeline config.
+ */
+workflow {
+    main:
+    CREATE_VERSION_CONFIG()
+
+    // Create channel of behaviors with their details
+    classifier_ch = Channel.from(params.single_mouse_classifiers.collect { k, v -> [k, v] })
+    
+    // Map to include behavior_path
+    behavior_projects_ch = classifier_ch.map { behavior_name, details ->
+        def behavior_path = behavior_name.replaceAll(' ', '_').replaceAll('[()]', '')
+        tuple(behavior_name, behavior_path, details.project_folder_name)
+    }
+
+    // Extract unique project folders
+    unique_projects_ch = classifier_ch
+        .map { behavior_name, details -> details.project_folder_name }
+        .unique()
+
+    // Calcualte project windows outside of container so we have access to jq
+    project_with_windows = CALCULTE_PROJECT_WINDOW_SIZES(unique_projects_ch)
+
+    // Initialize each unique project
+    initialized_projects_ch = INIT_JABS_PROJECTS(project_with_windows)
+
+    // Create a value channel from initialized projects for joining
+    initialized_projects_val = initialized_projects_ch.initialized_project
+        .collect()
+        .map { projects -> 
+            projects.collectEntries { [it, true] }
+        }
+
+    // Join behaviors with their initialized projects
+    ready_behaviors_ch = behavior_projects_ch
+        .combine(initialized_projects_val)
+        .map { behavior_name, behavior_path, project_folder_name, project_map ->
+            // Check if this behavior's project has been initialized
+            if (project_map[project_folder_name]) {
+                tuple(behavior_name, behavior_path, project_folder_name)
+            }
+        }
+
+    exported_h5_ch = EXPORT_TRAINING_DATA(ready_behaviors_ch)
+
+    trained_classifiers_ch = TRAIN_CLASSIFIER(exported_h5_ch.h5_file_with_hash)
+
+    // Collect only the behavior names that were successfully trained
+    trained_classifiers_ch.classifier_file
+        .map { behavior_name, behavior_path, _classifier_file, hash -> tuple(behavior_name, behavior_path, hash) }
+        .toList()
+        .set{ collected_behaviors }
+
+    GENERATE_PIPELINE_CONFIG(collected_behaviors)
+}
diff --git a/nextflow/configs/profiles/sumner2.config b/nextflow/configs/profiles/sumner2.config
index da11354..bc2e10f 100644
--- a/nextflow/configs/profiles/sumner2.config
+++ b/nextflow/configs/profiles/sumner2.config
@@ -14,10 +14,6 @@ params {
      * - filter_processed: whether to filter processed files in the batch
 
      * - jabs_version: version of JABS used for the classifiers
-     * - classifier_project_folders: directory containing the classifier project folders
-     * - classifier_training_file_folder: directory containing the classifier training files
-     * - exported_classifier_folder: directory containing the exported classifiers
-     * - classifier_artifact_suffix: suffix for the classifier artifacts
      * - classifier_window_sizes: window sizes cached for use in classifiers
      * - single_mouse_classifiers: classifiers for single mouse behavior. Each classifier is described as
          behavior name (identical key used in JABS): [
@@ -37,75 +33,64 @@ params {
     heuristic_classifier_folder = "/JABS-postprocess/heuristic_classifiers/"
     filter_processed = false
 
-    jabs_version = "0.18.1"
-    classifier_project_folders = "/projects/kumar-lab/multimouse-pipeline/nextflow-artifacts/project_folders/"
-    classifier_training_file_folder = "/projects/kumar-lab/multimouse-pipeline/nextflow-artifacts/training_files/"
-    exported_classifier_folder = "/projects/kumar-lab/multimouse-pipeline/nextflow-artifacts/exported_classifiers/"
-    classifier_artifact_suffix = "_classifier_v${jabs_version}.pickle"
+    jabs_version = "v0.37.0"
     classifier_window_sizes = [2, 5, 10, 20, 30, 60]
     // Classifiers are described as behavior_name: project_folder
+        jabs_version = "v0.37.0"
+    classifier_window_sizes = [2, 3, 5, 10, 16, 20, 30, 60]
     single_mouse_classifiers = [
-        "grooming": [
-            "project_folder_name": "grooming",
-            "stitch_value": 10*30,
-            "filter_value": 3*30,
+        'grooming': [
+            classifier_path: '/projects/kumar-lab/data/jabs/classifiers/v0.37.0/grooming/12199145d31213f79e23669d211cfa2fd33cedb9f11197b44f90a4b71297b6d2.pickle',
+            stitch_value: 300,
+            filter_value: 90
         ],
-        "scratch": [
-            "project_folder_name": "jabs-paper",
-            "stitch_value": 5,
-            "filter_value": 5,
+        'Side seizure': [
+            classifier_path: '/projects/kumar-lab/data/jabs/classifiers/v0.37.0/Side_seizure/50e865cfb045ff5b0c6bbad1dabf6465de43ef52ab4e13900b915986a51e9a0c.pickle',
+            stitch_value: 5,
+            filter_value: 5
         ],
-        // "Leg_splaying": [  // Currently can't be exported successfully
-        //     "project_folder_name": "ptz",
-        //     "stitch_value": 5,
-        //     "filter_value": 5,
-        // ],
-        "Side_seizure": [
-            "project_folder_name": "ptz",
-            "stitch_value": 5,
-            "filter_value": 5,
+        'scratch': [
+            classifier_path: '/projects/kumar-lab/data/jabs/classifiers/v0.37.0/scratch/261f1c21f015d47d30154fbbec98cb8860a40fbe383d3d86d6778e3da0f88a18.pickle',
+            stitch_value: 5,
+            filter_value: 5
         ],
-        "Tail_jerk": [
-            "project_folder_name": "ptz",
-            "stitch_value": 5,
-            "filter_value": 5,
+        'Tail jerk': [
+            classifier_path: '/projects/kumar-lab/data/jabs/classifiers/v0.37.0/Tail_jerk/34313467d88e7fc64ee90e10549eb48cfb9782d5e7a246fe106188c8ae50cdff.pickle',
+            stitch_value: 5,
+            filter_value: 5
         ],
-        "Wild_jumping": [
-            "project_folder_name": "ptz",
-            "stitch_value": 5,
-            "filter_value": 5,
+        'Wild jumping': [
+            classifier_path: '/projects/kumar-lab/data/jabs/classifiers/v0.37.0/Wild_jumping/7f114ce1b23a978afe82b4d9b7ec94c20982a2d5e7d16aefe4c5e0383324096a.pickle',
+            stitch_value: 5,
+            filter_value: 5
         ],
-        "Escape": [
-            "project_folder_name": "jabs-vivek",
-            "stitch_value": 5,
-            "filter_value": 5,
+        'Escape': [
+            classifier_path: '/projects/kumar-lab/data/jabs/classifiers/v0.37.0/Escape/0fa6b28e58fcc873f0c4ef053f5118566589d62d8e445b34ceaf1f962b5f8915.pickle',
+            stitch_value: 5,
+            filter_value: 5
         ],
-        "Rearing_supported": [
-            "project_folder_name": "jabs-vivek",
-            "stitch_value": 5,
-            "filter_value": 5,
+        'Rearing (unsupported)': [
+            classifier_path: '/projects/kumar-lab/data/jabs/classifiers/v0.37.0/Rearing_unsupported/562453b77d1a681a75c49bd774b5451e79e9e5f8784b48978f0f8cabbd90721e.pickle',
+            stitch_value: 5,
+            filter_value: 5
         ],
-        "Rearing_unsupported": [
-            "project_folder_name": "jabs-vivek",
-            "stitch_value": 5,
-            "filter_value": 5,
+        'Rearing (supported)': [
+            classifier_path: '/projects/kumar-lab/data/jabs/classifiers/v0.37.0/Rearing_supported/09ce1ed47e12556866045306bd221328454a8ccb5bc7ea7778a97e4882b386b6.pickle',
+            stitch_value: 5,
+            filter_value: 5
         ],
-        "Turn_left": [
-            "project_folder_name": "jabs-vivek",
-            "stitch_value": 5,
-            "filter_value": 5,
+        'Turn left': [
+            classifier_path: '/projects/kumar-lab/data/jabs/classifiers/v0.37.0/Turn_left/4779947116201f5fab5f2ec04f0ad4ee97a1e83649b6089d64354a7b07c42e4b.pickle',
+            stitch_value: 5,
+            filter_value: 5
         ],
-        "Turn_right": [
-            "project_folder_name": "jabs-vivek",
-            "stitch_value": 5,
-            "filter_value": 5,
+        'Turn right': [
+            classifier_path: '/projects/kumar-lab/data/jabs/classifiers/v0.37.0/Turn_right/b6fe24481513b87b757a61ad9495f7a9af270fad3cbec5d6b25556ece5f9c50a.pickle',
+            stitch_value: 5,
+            filter_value: 5
         ],
-        // "Jerk": [  // Needs to be re-exported to remove static object features (unavailable for single mouse)
-        //     "project_folder_name": "social-play",
-        //     "stitch_value": 1,
-        //     "filter_value": 6,
-        // ]
     ]
+
     heuristic_classifiers = ["corner", "corner_facing", "freeze", "locomotion", "periphery", "wall_facing"]
 
     // Number of 5-minute bins for transforming summary tables into bins
diff --git a/nextflow/modules/jabs_classifiers.nf b/nextflow/modules/jabs_classifiers.nf
index d890274..6f9efc8 100644
--- a/nextflow/modules/jabs_classifiers.nf
+++ b/nextflow/modules/jabs_classifiers.nf
@@ -67,10 +67,9 @@ process PREDICT_CLASSIFIERS {
 
     script:
     """
-    for classifier in ${classifiers.keySet().collect { params.exported_classifier_folder + it + params.classifier_artifact_suffix }.join(' ')};
+    for classifier_path in ${classifiers.collect { _behavior, details -> details.classifier_path }.join(' ')};
     do
-        ln -s \${classifier} .
-        jabs-classify classify --classifier \$(basename \${classifier}) --input-pose ${in_pose} --out-dir . --feature-dir .
+        jabs-classify classify --classifier "\${classifier_path}" --input-pose ${in_pose} --out-dir . --feature-dir .
     done
     """
 }
@@ -105,12 +104,12 @@ process GENERATE_BEHAVIOR_TABLES {
 
     script:
     """
-    behavior_command="--behavior ${classifiers.collect { entry -> "$entry.key --stitch_gap $entry.value.stitch_value --min_bout_length $entry.value.filter_value" }.join(' --behavior ')}"
+    behavior_command="--behavior ${classifiers.collect { entry -> "$entry.key --stitch-gap $entry.value.stitch_value --min-bout-length $entry.value.filter_value" }.join(' --behavior ')}"
     jabs-postprocess generate-tables \
-        --project_folder . \
-        --feature_folder . \
-        --out_prefix ${in_pose.baseName} \
-        --out_bin_size 5 \
+        --project-folder . \
+        --feature-folder . \
+        --out-prefix ${in_pose.baseName} \
+        --out-bin-size 5 \
         \${behavior_command}
     """
 }
@@ -149,7 +148,7 @@ process PREDICT_HEURISTICS {
             --feature-folder . \
             --behavior-config \${classifier} \
             --out-prefix ${in_pose.baseName} \
-            --out_bin_size 5
+            --out-bin-size 5
     done
     """
 }