diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
new file mode 100644
index 0000000..f25caf6
--- /dev/null
+++ b/.github/workflows/main.yml
@@ -0,0 +1,103 @@
+name: Task1 QC
+on:
+  push:
+    branches:
+      - main
+
+jobs:
+  process_raw:
+    runs-on: self-hosted
+    outputs:
+        sub: ${{ steps.set_vars.outputs.sub }}
+        task: ${{ steps.set_vars.outputs.task }}
+        version: ${{ steps.set_vars.outputs.version }}
+
+    steps:
+      - name: checkout code and return recently uploaded file in /data
+        uses: actions/checkout@v3
+      - name: Get changed files
+        run: |
+            #!/bin/bash
+
+            # Get the list of CSV files changed in the last 24 hours
+            data=$(git log --since="24 hours ago" --name-only --pretty=format: -- '*.csv' | sort | uniq)
+
+            # Export the data variable to the environment
+            echo "data=$data" >> $GITHUB_ENV
+
+            # Print the changed CSV files
+            echo "Changed CSV files in the last 24 hours: $data"
+
+      - name: set up python
+        run: |
+          python -m pip install --upgrade pip
+          pip install -r requirements.txt
+
+      - name: parse raw
+        id: set_vars
+        run: |
+            # Loop through each CSV file in $data
+            for file in $data; do
+                filename=$(basename "$file")
+                IFS='_' read -r sub task version <<< "$filename"
+                version="${version%.csv}"  # Remove the .csv extension from version
+                echo "::set-output name=sub::$sub"
+                echo "::set-output name=task::$task"
+                echo "::set-output name=version::$version"
+                echo "Subject: $sub"
+                echo "Task: $task"
+                echo "Version: $version"
+            done
+
+  run_qc:
+    runs-on: self-hosted
+    needs: process_raw
+    steps:
+      - name: Checkout code
+        uses: actions/checkout@v3
+
+      - name: Debug env vars
+        run: |
+            echo "sub=${{ needs.process_raw.outputs.sub }}"
+            echo "task=${{ needs.process_raw.outputs.task }}"
+            echo "version=${{ needs.process_raw.outputs.version }}"
+
+      - name: run quality control
+        run: |
+            sub=${{ needs.process_raw.outputs.sub }}
+            task=${{ needs.process_raw.outputs.task }}
+            vers=${{ needs.process_raw.outputs.version }}
+            for sub in ${sub}; do
+            echo "Processing subject: $sub"
+            for task in ${task}; do
+                echo "Processing task: $task"
+                for vers in ${vers}; do
+                echo "Processing version: $vers"
+                csv_file="./data/${sub}/processed/${sub}_${task}_${vers}.csv"
+                log_file="./data/${sub}/qc_${task}_${vers}.log"
+                echo "CSV file: $csv_file"
+                echo "Log file: $log_file"
+                if [ -f "$csv_file" ]; then
+                    python ./code/SMqC.py -s "$csv_file" -o "./data/${sub}/" -sub "$sub" | tee "$log_file"
+                    echo "QC for ${sub}_${task}_${vers} running"
+                else
+                    echo "CSV file $csv_file does not exist"
+                fi
+                done
+            done
+            done
+
+  push:
+    runs-on: self-hosted
+    needs: run_qc
+    steps:
+      - name: Commit and Push Changes
+        run: |
+          git config --global user.name "miloswrath"
+          git config --global user.email "miloswrath@users.noreply.github.com"
+          git remote set-url origin https://x-access-token:${{ secrets.GITHUB_TOKEN }}@github.com/$GITHUB_REPOSITORY
+          git add .
+          git commit -m "Automated commit by GitHub Actions"
+          git push
+        env:
+            GITHUB_TOKEN: ${{ secrets.GIT_PAT }}
diff --git a/code/ConvertBeh.py b/code/ConvertBeh.py
new file mode 100644
index 0000000..ff83c6a
--- /dev/null
+++ b/code/ConvertBeh.py
@@ -0,0 +1,71 @@
+# %%
+import pandas as pd
+import numpy as np
+import os
+import json
+
+def parse_args():
+    import argparse
+    parser = argparse.ArgumentParser(description='Convert Behavior Data')
+    parser.add_argument('-submission', type=str, help='Path to the submission file')
+    parser.add_argument('-out', type=str, help='Output directory')
+    parser.add_argument('-sub', type=str, help='Subject ID')
+    parser.add_argument('-task', type=str, help='Task name')
+    parser.add_argument('-taskvers', type=str, help='Task version')
+    return parser.parse_args()
+
+
+def rename_files(submission, subject, task, taskvers):
+    #rename raw text files to be subject_task_taskvers.txt
+    for root, dirs, files in os.walk(submission):
+        for file in files:
+            if file.endswith(".txt"):
+                os.rename(os.path.join(root, file), os.path.join(root, f"{subject}_{task}_{taskvers}.txt"))
+                submission = os.path.join(root, 'raw', f"{subject}_{task}_{taskvers}.txt")
+                print(submission)
+    return submission
+
+
+
+
+def convert_beh(submission, out):
+
+    if not os.path.isfile(submission):
+        print(f"file does not exist: {submission}")
+
+    # Use list_txt to store one file since I don't want to screw with Marco's code
+    
+
+    count = 0
+    dic = {}
+
+    count += 1
+    tweets = []
+    with open(submission, 'r') as file:
+        for line in file:
+            tweets.append(json.loads(line))
+    dic[count]= pd.json_normalize(tweets,'data')
+
+    print(dic)
+
+
+    paths = []
+    for i in range(len(dic)):
+        i += 1
+        for sub in np.unique(dic[i]['subject_id']):
+            print(sub)
+            paths.append((out+"/{0}_{1}_{2}"+".csv").format(sub,dic[i]['task'][0],dic[i]['task_vers'][0]))
+
+        for path in paths:
+            dic[i].to_csv(path, index=False)
+            print(f"saved {path}")
+
+def main():
+    args = parse_args()
+    submissive = rename_files(args.submission, args.sub, args.task, args.taskvers)
+    convert_beh(submissive, args.out)
+
+if __name__ == "__main__":
+    main()
+
+
diff --git a/jatosAPI.py b/jatosAPI.py
new file mode 100644
index 0000000..e5b2c7f
--- /dev/null
+++ b/jatosAPI.py
@@ -0,0 +1,225 @@
+import requests
+from datetime import datetime, timedelta
+import zipfile
+import os
+import numpy as np
+import pandas as pd
+import json
+import shutil
+import subprocess
+
+# jap_5ThOJ14yf7z1EPEUpAoZYMWoETZcmJk305719
+
+def get_met():
+
+
+    url = 'https://jatos.psychology.uiowa.edu/jatos/api/v1/results/metadata'
+    headers = {
+        'accept': 'application/json',
+        'Authorization': 'Bearer jap_5ThOJ14yf7z1EPEUpAoZYMWoETZcmJk305719',
+        'Content-Type': 'application/json',
+    }
+    data = {
+        'studyIds': [955, 971, 994, 917, 927, 943]
+    }
+
+    response = requests.post(url, headers=headers, json=data)
+
+    # If you want to print the response
+    print(response.status_code)
+    print(response.json())
+    response_json = response.json()
+
+    response = response_json
+
+    # Get the current timestamp
+    current_time = datetime.now().timestamp() * 1000  # Convert to milliseconds
+    one_day_ago = current_time - (24 * 60 * 60 * 1000)  # 24 hours ago in milliseconds
+
+    # Initialize an empty list to store study result IDs
+    study_result_ids = []
+
+    # Iterate through the data to check conditions and collect study result IDs
+    for study in response['data']:
+        for study_result in study['studyResults']:
+            if study_result['studyState'] == 'FINISHED' and study_result['endDate'] >= one_day_ago:
+                study_result_ids.append(study_result['id'])
+                break  # No need to check other component results for this study result
+
+    # Print the list of study result IDs
+    print(study_result_ids)
+
+    if len(study_result_ids) == 0:
+        print("No study results found.")
+        exit()
+    
+    return study_result_ids
+
+def get_data(study_result_ids):
+    headers = {
+        'accept': 'application/octet-stream',
+        'Authorization': 'Bearer jap_5ThOJ14yf7z1EPEUpAoZYMWoETZcmJk305719',
+        'Content-Type': 'application/json',
+    }
+    # Get the data for each study result
+    datas = {
+        'studyIds': [955, 971, 994, 917, 927, 943],
+        'studyResultIds': study_result_ids
+    }
+
+    url = 'https://jatos.psychology.uiowa.edu/jatos/api/v1/results/data'
+    response = requests.post(url, headers=headers, json=datas)
+    # Debugging information
+    print(f"Status Code: {response.status_code}")
+
+
+    # Save the unzip file and save .txt file to the current directory
+    if response.status_code == 200:
+        jrzip_file = 'response.jrzip'
+        with open(jrzip_file, 'wb') as f:
+            f.write(response.content)
+        print(f"Downloaded file: {jrzip_file}")
+
+        # Verify if the file is a valid zip file
+        if zipfile.is_zipfile(jrzip_file):
+            print("The file is a valid zip file.")
+
+            # Create a new zip file with only the desired files
+            filtered_jrzip_file = 'filtered_response.jrzip'
+            with zipfile.ZipFile(jrzip_file, 'r') as zip_ref:
+                with zipfile.ZipFile(filtered_jrzip_file, 'w') as filtered_zip_ref:
+                    for zip_info in zip_ref.infolist():
+                        # Check if the filename contains any of the study_result_ids
+                        if any(str(study_result_id) in zip_info.filename for study_result_id in study_result_ids):
+                            filtered_zip_ref.writestr(zip_info, zip_ref.read(zip_info.filename))
+            print(f"Filtered zip file created: {filtered_jrzip_file}")
+
+            # Extract the filtered zip file
+            with zipfile.ZipFile(filtered_jrzip_file, 'r') as zip_ref:
+                zip_ref.extractall('./data/raw')
+            print(f"Unzipped file: {filtered_jrzip_file}")
+
+            # Optionally, remove the original and filtered zip files after extraction
+            os.remove(jrzip_file)
+            os.remove(filtered_jrzip_file)
+
+            # Walk through the directory and find all .txt files, save paths to a list
+            txt_files = []
+            for root, dirs, files in os.walk("./data/raw"):
+                for file in files:
+                    if file.endswith(".txt"):
+                        txt_files.append(os.path.join(root, file))
+            print(f"Found {len(txt_files)} .txt files.")
+            #move the text file to the data folder
+
+        else:
+            print("The file is not a valid zip file.")
+    else:
+        print("Failed to retrieve or save the file.")
+        print(f"Response Text: {response.text}")
+
+    return txt_files
+
+def convert_beh():
+
+
+    txt = []
+    for root, dirs, files in os.walk('./data/raw'):
+        for file in files:
+            if file.endswith(".txt"):
+                txt.append(os.path.join(root, file))
+    print(txt)
+            
+    count = 0
+    dic = {}
+    for b in txt:
+        count += 1
+        tweets = []
+        with open(b, 'r') as file:
+            for line in file:
+                tweets.append(json.loads(line))
+        dic[count]= pd.json_normalize(tweets,'data')
+
+    print(dic)
+
+
+
+    paths = []
+    for i in range(len(dic)):
+        i += 1
+        for sub in np.unique(dic[i]['subject_id']):
+            print(sub)
+            paths.append((f'./data/{sub}/processed'+"/{0}_{1}_{2}"+".csv").format(sub,dic[i]['task'][0],dic[i]['task_vers'][0]))
+        
+
+
+        for path in paths:
+            os.makedirs(os.path.dirname(path), exist_ok=True)
+            dic[i].to_csv(path, index=False)
+            print(f"saved {path}")
+
+
+
+    return paths
+
+
+def move_txt(txt_files):
+    dic = {}
+    for file_path in txt_files:
+        tweets = []
+        with open(file_path, 'r') as file:
+            # Read text file and append each line as a JSON object to tweets
+            for line in file:
+                tweets.append(json.loads(line))
+        dic[file_path] = pd.json_normalize(tweets, 'data')
+
+    for file_path, df in dic.items():
+        for sub in np.unique(df['subject_id']):
+            print(sub)
+            target_dir = f'./data/{sub}/raw'
+            os.makedirs(target_dir, exist_ok=True)
+            # Save the DataFrame to a CSV file in the target directory
+            output_file = os.path.join(target_dir, os.path.basename(file_path))
+            # save df as a txt file to target dir
+            with open(output_file, 'w') as f:
+                f.write(df.to_string(index=False))
+            print(f"Saved {output_file} to {target_dir}")
+        os.remove(file_path)
+        print(f"Removed {file_path}")
+        # remove any dirs in data/raw
+        for root, dirs, files in os.walk('./data/raw'):
+            for d in dirs:
+                shutil.rmtree(os.path.join(root, d))
+
+    return None
+
+
+
+
+def push():
+
+    subprocess.run(["git", "add", "-A"])
+    subprocess.run(["git", "commit", "-m", "auto commit -> added subject task data"])
+    subprocess.run(["git", "push"])
+
+
+def main():
+    study_result_ids = get_met()
+    get_data(study_result_ids)
+    convert_beh()
+    txt_files = []
+    for root, dirs, files in os.walk('./data/raw'):
+        for file in files:
+            if file.endswith(".txt"):
+                txt_files.append(os.path.join(root, file))
+    move_txt(txt_files)
+    push()
+
+
+
+
+
+
+if __name__ == "__main__":
+    main()
+    
\ No newline at end of file